With artificial intelligence capabilities continuing to expand, universities now face complex, urgent questions about how to navigate increasing challenges, as well as new opportunities. Much attention is now directed toward this, including an MIT report on AI and education published this summer.
A new essay from MIT Statistics and Data Science Center Director Sasha Rakhlin, the Distinguished Professor in Data, Systems, and Society, IDSS and Brain and Cognitive Sciences, synthesizes recent discussions and readings about how AI is changing academia — particularly mathematics, statistics, machine learning, and engineering. Here, focusing primarily on graduate research and education, he describes key questions that departments and universities need to think about, and how they can best work with AI going forward.
Q: What is changing in research, and why is it happening so quickly?
A: Mathematics illustrates how quickly AI capabilities are advancing. Last year, a model reached gold-medal level at the International Mathematical Olympiad. Only a year later, models are producing new research results, including a recently proposed solution to one of the Millennium Prize Problems. Tasks that once demonstrated advanced mathematical ability can increasingly be performed by AI.
A central factor determining the pace of AI progress in a particular discipline is the speed and reliability of verification. Formalized proofs can be checked automatically; programs can be run and tested. Where evaluation is fast and reliable, systems can generate candidates, learn from outcomes, and improve. This also applies to AI research itself: improving models, training procedures, and their supporting tools. Better models can contribute to the next round of development, creating a compounding process that accelerates progress.
For experienced researchers, this creates opportunities to pursue questions whose technical demands previously put them out of reach. It also sharpens the distinction between obtaining a solution and understanding why it works, what generalizes, and what to ask next. However, we should not assume that abstraction, judgment, or problem formulation will remain beyond AI. Universities should prepare for a near future in which AI is smarter than all of us in many, perhaps most, aspects of our intellectual work.
Q: How should departments rethink academic credit and graduate training?
A: In a growing number of fields, a polished paper is becoming a weaker signal of individual expertise. Departments must begin now to reconsider what they reward, without simply defining valuable work as whatever AI cannot yet do. Asking good questions, replication, synthesis of ideas, informative negative results, and shared datasets may deserve greater recognition. More broadly, evaluation should establish what a researcher contributed and takes intellectual responsibility for, including when substantial parts of the work were performed by AI. These expectations should guide hiring, promotion, and funding — and be made explicit for current and incoming PhD students.
Training poses a harder problem. Intellectual muscles develop through exercise. Routine calculations, coding, failed approaches, and small discoveries have long helped students acquire intuition and judgment. Delegating this work can remove formative experiences, even as AI allows students to attempt more ambitious projects. We need to distinguish unnecessary friction from work through which expertise develops. Students should learn to formulate problems, audit model outputs, reproduce results, and defend their choices. Fundamentals may become more valuable as the basis for using these tools well.
Q: What should MIT and other universities build now?
A: This is where I see a real opportunity for universities. Let's first be clear about the goal: Universities should be able to pursue questions over long horizons, share results openly, and evaluate claims independently. Partnerships with industry will be essential, but we should not assume that commercial priorities will cover the breadth of science or remain aligned with it over time. Some degree of technological independence will therefore be necessary.
Academia's most important strategic asset may be the accumulated knowledge and experience within its laboratories. Published papers present a selective record: Failed experiments, abandoned directions, and the reasons an approach did not work often remain unpublished. Scientists and engineers hold tacit knowledge, acquired through experience, about which interventions are likely to fail and why. This missing context may help explain why models currently struggle in some domains, especially the empirical sciences, to anticipate consequences that are clear to an expert. These limitations may be temporary, and capturing negative results and scientists' interpretations could make models much better at scientific exploration.
We can imagine a future in which MIT's laboratories function more like a single, living, scientific organism, connected through shared AI research infrastructure. For example, suppose a neuroscience laboratory needs better methods for segmenting neurons in microscopy images. An AI agent could recognize a relevant advance from a computer vision group, connect the researchers, propose benchmarks, and help them iterate. It could surface unresolved questions to students and faculty, and make lessons from one laboratory available to others. To make this possible, we should build workflows that capture hypotheses, interventions, outcomes, failures, and interpretations. Shared systems should connect these workflows, allowing agents to use tools and information across laboratories with appropriate permissions. This will require substantial public and institutional investment now in compute, secure data systems, and expertise in adapting and post-training AI models.
Tracing this process could also preserve the lineage of ideas and make contributions, including those of graduate students, easier to recognize. With agreed rules for consent and credit, researchers could collaborate more openly, with greater confidence that their work would be acknowledged. This could also help answer the earlier question of how to recognize and reward intellectual contributions.
AI systems can already synthesize and reason about information from more sources than any individual researcher could absorb, and their ability to make useful connections will continue to improve. We should use this capacity to bring us together around hard scientific and engineering problems, helping us build on one another's strengths and insights. To me, this is a promising path forward, but universities need to invest now to make it possible.
Margaret Hamilton, computing pioneer who led software development for the Apollo program, dies at 90Over two decades at MIT, Hamilton forged new paths in computer science and engineering. Her groundbreaking work included leading the team that programmed the first computers that took humans to the moon.Margaret Hamilton, a profoundly influential computer scientist best known for leading the software engineering team at MIT’s Instrumentation Lab during NASA’s Apollo program, died on Sep. 30. She was 90.
A computing pioneer who authored over 130 publications, Hamilton helped to establish software engineering as a dedicated discipline. She worked at MIT from 1959 until the mid-1970s, after which she became a successful computing entrepreneur and CEO.
Her life’s work was recognized with many awards and honors, including the 2016 Presidential Medal of Freedom from President Barack Obama, whose citation noted: “Hamilton defined new forms of software engineering and helped launch an industry that would forever change human history. Her software architecture led to giant leaps for humankind, writing the code that helped America set foot on the moon.”
“To say Margaret Hamilton was a pioneer — to say she was ahead of her time — would be a dramatic understatement. She was a software engineer at a time when that field was in its infancy, and she not only developed advanced code herself but also led a team in using that nascent technology to develop one of the most complex systems humanity had ever achieved,” says Olivier de Weck, the Apollo Program Professor and interim head of the MIT Department Aeronautics and Astronautics.
“The Apollo program still stands as one of our greatest testaments to the power of collaboration, ingenuity, and engineering, and Margaret Hamilton was a fundamental contributor to that program’s success. And for her that was just the beginning! She went on to become an entrepreneur and remained on the cutting edge of systems software throughout an extraordinary career, while acting as an advocate, mentor, and inspiration to millions.”
Early life and projects at MIT
Born in Paoli, Indiana, in 1936, Hamilton began studying mathematics at the University of Michigan in 1955 before transferring to Earlham College, where she earned a BA in mathematics with a minor in philosophy in 1958. She moved to Boston, Massachusetts, in 1959 with her husband while he pursued a law degree.
Hamilton soon found a temporary position in the meteorology department at MIT, working with professor of meteorology Edward N. Lorenz SM ’43, ScD ’48 on weather prediction software. This was her first entry point into computer programming, and her work would go on to inform Lorenz’s future publications on chaos theory.
From there, Hamilton took a role as a programmer at MIT Lincoln Laboratory in 1961, working on the Semi-Automatic Ground Environment (SAGE) project, the United States’ first air defense system. Hamilton wrote software for the prototype AN/FSQ-7 computer (XD-1), used by the U.S. Air Force to search for potentially unfriendly aircraft. During this time, Hamilton began to take an interest in software reliability — a new and largely unexplored concept at the time.
In 1965 Hamilton was preparing to pursue graduate studies when her husband saw an advertisement in the newspaper: The Instrumentation Lab at MIT was seeking people to develop software to “send man to the moon.” The lab had won the contract from NASA to build the onboard flight software for the Apollo program. Intrigued by the challenge, Hamilton applied, and was hired as the first programmer for the Apollo project at MIT, as well as the first female programmer in the project.
Hamilton worked first on the software for the uncrewed Apollo missions and then was promoted into leading the team developing the on-board flight software for the crewed missions. By 1968 she was assistant director in charge of the Command and Service Module team, and more than 400 people were working on Apollo’s software.
“From my own perspective, the software experience itself (designing it, developing it, evolving it, watching it perform and learning from it for future systems) was at least as exciting as the events surrounding the mission,” Hamilton told MIT News in 2009. “There was no second chance. We knew that. We took our work seriously, many of us beginning this journey while still in our 20s. Coming up with solutions and new ideas was an adventure. Dedication and commitment were a given. Mutual respect was across the board. Because software was a mystery, a black box, upper management gave us total freedom and trust. We had to find a way, and we did. Looking back, we were the luckiest people in the world; there was no choice but to be pioneers.”
“Defensive” programming and priority-driven software take humans to the moon
Hamilton discovered a talent for leadership, as well as a keen instinct for problem-solving and critical thinking that would prove to save Project Apollo several times over.
There was the incident that became known as “the Lauren error”: One day, her daughter Lauren, then four years old, was playing with the command module simulator at the Instrumentation Lab when she somehow activated a pre-launch program, called P01, while the simulator was in midflight — which crashed the simulator altogether. Hamilton created a program add-on in the technical documentation warning users not to launch P01 during flight.
She also proposed a software fix to prevent the error happening during a real mission, but she was overruled on the grounds that the highly trained astronauts were never going to make that same mistake. Yet during the Apollo 8 mission in 1968, that’s exactly what happened: Jim Lovell inadvertently launched P01 during the flight, causing the on-flight navigational data to vanish. Hamilton and her team were called in to solve the error, and after that her proposed changes were integrated into the program. This was an early example of “defensive” programming, the practice of building software that could anticipate or fix errors on its own.
Hamilton’s most famous contribution to the Apollo program came during the pivotal Apollo 11 mission to land on the moon in July of 1969. Moments before the Eagle module was set to land on the lunar surface, the onboard computer raised the alarm. It had detected a 1202 error: The computer was overloaded due to a fault in a hardware switch, and it was possible the system would not be able to handle the complex landing procedure.
But Hamilton and her team had engineered priority-driven software, able to shut down unnecessary background tasks in order to prioritize mission-critical tasks. Houston trusted Hamilton’s software and allowed the mission to proceed, and two men walked on the moon for the first time.
Later career and legacy
Hamilton continued to work at the Instrumentation Lab into the 1970s. As the Apollo program wound down, the Instrumentation Lab spun out of MIT to become the independent Draper Laboratory.
“Margaret left an indelible mark on Draper, and we will be forever grateful,” says Jerry M. Wohletz SM ’97, PhD ’00, president and CEO at Draper. “Through her leadership and contributions to the development of the onboard flight software for NASA’s Apollo Guidance Computer, she helped ensure that Apollo astronauts landed safely on the lunar surface and safely returned home. We will honor her legacy at Draper forever.”
Hamilton went on to create her first software company, Higher Order Software, in 1976. The firm was based on Hamilton’s software engineering approach of error prevention and fault tolerance.
A decade later, Hamilton founded another software company, Hamilton Technologies, “to provide products and services to modernize the planning, system engineering and software development process in order to maximize reliability, lower cost and accelerate time to market.”
Hamilton Technologies’ flagship product is the Universal Systems Language (USL), a systems modeling language and methodology for engineering complex software systems that prioritize error prevention and defensive programming.
Throughout her career, Hamilton worked to achieve recognition for software engineering as a dedicated discipline.
“I fought to bring the software legitimacy so that it — and those building it — would be given its due respect, and thus I began to use the term ‘software engineering’ to distinguish it from hardware and other kinds of engineering, yet treat each type of engineering as part of the overall systems engineering process,” Hamilton told El Pais in 2018. “When I first started using this phrase, it was considered to be quite amusing. It was an ongoing joke for a long time. They liked to kid me about my radical ideas. Software eventually and necessarily gained the same respect as any other discipline.”
Among her many awards and honors, Hamilton was recognized with the NASA Exceptional Space Act Award for scientific and technical contributions in 2003; the Computer History Museum Fellow Award in 2017; the Intrepid Lifetime Achievement Award in 2019; and induction into the National Aviation Hall of Fame in 2022.
Later in life, Hamilton become an icon for women in science and technology, especially after a now-famous photo, showing her next to a printout of her MIT team’s Apollo code, began circulating online. In 2015, the Apollo software she helped to develop was added in its entirety to the code-sharing site GitHub. And in 2017, she became an official Lego Minifigure after a set originally designed by MIT science communicator Maia Weinstock, honoring her and several other women of NASA history, became available worldwide.
“Margaret Hamilton has been an inspiration to generations of computer scientists and engineers. Hers was a career dedicated to preventing errors and what she called ‘handling the unknown,’” says de Weck. “She personified leadership by example, and established a practice of software engineering based on problem-solving and systems engineering that we all benefit from.”
Hamilton is survived by her daughter, Lauren Hamilton; her son-in-law, Richard Selesnick; two grandsons; four great grandchildren; and her siblings John, David, and Kathryn. A memorial service will take place in the spring in Cambridge, Massachusetts.
Astronomers catch a star slowly snacking on a brown dwarf, 300 light years awayThe discovery reveals a new way that stars and planetary companions can interact.Like Earth, most planetary bodies circle their star in stable, detached orbits. These companionable systems can suddenly change when a planet comes too close to its star. In such a close encounter, a star can pull the planet in and swallow it whole.
Across the galaxy, astronomers have seen plenty of stable, detached planetary systems. They have also observed a handful of stars quickly engulfing their planet. Now, for the first time, scientists have spotted a system that is striking a curious balance between the two extremes. And it’s revealing a new way that stars can interact with planetary companions.
In a paper appearing today in Nature Astronomy, scientists at MIT and elsewhere have discovered a star leisurely snacking on a closely orbiting brown dwarf — a planet-like object that is more massive than a planet yet not quite as big as a star.
The new system, named ZTF J0440+2325, is within the Milky Way galaxy, roughly 300 light years from Earth, and represents the first observation of a low-mass object that is slowly and steadily consuming material from another low-mass object.
The rate at which the star is feeding from the brown dwarf suggests that this slow stellar cannibalism could carry on for hundreds of thousands, or even billions of years.
“When we think of stars interacting with planets or brown dwarfs, the picture is always that the star eventually swallows the other thing,” says Kevin Burdge, assistant professor of physics at MIT. “This is what will happen to the Earth when the sun becomes a red giant. But here, we’ve found an alternative: Instead of swallowing the thing up, the star can gradually eat it, for billions of years.”
The study’s MIT co-authors include Aaron Householder, Kaitlyn Shin, Saul Rappaport, Joheen Chakraborty, and Emma Chickles, along with collaborators from Caltech, the University of Hawaii, the Instituto de Astrofísica de Canarias and the Universidad de La Laguna in Spain, and the Harvard and Smithsonian Center for Astrophysics.
A “weird triangle”
The new system was spotted initially by the Zwicky Transient Facility. The ZTF uses a camera as part of a telescope at the Palomar Observatory, in California, to scan the sky for rapid changes in brightness, which could signal the presence of a supernova, a gamma-ray burst, or colliding neutron stars.
Several years ago, Burdge was looking through ZTF data when he noticed a strange light curve, or pattern in brightness. Light curves for supernova resemble a bell curve, signaling the gradual brightening and then fading of a star as it bursts. But what Burdge picked out looked more like a triangle, that didn’t appear once, but again and again.
“I remember first looking at this and thinking: Stars don’t make triangular waveforms like this,” he recalls.
At the time, he and his colleagues were focused on a different signal, which they identified as a “black widow binary” — a system in which an extremely dense, spinning neutron star is slowly consuming a much smaller companion star, similar to how its arachnid namesake plays with its prey.
Burdge wondered whether the triangle signal might also be from a black widow. But the light from the signal was puzzling. In black widow binaries, the light appears to wobble, as a result of a very light, low-mass object, such as a small companion star, whipping around a much heavier object, such as a neutron star.
“We weren’t seeing that whipping back and forth here,” Burdge says. “It didn’t make any sense. We couldn’t explain what this was.”
But they had a hunch: Could the signal be coming not from a wobbly, David-and-Goliath system, but from a more balanced pair of objects, each with a similarly low mass?
“If you have less mass in the system overall, things can gently orbit each other without whipping back and forth,” Burdge says. “That was the idea. But we never had any proof. And this weird triangle just sat for years.”
A slow and steady fireball
Recently, Burdge and Householder, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences, decided to revisit the triangle mystery. From the original ZTF signal, they determined the location of its source to be within the Milky Way galaxy, around 300 light years from Earth. They focused multiple telescopes on the source, named ZTF J0440+2325. From these observations, they measured various properties of the source, including its wobble. Compared to black widows and other similar binaries, the wobbling from ZTF J0440+2325 was much smaller — but not insignificant.
“That was the real clincher for this system,” Householder says. “When we measured that wobble, we found we were not seeing a black widow. This was a low-mass star that’s orbited by a brown dwarf. The wobble was too small in amplitude to be anything else.”
They determined that the star and the brown dwarf are extremely close, with the brown dwarf circling the star every 87 minutes, in an orbit that could fit within the diameter of the sun. Both objects are small by stellar standards. The star is around 85 times as massive as Jupiter, while the brown dwarf is around 25 times as massive.
With two low-mass objects circling at such close range, the scientists wondered if one object could be pulling material from the other. Such a process, known as accretion, is most often seen around objects that are extremely massive, though small in actual size, such as black holes and neutron stars. When a black hole accretes, or draws material from a much smaller nearby object, it pulls the matter around it in a disk.
“The difference here is: The thing absorbing matter is not a tiny black hole but a star, which is relatively big in size,” Burdge explains. “So matter just pummels directly onto the surface, at very high speeds, like an asteroid hitting the moon.”
The team carried out simulations of possible accretion in ZTF J0440+2325. Taking into account the properties of the star and the brown dwarf, they simulated particles of matter on the brown dwarf, and how these particles should behave within the system over time, according to the laws of physics and equations of motion.
“When we track those test particles, we see they indeed fall right onto the surface of the star,” Householder says. “This is the first time we’ve caught a low-mass star actively accreting from another low-mass object.”
What’s more, the team calculated that the brown dwarf must be feeding material to its star at a rate of about 1/100,000 of an Earth’s mass each year. That’s about 40 million dump trucks’ worth of material, or roughly 1.3 trillion one-pound burritos every second. While that may seem like a lot of matter to be losing, it is in fact a very small fraction of the brown dwarf. This rate, the researchers estimate, is actually quite slow and steady. Given the size of the system, they say the star could continue leisurely snacking on the brown dwarf, for billions of years.
This slow accretion, they say, would resemble a steady stream from the brown dwarf, onto the star. The researchers realized that if they were to view the system from afar, the brightness from the system would chart as a triangle, as the brown dwarf and its stream of matter circles its star.
“It’s like you’ve got this continuous fireball onto one of the objects, and as one orbits the other, that hotspot comes in and out of view, and the peak of the triangle signal is when you’re looking right at the fireball,” Burdge explains.
With the mystery of the triangle light curve solved, the team hopes to spot similar slow-feeding systems nearby.
“It’s inspiring a lot of new searches on our part,” Householder says. “I think we’re going to learn a lot about a different kind of way that planets and brown dwarfs interact with their host stars.”
This research was supported, in part, by the National Science Foundation.
Lung cancers can use two different mechanisms to evade KRAS-inhibiting drugsSome tumor cells develop resistance by amplifying the KRAS gene, while others change their tumor type, MIT researchers have found.About 25 percent of lung adenocarcinomas have mutations of the gene KRAS, which drives uncontrolled cell growth. In recent years, the FDA has approved two KRAS inhibitors to treat patients with KRAS mutations. While these drugs can work well initially, tumors almost always develop resistance to them.
Usually, resistance emerges because cells reactivate KRAS activity, through mutations that prevent drug binding or by increasing KRAS expression that overpowers the effects of the inhibitor. However, in a new study, MIT researchers have modeled an alternative mechanism that cancer cells can use to become resistant to KRAS inhibition.
The researchers found that in some cases, lung tumors undergo transformation from adenocarcinoma to squamous cell carcinoma. Both of these tumor types are commonly found in the lungs, but they are thought to arise from different cells and have different genetic profiles.
When this transition occurs, tumor cells no longer require KRAS, and appear to turn on alternative signaling pathways that help them continue to grow. Ongoing work to identify those pathways may reveal targets for new drugs that could help prevent resistance to KRAS inhibitors.
“The main takeaway is that there seem to be different routes of resistance to KRAS inhibitors, and so we need to be thinking about how we can address this,” says Carrie Rodriguez, an MIT graduate student and one of the lead authors of the paper.
Nicolas Mathey-Andrews PhD ’25 is also a lead author of the study, which appears today in the journal Nature Genetics. The paper’s senior author is Tyler Jacks, the David H. Koch Professor of Biology and a member of MIT’s Koch Institute for Integrative Cancer Research.
Tissue transformation
The two FDA-approved KRAS inhibitors both target a mutation called KRAS-G12C. These drugs are approved only for use in patients whose tumors have failed to respond to other drugs, and these patients usually have cancer that has spread beyond the lungs.
KRAS inhibitors are effective in about 35 percent of the patients who receive them. However, in those cases, the tumors almost always end up becoming resistant by generating additional copies of the KRAS gene or finding other ways to turn on the MAP kinase signaling pathway, which is usually triggered by KRAS and stimulates cell growth.
“Resistance to targeted therapies is a very serious problem,” Rodriguez says. “Sometimes these KRAS inhibitors can hold cancers at bay, but most cases do end up relapsing.”
A 2021 study from researchers at Dana-Farber Cancer Institute, which analyzed tumors from 17 non-small cell lung cancer patients treated with KRAS-G12C inhibition, identified secondary resistance mutations in a majority of patients. In two of these patients, however, the researchers found that tumors transformed from adenocarcinomas to squamous cell carcinomas, but they did not harbor obvious resistance mutations.
Both adenocarcinomas and squamous cell carcinomas are classified as non-small cell lung cancers (NSCLCs), which are the most common type of primary lung cancer. Adenocarcinomas, the most common type of NSCLCs, often originate from the surfactant-producing cells that line the lungs, while squamous cell carcinomas originate in the cells that line the central airways of the lungs.
Mutations of KRAS are found much more frequently in adenocarcinomas than in squamous cell carcinomas
In this study, the researchers set out to model the factors that might drive the transition from adenocarcinomas to squamous cell carcinomas. To do that, they engineered a mouse lung cancer model to express the mutation that is targeted by the FDA-approved KRAS inhibitors.
Following treatment with a KRAS-G12C inhibitor, tumors with genetic loss of Nkx2-1, which normally helps maintain alveolar epithelial identity, were able to undergo adeno-to-squamous transition. Turning on a transcription factor called DeltaNp63, which is overactive in many squamous cell carcinomas, also made this transition more likely. Another transcription factor known as SOX2 also helped stimulate the transition, but this gene could not initiate the transition on its own.
Paths to resistance
Tumors that underwent these tissue transformations did not acquire the mutations that typically boost KRAS expression in adenocarcinomas. Instead, KRAS signaling was shut off. The researchers hypothesize that these cells may turn on another signaling pathway that helps them to continue growing.
“There seem to be several different routes where you can get to squamous transformation, either through loss of lung-lineage-defining transcription factors, or overexpression of these squamous master regulators, SOX2 or DeltaNp63. Those resistant squamous tumors no longer respond to KRAS inhibition because they shut off the signaling or at least dampen it significantly,” Rodriguez says.
The researchers are now further exploring what happens to tumor cells as they transition to a squamous state, in hopes of identifying vulnerabilities that could be targeted with new drugs.
“Fundamentally this is a transition that’s poorly understood, and we were happy to see that we were able to model it,” Mathey-Andrews says. “Future directions that have an eye toward translation will utilize those models to understand the process and conditions by which histologic transformation occurs, and then also nominate potential targets downstream.”
The research was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute, a Ruth Kirschstein National Service Research Award, the National Institute of General Medical Sciences, and the Ludwig Center at MIT.
Scattering neutrinos to probe the fundamental laws of the universeAs she became integral to an international particle physics experiment, PhD student Faith Reyes found confidence in herself as a scientist.Many people who are successful in STEM fields were lucky to have someone who turned them on to a topic and encouraged their interest. For Faith Reyes, that person was her high school physics teacher, Ms. Bolster.
Whereas Reyes had earlier studied math and science without seeing how those subjects could be applied outside the classroom, her teacher helped her connect those dots and experience the excitement of investigating the physical world. With Ms. Bolster’s help, Reyes started a physics club where students would gather before school and conduct experiments.
“I just sort of fell in love with physics then,” Reyes says. “And luckily, as I began to study it more and more, I found I landed exactly where I wanted to be.”
Reyes has carried that enthusiasm for physics into not only her research but her role as a personal tutor and teaching assistant at MIT, where she works to foster the same love of the subject in first- and second-year undergraduate students.
As an experimental particle physicist and sixth-year PhD student in the Formaggio Group in the Laboratory for Nuclear Science, Reyes studies neutrinos, elementary particles that have vanishingly little mass and rarely interact with other matter. Neutrinos are produced during radioactive decay, including the processes taking place inside nuclear reactors. Because they interact so infrequently, detecting them can be difficult. (We can’t feel them, but neutrinos from the sun are streaming through our bodies every minute.) Their unusual behavior makes them valuable to physicists seeking to understand what lies beyond the Standard Model, the framework that describes many of the fundamental particles and forces in nature.
“The Standard Model is extremely accurate and describes most of everything that we see,” Reyes says. “But it’s not complete.”
Reyes is a member of the Ricochet neutrino experiment, an international collaboration studying neutrinos produced by a nuclear reactor at the Institut Laue-Langevin in Grenoble, France. The experiment seeks to observe coherent elastic neutrino-nucleus scattering, a low-energy interaction in which a neutrino scatters off an atomic nucleus.
Through Ricochet, scientists aim to investigate some properties of these elusive particles, and contribute to, as Reyes puts it, “just fundamentally understanding the world in which we live.”
When looking back at her time in graduate school, Reyes’ path has not always followed the plan she initially envisioned.
When she joined Ricochet, she expected to work on a particular project located at MIT. But a few years into her PhD, it was clear that the project would not be ready within her timeline. Reyes instead shifted her focus to work taking place in France, where she began learning the technical details of the experiment’s detectors.
Her first visit lasted three months. At the time, Ricochet had two detectors, and Reyes spent much of her time performing the routine work required to understand how they operated.
As the experiment expanded to nine detectors and eventually 18, the amount of work required to manage the system grew substantially. Reyes and a colleague recognized that many of the repetitive tasks could be automated.
Together, they developed a software framework that could perform much of the low-level analysis and detector monitoring that Reyes had initially carried out manually.
The project became an important part of her development as a physicist. By working closely with the detectors and helping build tools to manage them, Reyes gained a detailed understanding of the experiment’s operations.
“It’s sort of like you’re building your own stuff to replace yourself,” she says. “Which is nice in a way because you can save yourself a lot of time.”
The opportunity was both validating and humbling. As a graduate student, she had moved from learning the basics of the experiment to helping guide the work of other scientists.
“It felt like my collaborators trusted me, and I had something of value to give to the collaboration,” Reyes says.
The people she has met through MIT and the Ricochet collaboration have been among the most rewarding parts of her graduate experience. Students, mentors, and collaborators have helped her think critically and become a better physicist, she says.
Her increasing leadership responsibilities have also changed how she approaches research.
Earlier in her academic career, Reyes says, she was more comfortable being told what to do than proposing her own scientific ideas. Over time, leading projects and coordinating groups pushed her to become more confident in her judgment.
“I think I was sometimes not really standing up for myself,” she says. “But now I feel more confident in my position and my prowess as a physicist.”
That confidence has become one of the most important lessons of her PhD.
After completing her doctorate, Reyes hopes to continue conducting research. She is considering a postdoctoral position, which would allow her to continue working in physics at another institution.
Her time in France has also influenced her vision of the future. Reyes spent nine months there through the Chateaubriand Fellowship, following two earlier three-month visits. While the latest trip was primarily focused on research, working in the same office as her collaborators made it easier to coordinate across time zones and strengthened her connection to the experiment. She also grew fond of the country’s culture and work-life balance and would even consider living there.
“I fell in love with France and the people,” she says. “And also, the work culture.”
Outside the lab, Reyes enjoys playing video games and crocheting, a hobby she picked up during her time in France. She often crochets while watching movies or television, appreciating the opportunity to work with her hands while thinking about other things.
For a physicist whose work involves investigating some of the universe’s smallest and most elusive particles, the hobby offers a different kind of satisfaction: creating something tangible.
As Reyes moves toward the next stage of her career, she hopes to continue pursuing the questions that first drew her to physics. Her research may help reveal what lies beyond the Standard Model, but her experience at MIT has also shown her how much science depends on collaboration, adaptability, and the confidence to lead.
“I’ve learned a lot of lessons,” Reyes says. “Especially about wrangling people.”
Estimating suicide risk from textA new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.When people reach out during a mental health crisis, a top priority for counselors is identifying those with a high risk of suicide. The distressed person’s language holds critical clues, and a new tool developed by scientists at MIT’s McGovern Institute for Brain Research is designed to pick up on and rapidly evaluate those signals.
The language-processing tool was developed by Daniel Low, a former graduate student in Senior Research Scientist Satra Ghosh’s Senseable Intelligence Group who is now a research scientist at the Child Mind Institute, where he leads its AI, Risk, and Contemplative Science Lab, as well as a visiting scholar at Harvard University. It uses a custom-built list of words and phrases linked to 49 suicide risk factors, searching text for these and using them to estimate an individual’s risk.
Ghosh, Low, and colleagues report today in the Journal of Psychopathology and Clinical Science that their tool accurately predicts suicide risk from text conversations with crisis counselors. It is already helping to clarify which suicide risk factors matter most in times of crisis. With more validation, it could help with risk assessment in clinical settings and crisis-support situations.
Identifying key risk factors
Suicide attempts are notoriously difficult to predict. Dozens of risk factors have been linked to suicide, and even trained clinicians struggle to identify who will make an attempt among those who have some form of suicidal ideation. Among the factors that can make suicidal thoughts and behaviors more likely are certain psychiatric symptoms and disorders, like depression, borderline personality disorder, and post-traumatic stress disorder, as well as environmental and social stressors, like poverty, incarceration, discrimination, and loneliness.
“You see all these 50 risk factors, and they're all interacting in ways we don't really understand,” Low says. “Many different pathways could lead to someone feeling they want to escape their internal pain,” he says — and it’s challenging to know whose path will lead to a suicide attempt or death.
Ghosh and Low wanted to understand which risk factors counselors and clinicians should most look out for during a mental health crisis. To do that, they collaborated with the Crisis Text Line, whose trained volunteers provide confidential text-based support to people in distress.
Crisis Text Line, a global mental health nonprofit that provides free, 24/7, confidential mental health support for people in need, provided specialized training and controlled access to this restricted dataset. The researchers analyzed de-identified texts from approximately 16,000 conversations with Crisis Text Line’s volunteer crisis counselors. Based on Crisis Text Line’s assessments, those conversations were grouped into three different risk levels: non-suicidal, suicidal ideation without imminent risk, and imminent risk. It was this imminent risk group — those with a plan for suicide, or who have an intent to die within the next 48 hours — that the researchers most wanted to understand.
“We wanted to know what type of symptoms predict the highest suicide risk,” Low says. This question has been studied before, he says — but typically through epidemiological surveys that ask a person to recall their symptoms and experiences, often after their mental health crisis has passed. In contrast, he says, “Crisis Text Line gives us an opportunity to assess many different symptoms and potential risk factors as people are having the crises.”
Reading between the lines
Before analyzing the crisis line texts, the research team built a suicide-risk lexicon. They turned to artificial intelligence to generate a preliminary list of words and phrases tied to established suicide risk factors, including factors associated with suicidal ideation, suicide attempt, and suicide death. Then they manually reviewed and curated that list. Their final lexicon includes about 60 words or phrases for each of 49 risk factors, with the relevance of each one confirmed by expert clinicians.
Then they trained a machine learning model to search the crisis conversations for words and phrases in their lexicon and use these to predict suicide risk. Because the lexicon links each word or phrase to a specific risk factor, they could use these data to determine which risk factors are most closely tied to imminent risk among people in crisis.
What they found was consistent with patterns found in previous research, although not always intuitive. For example, depression is a well-known risk factor for suicidal ideation, but their model found that mentions of lethal means and substance use were more likely to be expressed by the highest-risk group than depressed mood or fatigue. Expressions of active suicidal ideation and self-injury were also strong predictors. Intermediate predictors included anxiety, post-traumatic stress disorder, and emotional pain.
The predictive model assigns a weight to each risk factor based on its contribution to risk. For example, mentions of lethal means for suicide, like “cut” or “pills,” are weighed heavily, whereas terms related to hopelessness, like “don’t know what to do” or “hopeless,” contribute to a lesser degree. After training their model, the team found they could use it to accurately predict risk severity in new conversations the model had not previously seen.
One limitation of lexicons, the researchers note, is that they do not consider the context of terms, and they can miss terms that are similar to those in the lexicon, but not explicitly included. Large language models have reasoning abilities, and Low and colleagues have developed ways of using large language models to detect suicide risk in other projects. However, they say they often use their lexicon in parallel to guarantee flagging certain terms, as well as to maintain data privacy.
Low stresses that while the team used the power of a large language model to develop its lexicon, its prediction model is a simpler, “lightweight” model. Unlike large language models, which require massive computational power, it can be run easily on a personal computer, reducing both cost and privacy concerns. Just as importantly, it is interpretable: Rather than merely generating a risk estimate like some deep learning models can do more effectively, it tells users how it got there. Words of concern can be flagged so users understand the basis for each assessment and act on that information. They are working on similar explainability approaches with large language models.
That’s critical, because the stakes are so high. “This is such a complex space that having a human in the loop is, I think, going to be critical for a long, long time,” says Ghosh, who is the director of the Open Data in Neuroscience Initiative at the McGovern Institute. Likewise, the researchers add that any predictive model must be thoroughly validated before clinical use, and might need to be continually refined to keep up with changes in language use or target populations.
Because a reliable lexicon opens doors to new ways of understanding mental health, Ghosh and Low are widely sharing not just their suicide risk lexicon, but also the software package they developed to build it. Researchers can use that tool to efficiently build lexicons for other mental health conditions. Meanwhile, Low says, the suicide risk lexicon is already being used to explore how text data from a variety of sources, from social media to electronic health records, might help researchers and clinicians better estimate risk.
Biologists identify a cellular pathway that allows colorectal cancer to metastasizeThey also found that obesity may put patients at higher risk for activation of the pathway. Drugs that block the pathway may help prevent metastasis.Most colon cancer deaths are caused by the spread of tumor cells beyond the colon, usually to the liver. In a new study, MIT biologists identified a cellular pathway necessary for colorectal cancer metastasis.
The pathway they identified, controlled by a protein known as YAP1, is normally involved in tissue repair. When activated in cancer cells, it promotes cell proliferation and migration. The researchers also found that a high-fat diet is more likely to turn on this pathway, through the production of fatty molecules called ceramides.
Drugs that block ceramide production could offer a new way to help prevent metastasis in patients diagnosed with colon cancer, the researchers say.
“We’ve found a pathway that we think is druggable. If we shut down the enzymes that make ceramides, tumor cells can’t switch on this regenerative program, and they largely fail to seed metastases in the liver,” says Omer Yilmaz, director of the MIT Stem Cell Initiative, a professor of biology at MIT and a member of MIT’s Koch Institute for Integrative Cancer Research. He is also a gastrointestinal pathologist and director of translational research in pathology at Beth Israel Deaconess Medical Center.
Yilmaz, Nilay Sethi, an assistant professor of medicine at Harvard Medical School and Dana Farber Cancer Institute, and Alpaslan Tasdogan, head of the Institute for Tumor Metabolism and a professor in the Department of Dermatology at University Hospital Essen and the German Cancer Consortium (DKTK), are the senior authors of the study, which appears today in Science. MIT postdocs Swagata Goswami, Qiming Zhang, and Abdullah Burak Yildiz are the paper’s lead authors.
A hijacked pathway
In the United States, colon cancer is usually diagnosed at stage 2 or 3 — before the cancer has spread. However, even after successful surgery, up to a third of these patients will relapse with metastatic disease.
While scientists have identified many genetic mutations that drive the development of colon cancer, it’s unknown exactly what prompts them to spread beyond the colon.
“Many studies have looked for a genetic driver of metastasis and come up empty,” Yilmaz says. “There isn’t a defining mutational signature that separates metastatic cells from the primary tumor, which points to metastasis being driven largely by changes in which genes are switched on and off, rather than by new mutations.”
In this study, the researchers sought to identify epigenetic programs that enable colon cancer cells to metastasize. Using tumor organoids from mouse models of several types of colon cancer and from patients with colorectal cancer, they found that metastatic cells shared one key feature: activation of the YAP1 program.
YAP1 is a protein that works with partner factors to switch on genes related to development, stem cell maintenance, and regeneration. In normal tissue, it is active during fetal development, and after injury, to promote healing.
In the gut, that repair response runs through a rare, fetal-like cell type, which normally appears only briefly to rebuild the intestinal lining after damage. YAP1 has been linked to cancer for years, but the new work shows that diet-derived lipids push tumor cells into this specific regenerative state — and that the state itself is what licenses metastasis.
“The regenerative program that we described is generally observed in the gut when there is severe injury or infection and the gut needs to regenerate. We see the tumor cells hijack this program to drive metastatic progression,” Goswami says.
Activation of this set of genes helps cancer cells to break free from the original tumor site and spread to other locations in the body. For colon cancer, the most common site of metastasis is the liver, followed by the lungs.
In mouse studies, the researchers also found that cancer cells in animals fed a high-fat diet turned on YAP1 to a greater extent than mice fed a healthy diet. A high-fat diet, the researchers found, triggers activation of enzymes that produce ceramides, a type of lipid. Ceramides then release the molecular brake that normally keeps YAP1 inactive, allowing it to move into the nucleus and switch on its target genes.
Preventing metastasis
The researchers showed that genetically targeting YAP1, or the genes involved in ceramide production, markedly reduced the spread of colon cancer to the liver in mice.
To determine if YAP1 is also involved in metastasis in humans, the researchers analyzed RNA sequencing data from patients with colorectal cancer. They found that YAP1 was more active in metastatic cancer cells, and that patients with higher body mass index (BMI) showed higher expression of the genes activated by YAP1 than normal-weight patients. Patients with higher levels of those genes also had lower survival rates.
“We don’t think that the YAP1 program is specific to obesity. It’s just that it becomes accentuated in obesity, and that may account for why obesity is known to drive the progression of colorectal cancer,” Yilmaz says.
They now plan to develop drugs that inhibit two of the enzymes involved in ceramide production, DEGS1 and DEGS2, in hopes that such drugs could help prevent colon cancer metastasis.
The researchers caution that the findings do not yet translate into dietary advice for patients who have already been diagnosed, and that any drug targeting ceramide synthesis will have to clear a high bar for selectivity, since these lipids are also essential in healthy tissues.
The research was funded by the National Institutes of Health/National Cancer Institute, the MIT Stem Cell Initiative, a Koch Institute Frontier grant, and the NRW Junior Research Program.
How the brain keeps its options straight at decision timeMaking a decision requires juggling a set of options without getting them confused or losing track of them. A new MIT study shows how neurons encode information to maximize clarity throughout the process.A new study by neuroscientists in The Picower Institute for Learning and Memory at MIT shows how the brain encodes information throughout the decision-making process to keep options clearly in mind and to ensure that chosen and unchosen options are remembered.
The key, the researchers show in the journal iScience, is that the brain convenes ensembles to produce coordinated patterns of electrical activity that distinctly represent and sort options, both during consideration and after choice.
“It keeps different neural ensembles, different thoughts, distinct from one another, preventing interference between them,” says senior author Earl K. Miller, Picower Professor in MIT’s Department of Brain and Cognitive Sciences.
Lead author Huidi Li, a graduate student in Miller’s lab, says the study results show how the brain responds dynamically to meet the challenge of decision-making.
“The brain doesn’t just hold information statically,” Li says. “Throughout the decision process, the brain flexibly reorganizes information representation to meet the changing task demands.”
Decisions decoded
To conduct the study, Li, Miller, and their team trained two animals to play a game in which they had to look in the direction of one of two indicated targets on a screen, based on which target was assigned the higher reward value. Importantly, the two options were presented and their values were assigned in sequence — first one target, then its value, then the other target and then its value. That way, the brain had to juggle multiple representations for each target — for instance, the order of presentation before the decision, and then chosen-or-not after the decision. Meanwhile, each time the animals played the game, researchers measured the electrical activity of hundreds of neurons in the lateral prefrontal cortex, a surface brain region known for having a key role in linking options, values, and actions in decision-making.
Using “declassifier” algorithms to decode the electrical patterns, the researchers found that the neurons acted in functional ensembles whose collective activity clearly indicated decision-related information, including the distinct target directions and their assigned values. To interpret and compare each ensemble’s patterns, the researchers visualized these “subspaces” geometrically as planes on a 3D graph.
The researchers’ key finding was that before the values were assigned and a decision was made, the neural ensemble patterns consistently represented options based on their order of presentation. For example, on the graphs each “target 1” plane was nicely parallel with the others. Similarly, each “target 2” plane was parallel with its brethren, but the target 2s were more orthogonal, or more perpendicular, with the target 1s, showing that they were represented as entirely distinct from each other.
Then, after the decision, new ensembles provided new representations. Now the “chosen” options, whether they had been presented first or second, had parallel representations. The unchosen targets were also represented as parallel with each other, but as orthogonal from the chosen ones.
Getting one’s neural ducks in a row
In other words, before the decision, the brain convened ensembles of neurons to distinguish targets by their presentation order, and then after the decisions, gathered ensembles to sort them by whether they were chosen or not. This consistent way of representing chosen options, Li and Miller wrote, could aid decision-making by essentially packaging it for downstream circuits responsible for converting the decision into action (in this case, directing the animal’s gaze in the chosen target direction).
“The observed alignment of chosen target representations could allow downstream areas to read out the location of the chosen target with a single decoder, regardless of its initial presentation order,” the authors wrote.
Notably, the researchers also found that round by round of the game, individual neurons could often be recruited in to different ensembles. A neuron that in one round seemed “selective” for option 2 could end up being selective for option 1 the next. The ensembles were therefore not permanent circuits of specialized neurons, but instead were assembled ad hoc among multifunctional neurons.
In other research, Miller has found that the brain uses brain waves to rapidly and flexibly accomplish this goal of ensemble recruitment.
Another clear implication of the data, Miller says, is that the brain maintained distinct memories of each option, whether it was chosen or not. This could be important for assigning credit down the line to facilitate learning. For instance, remembering that choosing target 2 in round 3 earned a reward.
“Our results illustrate the dynamic subspace reorganization supporting option maintenance and selection in economic decisions,” the authors wrote.
In addition to Li and Miller, the paper’s other authors are Nikolaos Chrysanthidis, Scott Brincat, and Jonas Rose.
The U.S. Office of Naval Research, the U.S. Army Research Office, the Freedom Together Foundation, and the National Institutes of Health provided support for the research.
Poitras Center to fuel early careers of 50 young scientists dedicated to psychiatric disorders researchPatricia and James Poitras ’63 provide fellowships for graduate students and postdocs who will shape the future of mental health research.Patricia and James Poitras ’63, longtime MIT supporters, have launched a fellowship program for graduate students and postdocs studying major mental illness, expanding their MIT philanthropy to directly support early-career scientists. The commitment establishes 50 two-year fellowships through the Poitras Center for Psychiatric Disorders Research at MIT’s McGovern Institute for Brain Research. Five fellowships will be awarded every year for the next decade, creating a long-term talent pipeline focused specifically on psychiatric disorders.
The $10 million gift is the latest in a series of philanthropic investments from the Poitras family to strengthen MIT’s capacity to address the growing burden of severe depression and anxiety, bipolar disorder, schizophrenia, and other complex psychiatric conditions. “Pat and Jim have remained steadfast in their decades-long commitment to bold research that can transform mental wellness,” says Robert Desimone, director of the McGovern Institute and head of the Poitras Center. “Their remarkable support of rising talent in the MIT ecosystem is yet another emblem of their commitment to that cause.”
A philanthropic legacy
Many recent mental health discoveries emerging from MIT — from molecular tools that can rewrite DNA to artificial intelligence-powered technologies that can calculate a person’s risk for developing mental illness — were hard to imagine two decades ago. Yet, Patricia and James Poitras envisioned a future where enigmatic mental health conditions could be solved. They understood this future would require not just research, but a fundamental reimagining of how psychiatric research itself is conducted.
In 2007, inspired by meetings with leadership at the McGovern Institute, the Poitras Family gifted $20 million to launch the Poitras Center. By bridging the fields of basic neuroscience, clinical psychiatry, and molecular biology, the center sought to establish a unified blueprint for understanding how psychiatric disorders hijack the mind at every level, from molecular mechanisms to whole brain systems, and guide the creation of novel therapies to better treat them.
Since the center’s establishment, additional investments by the Poitras family have supported research ranging from genome engineering to cognitive neuroscience. These investments have empowered scientists across disciplines to pursue innovative research questions, including how ketamine acts on synaptic communication and why schizophrenia distorts inner speech and reasoning. These efforts have ushered in major breakthroughs in mental health: an AI-powered calculator for predicting bipolar disorder risk in adolescents, molecular carriers that precisely deliver therapies throughout the body, and strategies that use patients’ brain activity patterns to match them with optimal treatments, among other advances.
Expanding support for early career scientists
The Poitras family’s latest gift invests directly in the PhD students and postdocs who will carry the field of psychiatric research forward. It comes at a time when federal funding has grown especially precarious. “To make the greatest impact on the world’s mental health, we recognize that we must not only support transformational research, but also the young people driving its progress,” says James Poitras, who is also chair of the McGovern Institute’s board.
Five McGovern Institute researchers have been selected as the inaugural cohort of Poitras Center Fellows and Graduate Scholars. Their projects span multiple areas in brain research and could reveal a suite of new ways to heal the mind.
The new gift extends the Poitras family’s support of mental health research at MIT to over $100 million. It also marks another step toward a bold vision years in the making.
“Serious brain disorders profoundly affect patients, families, and caregivers,” says Patricia Poitras. “We believe that investing in the next generation of researchers will accelerate powerful discoveries that lead to life-changing treatments and better future for countless patients and families.”
The next application window for Poitras Center fellowships will open in May 2027.
Finding purpose through researchThis summer, BSG-MSRP-Bio student Marina Milea investigated how lung cancers become resistant to targeted therapies, gaining hands-on research experience in the Jacks Lab at the Koch Institute.Most mornings this summer, Marina Milea arrived at the Koch Institute for Integrative Cancer Research building ready to juggle several experiments at once. While one set of samples incubated, she stained mouse tissue sections for immunohistochemical analysis, prepared to run a Western Blot gel, and checked in on an organoid culture.
All this work, and more, was part of learning the complex workflows behind studying how cancer evolves over time in the Jacks Lab at MIT. For the rising senior, who is majoring in biology at the City College of New York (CCNY), the pace was exactly what she had hoped to find through MIT's Bernard S. and Sophie G. Gould MIT Summer Research Program in Biology (BSG-MSRP-Bio).
"The techniques can be taught," she says. "The hardest part has been understanding the complex mouse and organoid models and why we're using them. Once you understand the biology behind the model, you can really interpret your results and think about how they might translate to human biology."
Milea is investigating how lung cancers driven by mutations in the KRAS gene become resistant to targeted therapies by transforming into a different subtype that is often harder to detect and treat, a phenomenon called adeno-to-squamous transition. By studying the signaling pathways and protein families that support this transition, researchers hope to identify new therapeutic targets for patients whose histologically-transformed cancers no longer respond to treatment.
"I wanted to do something that had translational aspects to it — to work on research that could potentially change how patients receive therapy," she says. "That's incredibly motivating as an undergraduate."
Building a foundation
Milea says CCNY has played an important role in helping her pursue research to build upon her strong academic foundation. Located in New York City, the university is uniquely positioned to foster collaborations with nearby institutions, connecting students with laboratory experiences across the city while serving a diverse student population that includes many first-generation and low-income students.
Milea's interest in biology began while attending high school in England, where students choose academic subjects early. Initially drawn to medicine, she pivoted to biomedical research after being diagnosed with an understudied health condition, sparking her curiosity about the mechanisms underlying disease.
Before coming to MIT, Milea gained research experience in several laboratories, most notably at Columbia University between the Azizi and McFaline-Figueroa labs.
"I went from having no cell culture experience to learning CRISPR techniques, T-cell engineering, and machine-learning approaches in a single summer," she says. "It was intense, but it gave me confidence that I could handle a research environment like MIT's."
Learning to think like a scientist
At MIT, Milea found herself in a laboratory that matched both her scientific interests and her desire for close mentorship, working with graduate student Carrie Rodriguez.
"I could tell Carrie genuinely wanted to teach," she says. "She explains not just the protocols, but the biology behind them. By understanding why we're doing each experiment, I could contribute my own ideas."
As the weeks progressed, Rodriguez gradually entrusted Milea with carrying out more and more work independently.
"By the second month, I was running entire workflows on my own," Milea says. "I felt like I was really helping move the project forward."
Milea's willingness to learn and engage deeply with the science made her a valuable member of the lab.
"Marina arrived in the lab with an outstanding attitude, ready to take full advantage of this opportunity. Over the course of the summer, she was able to learn a number of new techniques and, more importantly, dig deep into the biology of lung cancer. She was a wonderful addition to the lab," Tyler Jacks says.
Looking ahead
Outside the laboratory, faculty lectures, journal clubs, and conversations with researchers all play a part in broadening the scientific perspective of BSG-MSRP-Bio program students. A lecture by MIT Professor David C. Page, for example, whose work explores sex differences in health and disease, reinforced Milea's long-term goal of advancing research in women's health.
"He talked about pursuing scientific questions because you believe they're important, even if they're not yet considered priorities," she says. "That, in particular, resonated deeply with me."
Following graduation, Milea plans to pursue a PhD in biomedical sciences and hopes to build a career that combines research, teaching, and mentorship.
A program that values potential
Looking back, Milea hopes other students will feel confident pursuing opportunities that initially appear out of reach.
"A lot of people count themselves out without understanding what a program like this one is looking for," she says. "They value people who have original thinking and who can really contribute to the projects intellectually and practically."
Although the BSG-MSRP-Bio program is one of the country's premier undergraduate research programs, she believes its commitment to fostering students' potential is what makes it exceptional.
"It's somehow the most competitive and the most open-access program there is in the country," she says. "You can be an international student, first-generation, low-income, or from a non-research-intensive university — but you still need to meet high expectations. It's somehow both, which is great."
For Milea, that's what makes the program unique.
"They have high expectations," she says, "but you can be anybody."
MIT researchers are mapping extreme weather risks — and building tools to act on themThe Climate Grand Challenges “New World of Weather” project shows how modeling, planning tools, and infrastructure analysis are translating research into real-world resilience.Warming temperatures are fueling more extreme weather-related events — catastrophic floods, severe hurricanes and cyclones, and wildfires exacerbated by drought. But the tools used by local communities, emergency and public safety agencies, and insurance and risk markets have not kept pace with the up-to-date data and modeling for accurately predicting how these events will evolve.
Addressing that shortcoming was one of five research areas selected for MIT’s 2022 Climate Grand Challenges, an ambitious effort to accelerate science-based solutions to climate problems. The area, titled “Preparing for a New World of Weather and Climate Extremes,” focuses on tools to help evaluate a location’s vulnerabilities to flooding, cyclones, humid heat waves, or other climate-related events.
Four years later, collaborations among more than 40 faculty and student researchers on Weather and Climate Extremes projects have yielded 29 published research papers and digital tools and datasets that are already in use or close to deployment. Individual projects cut across forecasting, risk assessment, on-the-ground planning, and resilient infrastructure.
“Communities across the United States and around the world are already confronting the consequences of extreme weather,” says Evelyn Wang, MIT’s vice president for energy and climate, whose office has been funding and supporting all of the Grand Challenges since 2024. “Through the Climate Grand Challenges, an interdisciplinary team at MIT is advancing the science, technologies, and practical strategies needed to help communities anticipate these risks and build greater resilience.”
Reducing scientific uncertainties
Paul O’Gorman, the Robert R. Shrock Professor of Earth and Planetary Sciences at MIT and co-lead of Weather and Climate Extremes, is refining the science behind forecasting extreme weather events, such as last year’s major flooding events in Central Texas and in Pakistan. “There have been a lot of unprecedented, record-breaking events,” he says, “and we want to understand how they are changing as the climate warms, and how they’re changing in different regions.”
One aspect that his group has been examining is the relationship between extreme rainfall events and a warming climate. Climate models predict that extreme rainfall increases less in summer than other seasons in much of the United States and Europe. O’Gorman’s team found that these seasonal shifts stem from not only how much water is in the atmosphere, which is measured by specific humidity, but also how close it is to saturation, which is measured by relative humidity. “We found that changes in relative humidity played a big role, which was something that hadn’t been appreciated before, and something we need to take into account,” he says.
Modeling is challenging: Relative humidity depends on air circulation, how fast land warms relative to the ocean, soil moisture, and vegetation. “It’s a complex story, but this helps us understand precipitation patterns,” O’Gorman says.
Kerry Emanuel, MIT professor (post tenure) in the Department of Earth, Atmospheric and Planetary Sciences who was also a co-lead of Weather and Climate Extremes, is researching better ways to estimate the risks of extreme hurricanes and severe convective storms, such as thunderstorms and tornadoes. “For hurricanes, we’re pretty much there. We can reproduce the statistics of real hurricanes extremely well just using coarse-grained weather data that has no hurricanes in it,” he says. But for severe convective storms, “we’re not close to being there,” and these storms “in the last decade have cost more lives and more damage than hurricanes.”
Research on the physics of storms is already influencing practice, Emanuel notes. For example, a company called First Street uses Emanuel’s methods to guide local governments, insurers, developers, and real-estate platforms on environmental risk for every piece of private property in the United States.
Improving resilience
Another phase of the Grand Challenge, led by Miho Mazereeuw, an associate professor in MIT’s Department of Architecture and a leading expert on resilient design, translates the information from scientific modeling and data collection into tools for on-the-ground planners. For example, working with leaders and community members in Boston and Broward County, Florida, the team has developed interactive web-based tools that make it easier to plan for impacts such as flooding over a broad range of scenarios.
“When an extreme event happens, there is a gap between scientific knowledge and actionable public information,” says Aditya Barve, a research scientist in Mazereeuw’s Urban Risk Lab. This happens at various levels — from getting real-time information out to people when they need it to collecting data to enable long-term planning to disseminating those plans to communities. “The idea is to target the gap through tools in community emergency data collection, proactive recovery planning, and AI-assisted tools for at-scale visualization of future climate impacts, so that communities are prepared when something happens.”
The team has worked on making flood modeling outputs usable by a wider range of stakeholders, especially where the need for specialized software or technical expertise can slow decision-making across city departments. “Users can ask practical questions, such as which schools are likely to stay driest across different flood scenarios, and receive answers grounded in flood models and city datasets within seconds,” Barve says.
As for recovery after extreme weather events, Mazereeuw points out that most municipalities have an emergency response plan, but few create a recovery plan that includes housing before the event. But, she says, if communities plan how recovery can lead to a better future for the city, they can better leverage emergency relief funding that becomes available. “In almost all cases, the resources available after a disaster are much larger,” she says. “By having a plan in place, those resources can fit the vision of the place moving forward.”
Optimizing energy infrastructure
Associate Professor Michael Howland is working to analyze the impacts of extreme weather on energy infrastructure with a team that includes Jessika Trancik, a professor in the MIT Institute of Data Systems and Society (IDSS), and Saurabh Amin, the Edmund K. Turner Professor in Civil Engineering at MIT. The team is particularly looking at impacts on the electrical power system and ways to optimize decisions on the placement and sizing of new energy infrastructure.
Howland, who is the Jeffrey Cheah Career Development Professor of Civil and Environmental Engineering at MIT, says electrical power systems are increasingly being altered by two things at the same time: first, the proliferation of renewable energy and storage technologies, and second, large-scale changes in weather and extreme events driven by climate change. “Each of these would independently push our electrical power system potentially outside of what we are used to, and their combined, synergistic impacts could be even larger because they are occurring simultaneously,” he says.
Bringing climate modeling and grid-infrastructure work together has accelerated practical insights into how we can adapt to climate change while simultaneously mitigating it, Howland notes. Such modeling can also help to inform infrastructure decisions in ways that may not be obvious. For example, he says, their optimization model for the siting of power resources in Texas resulted in placing a number of wind power plants along the Gulf Coast. “If you look at an average wind speed map,” he says, “you would say this doesn’t make much sense because it’s really windy in northwest Texas on average, and much less windy along the Gulf Coast.”
But it turns out that the typical daily cycle of winds is complementary, so that wind farms distributed between both locations tend to smooth each other out and to better complement solar power generation, easing burdens on the grid. Now, “we’re trying to take it further not just by smoothing the generation, but actually aligning it with the time- and space-varying electricity demand so that we can reduce storage, transmission, and other backup generation needs,” he says.
This work is ongoing, and the hope is that it will lead to products that can directly help utility grid planners and regulators with actionable information about the siting and sizing of various electrical infrastructure resources, Howland says. “We want to continuously push on model realism and accuracy to eventually make it more of a practical and useful tool for grid planners.”
Emanuel adds that the Weather and Climate Extremes Grand Challenge, and other projects working to pinpoint the kinds of risks that can be expected from a changing climate, have produced a great deal of specific and detailed information that could guide political, economic, and civic decision-making. Applying it in the real world can be slow — “like steering a supertanker,” he says — but progress will come.
Robotic lab sets up and runs optics experiments on demandThe autonomous system could speed up testing of high-tech materials for applications such as solar cells, sensors, video displays, and quantum technologies.Every new generation of phone display, television screen, and solar panel is a result of precision optics experiments, which use lasers and other light sources to measure the optical properties of candidate materials. These experiments can take months to run, requiring scientists to meticulously angle and adjust delicate light sources, mirrors, cameras, and other components, in a careful and constant tuning that can be physically tedious and time-consuming.
But MIT scientists say the whole process of building and running an optics experiment could one day be fully automated. Taking a step toward such a future, they have developed a reconfigurable, robotic optics laboratory.
The new robotic lab autonomously assembles standard optical components into desired configurations. It can then tune the angle and position of mirrors and lenses with micron-scale precision to produce beams of light with specific properties. The system can also safely dismantle an experiment and reassemble the parts into an entirely new setup.
The team showed that the robotic system could autonomously build and fine-tune a tabletop laser cavity — a key element of most optics experiments. The system could also precisely manipulate components to perform several optical tasks, such as centering a laser beam, aligning multiple beams, and automatically stabilizing the beams in response to physical disturbances.
“We start with randomly placed components,” says Sachin Vaidya, a postdoc in MIT’s Research Laboratory of Electronics. “At the end, we have a fully functioning laser that the robot has built.”
The researchers are expanding the robotic lab, in a physical and virtual sense. In addition to improving the system’s physical sensing, maneuvering, and overall space, they are developing a cloud-based application that gives users virtual access to the physical robot. They envision that one day, scientists from anywhere will be able to remotely access robotic optics labs and virtually submit experimental protocols or queries that the labs would then set up and run autonomously.
“There are many things this could enable,” says Marin Soljacic, the Cecil and Ida Green Professor of Physics at MIT. “A robot isn’t going to get bored. It can work 365 days, 24 hours a day, on very boring things. That will free up so much creativity and time for scientists to then push theories and see what we can do. Science could progress much faster.”
The MIT team will present the details of the new system at the Intelligent Robots and Systems (IROS) conference later this month. Along with Soljacic and Vaidya, project team members include co-lead Seou Choi, Caio Silva, and Shrish Choudhury from MIT, Shiekh Uddin of Nokia Bell Labs, and Sajib Shuvo of Arizona State University.
A city of light
A tabletop optics experiment can resemble a miniature city of densely packed mirrors, lenses, and light sources. Scientists manually arrange and align the various components in precise configurations, then shine light into the experiment. The lenses and mirrors bounce and focus the beam into a desired wavelength, frequency, or intensity that can then be used to probe or manipulate a given material.
“Sometimes this manual setup takes days or months depending on the complexity of the experiment,” Soljacic says. “It’s meticulous work that has to be done again and again for each experiment.”
Most labs do incorporate some level of automation in an optics setup, such as motorized tuners that mechanically turn knobs to precisely angle a mirror.
“These components can automate the most tedious parts of an experiment,” Vaidya notes. “But no one has built a full system that goes from no setup to a completely aligned setup in one tool. That was our goal, to show complete automation through all the steps that go into an optics experiment.”
Auto-tuned optics
The team’s robotic lab centers around a robotic arm with seven moveable joints that is attached to a metallic tabletop. The robot picks and places lenses, mirrors, and other optical components, each of which the researchers installed in its own 3D-printed plastic housing.
The housings are designed such that the robot can easily and safely grip and move each component. The researchers etched the top of each housing with a QR code containing information about the component within the housing (such as whether it is a lens versus a mirror, and its exact dimensions and capabilities). Each housing has a magnetic base that helps stabilize a component once the arm places it down on the metallic tabletop.
The researchers designed a Wi-Fi-enabled “fine-adjustment tool” that clips onto the mount of standard optical components. The motorized tool can be wirelessly controlled to turn a component’s knobs, for instance to angle a mirror.
“The way humans do this tuning is by feel, and based on a lot of intuition,” Vaidya says. “This tool is at least as precise as a human, but in reality it is much more precise.”
The team also installed a pair of cameras over the entire setup that provides a birds-eye view of the tabletop experiment. Finally, they developed a “software stack,” or a set of programs that enables the robot to navigate through every step of setting up and continuously tuning an experiment. These steps include recognizing a specific component, knowing how to safely approach and pick it up, where to move it, and how to avoid collisions with other parts of the experiment along the way.
Finally, they designed a simple virtual user interface to allow an experimenter to remotely direct the robot. For instance, when a user drags the icon for a mirror from one spot to another, and clicks a button to confirm, the robot responds by picking up the actual mirror and placing it down at the corresponding location on the table.
As a demonstration, they directed the robot to assemble various components into a laser cavity. A laser cavity consists of two mirrors arranged on either side of a crystal. When a beam of light is shone into the setup, it pings back and forth between the two mirrors. With each pass, the light also passes through the crystal, which amplifies the light’s intensity, to a point that whatever light escapes, is intense enough to form a laser.
“We wanted to pick a demonstration in optics that’s reasonably challenging,” says co-lead author Seou Choi, a graduate student in electrical engineering and computer science. “This is not something a new trainee could do in an afternoon. It requires a lot of alignment and component experience.”
In the end, the robot successfully built a functional laser cavity by autonomously carrying out 50 maneuvers, all within 30 minutes. When the researchers introduced physical disturbances to the setup, such as randomly moving a component on the table, the system automatically readjusted components to maintain the laser’s intensity.
“Even tiny vibrations or temperature changes can degrade an optics experiment,” Vaidya says. “An autonomous lab could continuously monitor its own performance and repair the alignment before valuable data is lost.”
The researchers envision that robotic labs like theirs could be paired with a nearby library of physical components that another robot could fetch and deliver to a tabletop robot to arrange into an experiment. Such a system could work to build and run experiments, then break them down and set up new ones on demand, or continuously run an experiment that requires active 24/7 monitoring.
“A system like this could help industry test prototypes faster, for everything from cameras and displays to solar cells and AR/VR goggles,” Vaidya says.
For their part, the researchers are applying the new robot lab to test promising carbon-capture materials. By shining light with specific properties at these materials, they can get information about how a material absorbs carbon dioxide.
“Experimental optics is the backbone of many important fields,” Vaidya says. “Our work takes the first step toward optical labs that can operate faster, more reliably, and without manual intervention in a domain that demands extreme precision and diversity of experimental setups.”
This research was supported, in part, by the Korea Foundation for Advanced Studies Overseas PhD Scholarship, the U.S. National Science Foundation, the U.S. Army DEVCOM ARL Army Research Office, Parviz Tayebati, the MIT Undergraduate Research Opportunities Program (UROP), the MIT Generative AI Impact Consortium (MGAIC), and Shell International Exploration and Production Inc.
New artist residency program at MIT expands views of the cosmosInaugural artist Amy Karle will join astrophysicists at MIT’s Kavli Institute to explore questions about the universe and translate them into an immersive multimedia experience.MIT’s Kavli Institute for Astrophysics and Space Research (MKI) is launching a pilot artist-in-residence program to facilitate cross-disciplinary dialogue between art, science, and the public.
MKI is a world-leading institution for research in astrophysics, combining more than 60 years of expertise in space and ground-based instrumentation development with the intellectual energy of MIT’s faculty, research and technical staff, and students in the departments of Aeronautical and Astronautical Engineering; Earth, Atmospheric and Planetary Sciences; and Physics.
During the 2026-27 academic year, internationally acclaimed ultra-contemporary artist Amy Karle will work as the program’s inaugural artist-in-residence alongside MKI researchers to explore the research and processes behind cutting-edge astrophysical discoveries and instrumentation, and to translate this experience into an immersive, multimedia installation available for public display beginning in early 2028. Karle is known for her work as an artist, designer, and researcher whose projects explore how science and technology shape humanity, evolution, and the future across scales and systems, from cells to cosmos.
“We are excited to work with Amy in this collaborative environment” says MKI Director Robert Simcoe, the Bruno B. Rossi Professor of Experimental Physics at MIT. “Her approach is unlike anything we have previously experienced at MKI and presents many opportunities to challenge the way we, as scientists and engineers, think about our study of the universe. At the same time, the resulting artwork will be shaped by the deep research we do, and the wide-ranging scientific and technical perspectives of the MKI community.”
Karle’s proposal, which envisions astrophysical research and data as a co-creative experience toward embodied understanding of cosmic phenomena and touches on themes of scientific observation, signals, and inference, was selected by an interdisciplinary committee of astronomers, museum curators, and art-science practitioners. Reviewers praised Karle’s ambitious-yet-grounded approach to engagement, her attention to audience experience, her unique approach to science communication through art and technology, and the collaborative potential of her artistic vision.
“I am thrilled to be partnering with MKI,” says Karle, whose practice over the years has included dedicated art-science collaborations with Copernicus Science Centre, the Interstellar Foundation, and Studio Quantum, as well as multiple installations for museums, festivals, and public spaces across the globe. “My first job was at a public observatory. I still remember showing strangers Saturn’s rings through a telescope and watching awe and understanding arrive as felt experience. That has shaped my work since. What MKI does at the frontier of astrophysics, translating faint signals into knowledge through instruments, computation, and human judgment, is a profound expression of that same process. I am excited to be in dialogue with that work and with MKI scientists to create art that makes this tangible and deeply felt, inviting people into the threshold where our ways of knowing the universe reshape how we understand ourselves.”
The residency begins with a one-month exploratory period in the fall semester, centered on meetings with MKI researchers, attendance at seminars and classes, and a public presentation to the MKI community. The project will then move from conceptualization to development, shaped through continued exchange with MKI researchers and complementary independent work in Karle’s California studio throughout 2027.
In March, Karle and selected scientific collaborators will be in residence at the Studios at MASS MoCA, a national and international residency program embedded within one of the world’s largest and liveliest museums dedicated to contemporary art. During their time in residence, Karle and collaborators will test ideas, exchange knowledge, and engage with a multidisciplinary cohort of 16 other artists from across the globe.
“MASS MoCA [the Massachusetts Museum of Contemporary Art] is pleased to be part of MKI’s artist-in-residence program and to contribute to the meaningful exchange between art and science,” says Susan Cross, MASS MoCA director of curatorial affairs. “We look forward to welcoming artist Amy Karle and collaborators from MIT’s Kavli Institute for Astrophysics and Space Research to our campus, and to the Studios at MASS MoCA.”
The residency is supported by the Kavli Foundation’s Kavli Innovation Fund. The initiative seeks to develop new modes of public engagement with astrophysical research and discovery, and deepen emotional connections across the interplay of science and art.
“We are grateful for the Kavli Foundation’s support,” says Simcoe, “as it allows us to push boundaries and engage new audiences in the wonder of the universe and the process of science.”
Karle’s work has been exhibited internationally at institutions including Centre Pompidou, Mori Art Museum, the Smithsonian Institution, the Museum of Modern Art, Ars Electronica, ArtScience Museum, Triennale Milano, and the Victoria and Albert Museum, with works on the moon and in space. She collaborates with and presents at scientific, technological, and cultural institutions including NASA, CERN, SLAC National Accelerator Laboratory, Autodesk, HP Labs, and NVIDIA.
She was honored as one of BBC’s 100 Most Inspiring and Influential Women, a Pioneer in Design, and one of the Most Influential Women in 3D Printing. Karle also served as an American Arts Incubator U.S. Department of State artist diplomat. Her first job was at a public observatory, where she began asking fundamental questions about space and witnessing the wonder it can awaken in people, an early experience that continues to inspire her to create works that allow people to feel how we come to know the universe and our place within it.
To learn more about Karle's work, visit amykarle.com. As the project develops, MKI will be seeking museum and festival partners to host the installation in 2028 and beyond.
Nanoscale mechanics could enable brain-inspired computingA new device uses reconfigurable motion to mimic the firing behavior of a neuron, which could lead to more efficient computing.MIT researchers have created a new computing platform that could be used to develop intelligent and adaptive next-generation electronics that can simultaneously perform multiple functions, like computing and memory, all within one extremely compact, energy-efficient device.
Such a platform opens opportunities for low-power edge computing applications, interactive medical and environmental monitoring systems, and smart robots.
The researchers accomplished this by leveraging the unique mechanical response of soft polymers at the nanoscale. A mechanical response is how a structure changes when a force is applied to it.
They harnessed this response to create tiny mechanical devices that use reconfigurable motion to remember and process information in a way that mimics how neurons behave in the brain.
Because key computing functions are built into the intrinsic properties of the soft polymer material, the number of components needed to perform the functions are minimized, enabling a compact and versatile platform for information processing.
“Complex and coupled nanoscale phenomena can provide tremendous opportunities for new approaches to information processing and integrating multiple functionalities, such as computing, sensing, and actuation. This could enable levels of energy efficiency, autonomy, and reconfigurability in nanoscale devices and systems that are challenging to achieve with conventional computing platforms,” says Farnaz Niroui, an associate professor of electrical engineering and computer science (EECS), a member of the Research Laboratory of Electronics (RLE), and senior author of a paper on this device. “Here, we harness the intrinsic mechanical properties of materials to engineer device-level dynamics, such that the material building blocks play a much more active role in defining device functionality than conventionally considered.”
She is joined on the paper by co-lead authors Peter Satterthwaite and Sarah Spector, EECS graduate students; as well as Jeremiah Johnson, the A. Thomas Guertin Professor of Chemistry at MIT; Maxwell Conte, a graduate student in the Department of Materials Science and Engineering; Teddy Hsieh, an EECS graduate student; postdoc Eduard Bobylev; and Srinidhi Venkatesh ’25. The research appears today in Science Advances.
Bioinspired computation
Biological systems can leverage physical changes, like motion or deformation, to process information efficiently and without needing access to a central controller.
For instance, an octopus has a highly distributed nervous systems, with about two-thirds of its neurons spread throughout its arms. This allows the octopus to sense and process information about its environment locally and generate responses without requiring access to the central brain.
As an example, an octopus can mechanically change the color cells in its skin, enabling it to go through a rapid and context-specific camouflage process.
“You can think of an octopus as continuous computing matter, with computing, memory, sensing, and actuation distributed throughout its body,” Niroui adds.
Inspired by such performance, the researchers sought to develop a platform that can compute using mechanical transformations at the nanoscale. In mechanical computing, calculations are performed through physical transformations like movement and compression.
While bioinspired mechanical computing platforms have been developed at the micro and macro scales, the MIT researchers shrunk their device to the nanoscale. At this scale, even minute mechanical transformations can lead to drastic changes in a material’s properties. This can enable complex computing in an energy-efficient platform.
But achieving the reversible nanomechanical transformations needed for such computing is a fundamental challenge. When two surfaces come very close, they experience strong adhesive forces that pull the surfaces together, making them impossible to unstick.
To overcome this fundamental challenge, the researchers built a device with a super-thin film of the soft polymer polydimethylsiloxane (PDMS) sandwiched between two metal electrodes. This soft spacer balances the adhesive forces between the two metal surfaces, keeping the electrodes from crashing together in an irreversible way.
“The soft material serves as a ‘nano-spring,’ to help balance the forces to achieve nanoscale mechanical reconfiguration in a controlled and reversible manner,” Niroui explains.
When the researchers apply a voltage to the device, the two metal plates attract to one another, compressing the soft material and altering the electrical current flowing through the device.
“PDMS is viscoelastic, which means that after being compressed, it takes time to return to its original state. This allows the devices to dynamically remember the history of forces and voltages applied to them, and convert that history into an electrical response,” says Satterthwaite.
They researchers used this performance to demonstrate an artificial neuron.
Brain-inspired information processing
In the brain, each neuron accumulates an electrical charge a little bit at a time until it reaches a threshold and fires, passing information to other neurons in the network.
The researchers’ device mirrors this behavior. As voltage is applied over time, it accumulates stimulus as the electrodes gradually compress the PDMS. After crossing a threshold, it “fires” like a neuron before relaxing back to its original state.
“We have this complex functionality, which is the basis of biological computing, all contained in one nanoscale device,” Satterthwaite says.
Since computing and memory are incorporated within a single device with no need for external components, like capacitors or complex circuitry, this platform can achieve high energy efficiency with a small footprint.
“The performance highly relies on the memory introduced using the soft polymer. We can intentionally engineer this over a large design space to meet the requirements of the desired applications,” Spector says.
The device can also be compatible with biological systems, Spector adds. For instance, it could be useful in applications like smart prosthetics that can rapidly process tactile data or low-power wearable patches that collect and analyze health indicators in real-time.
In the future, the researchers want to expand this work to further integrate sensing with computing and memory to realize nanomechanical computing matter with applications in intelligent and adaptive systems.
This work was funded, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), an MIT EECS MathWorks Fellowship, and the Netherlands Organization for Scientific Research. Device fabrication was carried out, in part, using MIT.nano facilities.
MIT makes progress on campus climate goalsInnovative strategies, technologies, and collaborations are helping MIT advance solutions on campus and beyond.In 2021, MIT set campus decarbonization goals as part of its Fast Forward climate action plan. Five years later, many of those goals have been met or are on track for completion, including efforts to make the Institute’s buildings more efficient, expand rooftop solar installations, and attain net-zero emissions.
“Decarbonizing our campus goes hand-in-hand with MIT’s playing a leadership role in promoting carbon reduction and climate resilience through its research, innovation, and efforts to inform public policy in this area,” says Glen Shor, executive vice president and treasurer. “Our teams are leveraging that same innovative spirit to meet our campus climate goals.”
Creating an energy-efficient campus
Over the past decade, the Institute has decreased energy use per square foot by more than 10 percent, even as the campus has grown and research activity has intensified. Rooftop solar power generation has increased by more than five times in the same period, with installations added to the Stratton Student Center (Building W20), the Dewey Library (Building E53), the New Vassar undergraduate residence hall (Building W46), Graduate Junction (Buildings W87 and W88), and the theater arts building (Building W97). Thirty-three MIT building projects have earned Leadership in Energy and Environmental Design (LEED) certification. And in May, the Tina and Hamid Moghadam Building (Building 55) became MIT’s first Living Future Zero Carbon Certified building.
“We’ve completed more than 300 energy-efficiency projects across campus, focusing on our most energy-intensive research buildings, and ultimately touching nearly every corner of MIT,” notes Joe Higgins, vice president for campus services and stewardship.
Case in point: Building 46, home to the Brain and Cognitive Sciences Complex, and the Metropolitan Storage Warehouse (Building W41), newly home to the School of Architecture and Planning.
Building 46 was identified as one of MIT’s biggest energy users and the building with the greatest carbon-reduction potential. In 2024, the Institute completed a lab-by-lab renovation and improved Building 46’s mechanical systems infrastructure. The result: a 35 percent reduction in building energy use and carbon emissions — roughly a 2 percent reduction in overall campus emissions.
The newly renovated Met Warehouse, which opened in August, features an innovative heat-recovery system, capturing heat rejected from the campus cooling system and using electric heat pumps to generate heat for the building. Higgins says the system will help inform the design of larger, campus-level heat-recovery systems.
Since 2014, 101 of the 168 buildings on MIT’s Main Campus have undergone energy-efficiency upgrades. The Institute’s 2030 Capital Plan will continue to invest in projects to reduce energy consumption and make efficiency upgrades a core element of all comprehensive building renewal projects. Examples of new projects include further optimizing heat-recovery systems; deploying more sophisticated controls to better manage ventilation, heating, and cooling; and using artificial intelligence to set classroom and office temperatures based on weather forecasts, occupancy patterns, and the forecasted carbon intensity of the regional power grid.
The renovation of Building 39, which is set to be home to a next-generation quantum research laboratory, will incorporate energy-saving features and technologies, including advanced insulation and windows, a smart ventilation system, LED lighting with automatic controls, and heat-recovery systems to maximize efficiency. These integrated systems are projected to dramatically reduce energy use and carbon emissions — cutting them by approximately 70–80 percent relative to the existing building baseline.
Similarly, the McCormick Hall (Building W4) undergraduate residence hall renovation, which began this summer and is expected to be ready for students by the fall 2028 semester, will add high-performance windows, LED lighting, ventilation energy recovery, and low-flow plumbing fixtures. The project will use low-carbon flooring, improve stormwater management, and enhance the courtyard with native plantings, which require less water and maintenance while supporting local biodiversity.
On the path to net zero
MIT’s decarbonization efforts extend well beyond its campus. In recent years, the Institute has entered collaborations to create several large-scale renewable energy projects in regions of the United States where electric grids are still heavily reliant on fossil fuels. Together, these projects avoid over 200,000 tons of carbon dioxide per year, about equal to MIT’s annual direct campus emissions.
“These projects, within a very short window of time, have had a significant impact on reducing emissions,” says Higgins. “They also put us on track to reach our net-zero target this year.”
The first of these projects, the Summit Farms 60-megawatt solar farm in North Carolina, went online in 2016. Big Elm Solar in Texas, a 200 MW facility, followed in 2024, and Bowman Wind, a 208 MW wind farm in North Dakota, began operation in December 2025. Together, Big Elm and Bowman represent a landmark collaboration between MIT and 11 Massachusetts nonprofit and public sector organizations, including the City of Cambridge.
“It’s a new market model that allows smaller organizations and government agencies to achieve greater reductions in carbon emissions that wouldn’t be possible on their own,” says Julie Newman, MIT director of sustainability.
To capture the broader benefits of these projects, the Office of Sustainability worked with Institute researchers to develop a framework that assesses not only avoided emissions, but also economic and health outcomes. The team found that the projects generate economic benefits comparable to 7,000 one-year construction jobs and 189 maintenance jobs over 20 years. The projects’ annual health benefits are equivalent to 640 people quitting smoking for life, or nearly 200 premature deaths avoided each year for 20 years.
“Greener power sources are one of the building blocks we need to decarbonize our cities and campuses for the long run,” says Higgins. “That’s why we have made decarbonizing regional electricity grids a priority.”
The building blocks of campus decarbonization
To fully decarbonize MIT’s campus, the Institute will need to significantly change how it produces and distributes energy.
Currently, MIT’s Central Utilities Plant (CUP) burns natural gas to create electricity and steam-based heat, while also getting a small amount of electricity from the power grid. Electricity, heat, and air conditioning are distributed to campus buildings through a network of underground power lines and pipes.
To move away from burning natural gas, and to take advantage of electricity from a greening grid for making heat, MIT is exploring creating a large-scale electric heat pump plant adjacent to the CUP on Vassar Street. The plant, a key building block for a long-term campus decarbonization strategy, will produce hot water and distribute it to campus buildings through a hot water-based heating system.
“We’re starting the design process now, and in the coming year, we should know more about the scale and phasing of the heat pump plant we would construct, how it would interface with our existing district energy system, and the implementation timetable,” says Vasso Mathes, senior campus planner in the Office of Campus Planning, who is the campus decarbonization program manager. The heat pump plant will aim to recapture waste heat from existing cooling systems, supplying source energy to meet 30 to 40 percent of campus heating needs.
Another critical building block is transitioning MIT’s existing steam-based infrastructure to a hot-water system. That work — already underway — includes replacing steam distribution pipes to buildings with more efficient, easier-to-maintain hot-water pipes and converting buildings from steam to hot-water heat.
The third building block of a campus decarbonization strategy will be MIT’s ability to rely on the power grid for electricity instead of the CUP. “The electricity generated by the CUP is 15 to 20 percent lower in carbon emissions than the New England grid,” says Mathes. “We expect this to change over time as more and more renewables are added to the grid.” Even then, the CUP would be maintained as a backup system for use during peak heating and cooling days and grid stress events.
Finally, “the fourth building block is to go bigger, and look at shared infrastructure and coordinated planning with neighboring institutions and municipal partners,” says Higgins.
In that vein, earlier this year MIT became an anchor institution in the BosTEN Project, a year-long study to explore the feasibility of creating what could become the first city-scale thermal network in the United States. The network would help decrease the carbon footprints of major buildings across Boston and Cambridge, Massachusetts, by harnessing heat from the soil and rock under the Charles River and Boston Harbor, as well as waste heat from buildings and industrial facilities. It would also provide a renewable source of energy that can stabilize and even reduce the costs to heat and cool buildings.
“We’re thinking through how we can not only decarbonize our campus, but also how to use our work as a catalyst for broader strategies and technologies that others could readily employ,” Higgins says. “The unit of change needs to be at the city scale.”
An electrochemical approach turns ammonia into pure hydrogenAn MIT team has demonstrated a more efficient way to extract pure hydrogen gas from hydrogen carrier molecules.As a liquid that is easily stored and transported, ammonia (NH3) is an attractive carrier for hydrogen, which is used in fuel cells, semiconductor manufacturing, chemical processing, and other applications. However, breaking ammonia into hydrogen and nitrogen typically requires high temperatures, and the resulting gas mixture must undergo additional purification before the hydrogen can be used in many applications.
MIT researchers have now developed an electrochemical approach to promote hydrogen release from ammonia while simultaneously separating and concentrating the hydrogen into a high-purity stream. Their strategy, which uses electricity to speed up the extraction, reduces the temperature and energy required to recover hydrogen from ammonia and other hydrogen carriers.
In a new study, the researchers showed that their approach can generate highly concentrated, pure streams of hydrogen.
“We have shown the ability to use electrochemistry to drive thermodynamically uphill and kinetically difficult dehydrogenation reactions,” says Yogesh Surendranath, the Donner Professor of Science and a professor of chemistry and chemical engineering. “In this case, we studied the conversion of ammonia and a liquid organic molecule because of their importance as possible hydrogen carriers for a hydrogen economy. But the concepts we learned here could in principle be translated further, and we’re actively working on translating it to other important dehydrogenation reactions.”
Surendranath is the corresponding author of the study, which appears today in Nature. MIT postdoc Rui Zeng, now a professor of materials science and engineering at Harbin Institute of Technology in Shenzhen, China, is the paper’s lead author.
Extracting hydrogen
Hydrogen is widely used in semiconductor manufacturing and chemical processing and is also an energy carrier in fuel cells that use hydrogen and oxygen to generate electricity without combustion. Expanding its use, however, will require practical ways to store and distribute it.
Hydrogen gas itself is difficult to transport efficiently without compression or liquefaction. One alternative is to store hydrogen chemically in compounds that are liquids or can be readily liquefied, then release it where and when it is needed.
Ammonia is one promising hydrogen carrier because it is already produced and transported across large distances, but recovering hydrogen from ammonia remains challenging. That process, known as “cracking,” requires temperatures higher than 500 degrees Celsius to achieve high reaction rates and conversion. The hydrogen must then be separated from nitrogen and unreacted ammonia.
“We wanted to ask whether we could use electrical inputs to drive what would otherwise be an unfavorable dehydrogenation reaction, and simultaneously do it in a way that would separate the hydrogen from the hydrogen carrier, so that it would be very pure and could be used directly in a fuel cell or other application that requires a high purity hydrogen stream,” Surendranath says.
The key element of the researchers’ new design is the coupling of a palladium-based separation membrane with a hydrogen-generating electrode through a molten hydroxide electrolyte. The separation membrane selectively transports hydrogen while preventing other components of the reaction mixture from passing through.
Using the new setup, ammonia is first dehydrogenated by a catalyst containing ruthenium and cesium. The hydrogen then reaches the separation membrane, whose opposite side is in contact with a molten hydroxide electrolyte.
The electrochemical gradient across this membrane effectively creates a “vacuum” for hydrogen, providing a strong driving force for its transport across the membrane. It also converts the hydrogen into protons and electrons, which travel separately through the molten electrolyte and external circuit, respectively, before recombining at a second electrode to form hydrogen gas.
Because the membrane selectively transports hydrogen, the system produces a concentrated stream of hydrogen gas without requiring a separate downstream purification process.
“Using this electrochemical process, we’re able to do this active pumping of hydrogen from a low concentration to a high concentration,” Surendranath says.
Continuously extracting hydrogen can also help drive the dehydrogenation reaction forward, especially when the presence of hydrogen inhibits the reaction. In this way, this strategy does more than separate the product: It changes the reaction environment and enables hydrogen recovery under milder conditions.
This process thus can be performed at temperatures around 200 or 300 degrees Celsius, much lower than those required for conventional ammonia cracking. Another advantage is that it creates a pure stream of hydrogen that doesn’t need to be purified later on — a step that requires additional energy.
Curtis Berlinguette, a professor of chemistry and chemical and biological engineering at the University of British Columbia, described the method as “a powerful new way” to solve the problem of obtaining a pure stream of hydrogen from ammonia and other hydrogen carriers.
“By using electricity to pull hydrogen through the membrane as it is released, they accelerate the dehydrogenation of ammonia and liquid organic hydrogen carriers while simultaneously producing a purified hydrogen stream. This is an important advance for the energy sciences because it opens a credible pathway for transporting hydrogen in stable chemical carriers and releasing it where and when it is needed,” says Berlinguette, who was not involved in the research.
Powering transportation
In this study, the researchers showed that this approach could be used to dehydrogenate not only ammonia but also methylcyclohexane. This molecule is part of a class known as liquid organic hydrogen carriers (LOHCs), which also hold potential as an energy carrier.
The researchers envision that their new strategy could be useful for transportation applications, such as powering cars, buses, or ships, or for fabricating semiconductors or electronics. Pure hydrogen gas is used for several steps in semiconductor manufacturing, where it plays important roles in boosting manufacturing yields and reducing surface defects.
Because palladium is an expensive metal, the researchers are now working on ways to reduce the amount of palladium needed for the separation membrane. They are also working on scaling up the process, and on applying it to other dehydrogenation reactions that could be industrially useful.
The research was funded by the U.S. National Science Foundation.
New research shows a neutrino laser is impossibleDue to physical and fundamental limitations, an earlier proposal for producing laserlike beams of neutrinos cannot be achieved, scientists report.Neutrinos are the pervasive yet intangible particles that permeate the universe, streaming through whole planets, stars, and our bodies by the trillions each second. The elementary particles are often described as “ghostly” for their near-zero mass and their elusive nature, as they have very little interaction with normal matter.
Since their discovery in 1956, neutrinos have continued to surprise physicists with their unexpected properties and behaviors. For instance, the particles come in multiple “flavors” and can morph from one to the other like subatomic shape-shifters. Neutrinos may also be their own anti-particle, in a Jekyll-and-Hyde-like quantum duality. And their extremely weak interactions make them nearly impossible to detect.
Last year, scientists seemed to add to the particle’s mystique, with a concept for a neutrino laser. They proposed that a concentrated beam of neutrinos could be produced by cooling a cloud of radioactive atoms to nanokelvin temperatures, one-billionth the temperature of interstellar space. Slowed to a near-frozen crawl, the atoms would form a Bose-Einstein condensate and should act as one quantum, coherent whole, in a way that speeds up and amplifies their radioactive decay. The physicists assumed that neutrinos, being a natural byproduct of radioactive decay, should also be amplified, and that such a process should emit a laser-like beam of the ghostly particles.
But work by MIT physicists has now shown that the neutrino laser concept, and a similar proposal for gamma-rays, is impossible. In two companion papers appearing today in Physical Review Letters, Wolfgang Ketterle, the John D. MacArthur Professor of Physics at MIT, together with postdocs Hanzhen Lin and Yu-Kun Lu, presents a two-part analysis that demonstrates both concepts are physically and fundamentally not possible. More specifically, they have shown that the neutrino laser concept is flawed, due to “recoil” (as in, the kinetic energy created by the reaction), and due to a neutrino’s fundamental “fermionic” nature.
“These two papers are sort of punch one and punch two,” Ketterle says. “Each paper would have killed the proposal.”
MIT professor of physics Joe Formaggio, who put forth the neutrino laser proposal with Ben Jones, who at the time was associate professor of physics at the University of Texas at Arlington, sees the new results as a convincing and constructive challenge.
“When a new idea — such as the one we proposed — is shared, it is the duty of the community to scrutinize it. Such is the scientific process,” Formaggio says. “Indeed, it was great to see how our paper generated a lot of thinking outside of our original concept. We suspect that will continue.”
A quantum amplifier
The proposal for a neutrino laser was based on the idea of “superradiance” — a quantum, amplifying effect that had only been observed for photons.
One form of superradiance occurs when a cloud of atoms is cooled to near absolute zero, at which point an atom’s motion is determined not by thermal effects, but purely by quantum uncertainty. In this state of near standstill, which is known as a “Bose-Einstein condensate,” (BEC) the atoms move in sync, as a quantumly correlated whole.
If photons are pumped into the condensate as a laser beam, the atoms synchronize to scatter the photons back out, in the exact same direction. In contrast, a cloud of atoms at room temperature would simply scatter the photons in random directions, generating, at best, a soft glow. As photons scatter off atoms, the atoms should in turn “recoil,” as if they were physically pushed backward from the impact. In a BEC, because the atoms recoil in sync, the rate at which they scatter photons, in the same direction, grows exponentially. This amplifying effect results in a “superradiant” laser of photons, which scientists have observed.
In their proposal, Formaggio and Jones, who is now at the University of Manchester, suggested that the same superradiant effect could be possible for radioactive atoms, which naturally emit neutrinos as they decay. If a cloud of radioactive atoms were cooled to form a Bose-Einstein condensate, a similar amplifying effect should kick in and generate a concentrated beam of neutrinos as the atoms decay in sync. To illustrate their point, they outlined a scenario in which a cloud of radioactive rubidium atoms, once cooled into a BEC, would accelerate its radioactive decay, from a half-life of 86 days, to one minute.
No one has ever produced a BEC from radioactive atoms. But if it could be done, then the quantum state should, in theory, produce a neutrino laser.
Instant recoil
For Ketterle, the idea seemed too good to be true. Ketterle is the leading expert on Bose-Einstein condensates, which he co-discovered in 1995, and for which he shared the Nobel Prize in Physics in 2001. He and his group at MIT have revealed many surprising properties in Bose-Einstein condensates and other ultracold matter, where the energy of atoms is at their lowest.
“My experience has always been that the condensate can do marvelous things at low energy — superfluidity, vortices — and if you were to speak in a room filled with condensate, it would take one hour for you to hear my voice. That’s how slow the condensate is,” Ketterle says. “And I had always come to the conclusion that for anything violent, like nuclear reactions, the condensate would not do anything.”
Compared to visible photons, which have an energy of 1 electron volt, neutrinos are naturally emitted as atoms decay, with a million times more energy. When a neutrino blasts out from an atom, the emission should cause the atom in turn to recoil a million times more strongly than for visible photons.
“As long as the recoil atom stays in the condensate, it can make the condensate superradiant,” Ketterle says. “But when a neutrino is emitted at a million electronvolts, the atom recoils at velocities equivalent to Mach 10, faster than a fighter jet. This is so fast that the atom would almost instantly disappear.”
Even so, the neutrino laser proposal assumed that the escaped atom should leave a sort of quantum imprint in the condensate, which tells the condensate as a whole to emit future neutrinos in the same exact, laser-like direction.
But in the first of two new papers, Ketterle and his team show through a theoretical analysis that this is not the case. They considered a model that describes superradiance. This model determines the conditions that would lead to superradiance of photons. Ketterle applied the model to the case of radioactive atoms and neutrinos, taking into account the range of energies at which the particles are emitted, as well as the resulting recoil of the decaying atom and the dynamics of the condensate throughout.
These calculations showed that, in every scenario the team considered, superradiance was not possible. The atom simply recoiled too fast for any quantum imprint to build up. It was as if the condensate instantly loses the “memory” of the neutrino emitted, and therefore would continue emitting neutrinos as atoms normally would, without enhancement.
An anti-memory
In their second paper, the MIT researchers showed that in addition to being impossible due to a physical recoil effect, the concept of a neutrino laser is flawed due to the fundamental nature of neutrinos.
They found that even if a recoiling atom were to leave a quantum imprint in the condensate, the imprint would not be of what to emit next, but rather, what not to emit. In other words, the memory of the emitted neutrino would tell the condensate to emit the next neutrino in any other direction, preventing the buildup of a directional neutrino beam. The researchers showed that this opposing memory, or “anti-correlation,” is due to the fact that a neutrino is, fundamentally, a fermion.
Fermions and bosons are the two fundamental classes of particles that make up all the matter in the universe. Bosons are particles with whole-integer spins, such as photons. In contrast, fermions, such as electrons and neutrinos, have half-integer spins. Whether a particle has a whole or half integer spin determines how it interacts at a quantum level with other particles.
“In superradiance, it is about a memory effect, or quantum correlations in the condensate. And in that context, people had thought that whatever is emitted from the condensate, it doesn’t matter if it is a boson or a fermion,” Ketterle explains. “But we analyzed it, and if you describe it correctly for emitted fermions, you get an anti-memory, which makes the condensate not accelerate in a superradiant form. It rather has the memory to not do it.”
Ketterle, Formaggio, and Jones have met on numerous occasions to talk through the original neutrino laser proposal, and Ketterle’s challenge to it.
“I suspect that someday, someone will do the experiment,” Formaggio says. “Nature, as always, is the final arbiter of such things. And here I would be remiss to not point out that every prior prediction about neutrinos has been wrong. The one thing about neutrinos that never surprises physicists is that they never fail to surprise.”
In part, Ketterle agrees:
“Creative ideas and discussions among scientists are needed to uncover nature’s surprises,” he says. “But in the case of neutrino lasers, the surprise was too good to be true.”
This research is supported, in part, by the National Science Foundation, the Center for Ultracold Atoms, the Vannevar-Bush Faculty Fellowship, the Gordon and Betty Moore Foundation, and the U.S. Army Research Office.
Cognition and consciousness arise from analog computations, says new theoryThe brain's ability to generate quick, nimble volitional thought — and a unified awareness of thought and experience — arises from analog computations, MIT neuroscientists argue in a new review.A new theory, published in The Journal of Neuroscience by three scientists in The Picower Institute for Learning and Memory at MIT, offers an explanation of how the brain produces cognition and consciousness: It uses traveling waves of rhythmic neural activity to coordinate nimble neural networks with analog computations.
The metaphor that the brain operates with “circuits” is incomplete, says Picower Professor Earl K. Miller, the paper’s senior author. Indubitably, the brain’s physically connected circuits provide the infrastructure to store our memories and represent our ongoing needs and goals. But when we need to make improvised use of that knowledge in the rapid-fire, anything-goes sensory context the world constantly throws our way, we can’t just depend on the relatively slow chemical process of rewiring those circuit connections called “synapses,” he says.
Instead, the brain needs a control system that can coordinate millions of neurons to process information in a fraction of a second. Brain waves, long understood to be the synchronized rhythmic fluctuations of large groups of neurons, turn out to be performing that crucial service, Miller and his colleagues argue, citing years of experimental evidence from his lab and many others.
“Circuits and synapses are important and fundamental, that’s the start. But there is more going on,” says Miller, a member of MIT’s Department of Brain and Cognitive Sciences faculty. “The brain generates waves, and wave dynamics are a highly efficient way to coordinate and perform computation.”
While digital circuits make calculations one step at a time through sequential switches and gates, analog computation, which can be performed via the interference of waves, processes multiple calculations in parallel. That’s not only more efficient, but also locally focused traveling waves happen to be a ubiquitous feature of the brain, the scientists note.
“The brain exploits its own physics,” wrote Miller and co-authors Scott L. Brincat and Jefferson E. Roy, who are research scientists in Miller’s lab.
The new theory is important not only because it provides an explanation of cognition and consciousness, but also because it asserts the potential importance of considering waves in clinical treatment. Conveniently, waves can be manipulated non-invasively.
“Developing treatments based on brain wave dynamics is not just an opportunity, but also an obligation,” says Miller, whose lab is part of a collaboration studying brain waves in autism.
Building the analog argument
To make the case that the brain uses waves to coordinate neurons to produce cognition and consciousness, the scientists begin with the now well-established observation that many neurons don’t just do one job. Instead, they respond to multiple cues and contexts, essentially participating in multiple functional networks at once, a property called “mixed selectivity.” Miller and colleagues have argued for years that this gives the brain immense computational horsepower, but it also initially raised the question of how the brain organizes these multiple overlapping networks with such speed and flexibility to produce the nimble thought we all depend on.
After numerous studies, the answer that has emerged for Miller and many other neuroscientists is that brain waves organize neural ensembles to process information. Miller has shown that brain waves of different frequencies govern cognitive processes such as working memory and predictive coding. Relatively slow “alpha” and “beta” frequency waves, representing memories and goals, regulate faster frequency “gamma” waves, which represent and report incoming sensory information.
The new theory posits that these alpha/beta control waves emerge from the coordinated spiking of neurons in circuits (connected at junctions called “synapses”) that encode stored memories and goals.
“Synapses store representations, while wave dynamics help determine which representations are active at any given time,” the authors wrote.
In some of the Miller lab’s newer research, the team has found evidence that even as waves emerge from neural spiking, the waves can rapidly grow to directly influence and coordinate spiking via an electric field-mediated process called ephaptic coupling. Importantly, electric fields can exert this coordinating influence very rapidly.
Another essential component of the theory, which Miller’s lab has also shown experimentally, is that alpha/beta waves are capable of exerting their control spatially, by affecting local areas of the cortex, and temporally, by traveling along the cortex. Essentially, the beta waves act as mobile stencils that govern where and when gamma waves can process sensory information and which ensembles of neurons will participate. Taken together, this suggests that the brain engages in “spatiotemporal computing,” the authors write. And where the waves intersect, they can add and subtract, enabling analog computations.
Miller acknowledges that his lab’s next step should be to provide direct evidence that the analog computations are taking place.
“This is a theory. We aim to test it by looking for signatures of analog computation in brain wave patterns,” Miller says.
Connection to consciousness
The article asserts that consciousness “emerges when these dynamic wave patterns bring the cortex in an organized, globally integrated state, one that naturally links and influences widespread activity.”
Some of the most compelling evidence linking wave dynamics to consciousness comes from studies of general anesthesia that Miller has conducted with Picower Institute colleague Emery N. Brown, who is an Institute professor at MIT, an anesthesiologist at Massachusetts General Hospital, and a professor in Harvard Medical School. Their labs have shown that three different drugs, each with different molecular mechanisms of action, all similarly disrupt brain wave dynamics to produce unconsciousness.
“Consciousness depends less on specific receptors or cell types and more on the integrity of large-scale wave organization,” the authors write in the review.
In other words, much like cognition, consciousness depends on how the brain efficiently organizes itself with brain waves.
“Electric field dynamics offer a low-overhead substrate for organizing and coordinating information across cortical networks,” they conclude. “Given strong evolutionary pressure to maximize computation per unit energy, it would be surprising if evolution did not exploit such a built-in analog computing substrate.”
The Freedom Together Foundation, The Picower Institute for Learning and Memory, the U.S. Army Research Office, the U.S. Office of Naval Research, a MURI grant, the National Institutes of Health, and the Simons Center for the Social Brain supported the research.
Atlas of the brain’s striatum could guide researchers to new drug treatmentsA new study reveals insights into populations of neurons affected by Huntington’s disease, schizophrenia, addiction, and other disorders.A region of the brain called the striatum is critical for many cognitive and motor functions, including decision-making, control of movement, habit formation, and processing of reward. It also plays a role in addiction and is significantly affected by Huntington’s disease, schizophrenia, and other disorders.
In work that could help scientists devise new treatments for those diseases, MIT researchers have generated a new atlas of the neurons found within the striatum. Using single-cell RNA sequencing and other techniques, they were able to identify 31 subgroups of neurons based on which genes they express.
These groups include neurons that are involved in addiction, depression, and schizophrenia. The researchers also discovered why some neurons of the striatum are more vulnerable to Huntington’s disease. All of these results, the researchers say, could help scientists develop new drugs to combat these conditions.
“We see this as the foundation that will allow more studies in our Huntington’s disease and opioid use disorder projects. We needed a roadmap of what is there,” says Myriam Heiman, the Picower Professor of Neuroscience and director of MIT’s Picower Institute for Learning and Memory.
Heiman; Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard; and Dana Gabuzda, a principal investigator at Dana-Farber Cancer Institute and a professor of neurology at Brigham and Women’s Hospital and Harvard Medical School, are the senior authors of the study, which appears today in Cell. MIT postdoc Raleigh Linville and MIT graduate student Benjamin James are the paper’s lead authors.
Mapping the striatum
The striatum, located deep within the brain, receives diverse inputs from the cortex, midbrain, hippocampus, and other regions, which it uses to coordinate planning, movement, and decision-making, as well as processing reward. In this study, the researchers focused on the most populous cell type in the striatum, a type of inhibitory neuron called the medium spiny neuron, which responds to dopamine.
Most of these medium spiny neurons belong to either the direct pathway, which helps to promote movement, or the indirect pathway, which suppresses unwanted movements. These pathways are distinguishable by what type of dopamine receptor they express — dopamine receptor 1 (D1) or dopamine receptor 2 (D2).
Beyond these two divisions, scientists knew that there were many subpopulations performing different roles, especially in the anatomically ventral (lower) regions of the striatum. However, it has been difficult to generate a consensus on how to classify these cells, in part because prior studies focused on specific subregions, meaning that overarching principles of striatal cellular organization were lacking.
To overcome that challenge, the researchers worked closely with brain banks in the United States and Canada to collect postmortem striatal samples representing diverse anatomical regions.
Then, they used three different techniques to analyze the samples, including single-cell RNA sequencing — a method that can measure RNA molecules within individual cells to reveal which genes are being expressed. Two additional techniques — multiplexed fluorescent in situ hybridization and spatial transcriptomics — allowed the researchers to identify spatial principles of organization within the tissue.
Using these techniques, the researchers were able to identify 31 different subpopulations of neurons, including nine types of medium spiny neurons. Among their medium spiny neuron types are two “outlier” populations that appear to play important roles in schizophrenia, substance use disorder, and depression.
One of those populations, known as D1 outliers, showed high expression of genes involved in addiction and substance use disorder, especially genes related to opioid response. Another population, called D2 outliers, showed high expression of genes that respond to antidepressants. And, both populations appeared to respond strongly to clozapine, an antipsychotic drug used to treat schizophrenia.
Clozapine is among the most effective antipsychotics available, but it’s not widely used in the United States because it can cause a fatal blood disorder in a small percentage of patients. Now that researchers know which cells the drug acts on, they may be able to design more targeted therapeutics to overcome psychosis, but without the harmful side effects, Heiman says.
Huntington’s vulnerability
Another key finding of the paper helps to shed light on why the dorsal (upper) part of the striatum is more vulnerable to Huntington’s disease. The disease is caused by an inherited version of the huntingtin gene that carries too many repetitive DNA segments, called CAG repeats.
The researchers found that dorsal populations of medium spiny neurons express higher levels of the genes MSH2 and MSH3, which play a role in increasing the number of CAG repeats found in the huntingtin gene. As more of those repeats accumulate, the mutated version of the huntingtin protein becomes more harmful to cells.
The researchers also found that a rare population of medium spiny neurons that forms island-like structures in the ventral striatum was more resistant to the accumulation of CAG repeats. Further study of this class of cells might help researchers learn how to induce other medium spiny neurons to become more resistant to the disease, Heiman says.
“Looking at the genes that these neurons express or don’t express might give us some clues as to how to make other medium spiny neurons resilient like them,” she says.
Insights into substance use disorders
The researchers also compared their findings from human tissue samples to samples from mice and found several differences, especially in the expression of genes related to drug response and substance use disorders. One such gene, which encodes the mu opioid receptor (OPRM1), is highly expressed in the human D1 outlier population, but not in the corresponding population of neurons in mice.
This means that standard mouse models may not fully capture the biology of opioid responses, and that engineering mice to express this receptor in a similar manner to humans could make those models significantly more accurate.
“Some of the diversity we’re seeing in the human ventral striatum is species-specific and has implications for modeling substance use disorder in rodents,” Heiman says. “Now that we understand better the species differences, we can use the rodent models for specific questions that apply for conserved genes, but we could also think about humanizing some models.”
The researchers hope that this map, built from tissue contributions by brain donors and their families, and assembled across disciplines and institutions, will provide an important starting point for researchers pursuing new treatments for some of the most difficult-to-treat brain disorders.
The research was funded, in part, by the National Institutes of Health, the G. Harold and Leila Y. Mathers Charitable Foundation, the Freedom Together Foundation, the Natalia Mental Health Foundation, the Biswas Family Foundation, and the Milken Institute.
MIT Quantum Initiative launches postdoctoral fellowship programThe Institute welcomes its first cohort of QMIT Fellows this fall to advance interdisciplinary quantum research.The MIT Quantum Initiative (QMIT) has launched a new postdoctoral fellowship program to accelerate interdisciplinary quantum research and develop the next generation of scientific leaders working at the frontiers of quantum science and technology.
Supported by a grant from the Gordon and Betty Moore Foundation, the program reflects QMIT’s vision of expanding the boundaries of quantum science by encouraging researchers to connect quantum approaches with other disciplines and emerging applications.
As opportunities in quantum research expand, investing in outstanding early-career researchers has never been more important. These fellowships are designed to help cultivate the next generation of quantum leaders, providing the resources and collaborative environment needed to advance transformative research at MIT.
“Quantum science and technology is in a period of extraordinary opportunity, opening new pathways to solving problems across computation, materials, sensing, and communication. Programs like this help MIT attract outstanding researchers whose ideas will shape the future of the field,” says Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science.
Launched in December 2025 as an MIT strategic initiative, QMIT brings together researchers from across the Institute to accelerate quantum discovery and apply quantum advances to some of society’s most consequential scientific, technological, industrial, and national security challenges.
“Quantum science is becoming increasingly interdisciplinary,” says Danna Freedman, the Frederick George Keyes Professor of Chemistry and faculty director of QMIT. “Some of the most exciting breakthroughs will come from researchers who combine deep expertise in quantum with new perspectives from other fields. This fellowship is designed to create exactly those kinds of opportunities.”
The QMIT Fellowship is intentionally designed to foster an interdisciplinary research community. Eligible applicants are outstanding quantum researchers working in a range of fields across physics, chemistry and materials science, and fundamental aspects of biological and Earth sciences. The program specifically seeks researchers whose work combines deep expertise in quantum science with a willingness to explore new intellectual frontiers.
One example of the interdisciplinary vision behind the program is the possibility of applying quantum systems to better understand biological processes, bringing together expertise in atomic physics, quantum algorithms, and biology. The fellows will be embedded across the research areas that define QMIT, including quantum computing, quantum sensing and precision measurement, quantum materials, quantum simulation, and quantum networks. Their research may also explore emerging interdisciplinary approaches that combine artificial intelligence and quantum science.
Fellows supported through the program will join MIT’s extensive quantum ecosystem, working alongside researchers across the Institute, including those affiliated with the Research Laboratory of Electronics, MIT Lincoln Laboratory, the Department of Physics, the Department of Electrical Engineering and Computer Science, the MIT-Harvard Center for Ultracold Atoms, and numerous interdisciplinary research centers and laboratories.
Beyond supporting individual research projects, the fellowship program is intended to strengthen the broader quantum community at MIT by fostering collaboration, mentorship, and intellectual exchange across disciplines.
“Quantum research, in the next few years and across a wide range of domains, is going to make the impossible possible,” says Ian Waitz, MIT’s vice president for research and the head of QMIT. “The QMIT fellowship program is an investment in outstanding postdoctoral scholars who will help bring tremendous new quantum capabilities to unforeseen, creative, and transformative applications in science and technology.”
The inaugural QMIT Fellows will begin their appointments during the 2026 academic year. QMIT expects to open applications for a new cohort in fall 2026 as it continues building a community of researchers working across disciplines to advance the future of quantum science.
Study: Peptides can form well-defined structures in harsh, Venus-like conditionsNew research offers support for the possibility that complex biological molecules could exist in the highly acidic environment of Venus’s cloud layer.When exploring solar system bodies for signs of past or present life, scientists have mainly focused on planets that have (or had) a liquid surface similar to Earth’s. However, mounting evidence suggests that the ingredients for life may exist in a very different environment: the highly acidic clouds that blanket Venus.
Those clouds are made up of about 98 percent sulfuric acid, which scientists had believed to be too acidic for complex biological molecules to survive. But in a new study, MIT researchers have shown that short peptides can not only remain stable in these extremely acidic conditions, they can also fold into shapes that may allow them to have biological functions.
“If peptides find their way to that cloud layer of concentrated sulfuric acid, they will stay and be stably preserved in that cloud of droplets. And once these macromolecules have a defined three-dimensional structure, they can potentially have a function,” says Mei Hong, an MIT professor of chemistry and one of the senior authors of the new study.
The findings suggest that scientists should not rule out planets that don’t resemble Earth in their search for life, says Sara Seager, the Class of 1941 Professor of Planetary Sciences in the Department of Earth, Atmospheric and Planetary Sciences and a professor in the departments of Physics and of Aeronautics and Astronautics.
“We really don’t know the full extent of what planet archetypes are out there. We’re seeking exoplanets that might be a true Earth twin, but what if they’re all Venuses? Our findings definitely open up a whole range of possibilities,” says Seager, another senior author of the study. She will be joining the University of Toronto faculty in September.
Janusz Petkowski, a research assistant professor at Wroclaw University of Science and Technology, is also a senior author of the paper, which appears this week in the Proceedings of the National Academy of Sciences. Jia Yi Zhang, an MIT graduate student, is the paper’s lead author, and former MIT postdoc Aurelio Dregni is also an author.
Surviving harsh conditions
While Venus’s surface is too hot to be hospitable to life, its cloud layer, which extends from 30 to 40 miles above the planet’s surface, features milder temperatures suitable for life. The clouds are made from droplets of sulfuric acid, which can dissolve metals and destroys most biological molecules on Earth.
Meteorites that contain peptide building blocks regularly enter Venus’s atmosphere, raising the possibility that those peptides could serve as building blocks for simple life forms — if they could survive the clouds’ corrosive environment.
In 2020, Seager’s lab began a series of studies looking at whether different types of biological molecules could persist under those highly acidic conditions. In their initial experiments, working with MIT’s Department of Chemistry Instrumentation Facility (DCIF), they used nuclear magnetic resonance (NMR) spectroscopy — which measures the magnetic properties of atomic nuclei within molecules — to analyze the structures of a variety of molecules in a solution of nearly pure sulfuric acid.
Those studies showed that nucleic acids, the building blocks of DNA, could remain intact under highly acidic conditions, as could lipids and amino acids. The next step was to figure out if peptides — short strings of amino acids — could persist, and more importantly, whether they could then fold into shapes that might give them biological functions.
For that challenging task, researchers at DCIF suggested that Seager join forces with Hong, an NMR expert who has an advanced 800-megahertz solution NMR spectrometer in her lab.
To their surprise, the researchers found that the peptides they studied remained stable for many weeks. They believe this is a result of the lack of water in such highly acidic solutions. At 98 percent sulfuric acid, there are very few water molecules, which means that hydrolysis, the chemical reaction that breaks peptide bonds in acid, can’t happen.
“Without water, an acid that you would consider a harsh solvent suddenly is not as menacing as one might think,” Hong says.
After confirming that the peptides remained intact, the researchers began to explore their structures. One of the peptides that the researchers analyzed, a molecule known as HHQ, is a synthetic seven-amino-acid peptide that Hong had previously studied for its role in forming catalytic amyloid fibrils.
In water, this peptide forms flat beta sheets that eventually form long fibrils. However, in concentrated sulfuric acid, the researchers found that it takes on an entirely different shape — a loop shaped like the Greek letter omega. Such so-called omega loops are occasionally found in some naturally occurring proteins, where they form links between other structural motifs such as sheets or helices.
The other two peptides that the researchers analyzed were a longer variation of HHQ, called HHQ13, and a completely different peptide called K7, which contains seven amino acids. These peptides also formed omega loops in sulfuric acid.
The researchers believe that molecules of sulfuric acid act as a scaffold for the loops, sliding into the center of each loop and holding it in that shape.
“What hadn’t been known is that peptides can survive so well and have specific three-dimensional shapes in an acidic environment,” Hong says.
Structure and function
In naturally occurring proteins in aqueous solution, omega loops are thought to play a role in protein folding and molecular recognition. Whether they could have other biological functions is not known. However, the fact that peptides can form well-defined, folded structures in acidic environments is an important step in showing that peptides may be able to perform biological functions in such environments.
“Life needs to have specially shaped proteins so that they have a specific target they can latch onto and perform their function. Before this, people thought that peptides couldn’t survive in sulfuric acid, so showing peptides are not only stable, but also fold, is a really big deal,” says Seager, who is leading the Morning Star Missions to Venus.
Adriaan Bax, chief of the Section on Biophysical NMR at the Laboratory of Chemical Physics at the National Institute of Diabetes and Digestive and Kidney Diseases, described the results as “important and unexpected.”
“The observation that these peptides retain a substantial degree of conformational order in concentrated sulfuric acid raises the prospect that folded oligopeptide/protein structures can exist in such environments, potentially supporting the possibility of life in atmospheric conditions that are very different from Earth,” says Bax, who was not involved in the research.
Seager now hopes to pursue additional studies of a molecule called peptide nucleic acid (PNA) — an artificially synthesized molecule that is similar to DNA but with the sugar-phosphate backbone replaced by a peptide backbone. Her lab has previously shown that this molecule, which doesn’t naturally exist on Earth but could offer a potential alternative to DNA, is stable as a single strand in highly acidic environments. She now hopes to study the stability of double-stranded PNA.
The researchers also hope to analyze longer peptides to see if they also take on omega loop shapes, or other structures, in highly concentrated sulfuric acid.
The research was funded by the Alfred P. Sloan Foundation, the NOMIS Foundation, and the National Institutes of Health.
Playing against climate riskClimate scientist Sai Ravela is using games to help coastal communities find creative adaptations to climate change.Sai Ravela, principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS), works with a team of researchers, local partners, and community collaborators to develop game-based computer models to help local communities find solutions to their unique geographical and environmental challenges.
Ravela came to MIT as a postdoc in 2002. Prior to that, he had been working on robotics and computer vision, but he was excited by the idea of studying the climate system and wanted to work in the field of sustainability. “Suddenly, overnight, I became a climate person,” Ravela says.
His project, funded by a 2025 Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) India Grant, explores how agricultural decision-making occurs under climate stress. Using localized climate projections and a participatory approach, the project aims to help communities discover ways to improve their collective agricultural resilience.
EAPS postdoc Anamitra Saha is a key contributor on the grant, working with Ravela and local collaborators to combine downscaled climate modeling, participatory decision-making, and community-based adaptation planning. Other team members include Myisha Ahmad (Carthago Consultancy), Jayanta Basu (University of Calcutta), Anusree Ghosh (Bangladesh Open University), Showmitra Sarkar (Khulna University of Engineering and Technology), and Bivuti Sikder (Dhaka University).
In a process known as downscaling, researchers take large-scale climate projections and turn them into highly detailed local projections. From these hazard maps, Ravela and Saha can estimate the risk of extreme weather phenomena such as flooding, drought, heat waves, and salinity-related stress.
“We kind of simulate what the outcome could be in that region,” Ravela explains. “Would it improve agricultural productivity? Would it reduce agricultural productivity? Would it change certain land use patterns? Would the land be less livable, more livable?”
The team combines surveys, scientific models, and local knowledge to build an impact graph that allows them to explore what might happen to a region during simulated weather events.
Although Ravela knew hazard maps could be useful, he was troubled by how rarely they reached the people whose lives were most affected by the risks they described. “We had clients like insurance companies,” he says. “But I never saw it reach people in a way that made a difference in their lives. And that really bothered me.”
To address this gap, he began thinking about how to help communities engage with hazard maps directly and take part in the decision-making process. In conversations that informed the game’s development, Ravela heard people whose livelihoods are vulnerable to climate events voice immediate concerns about what would happen if a future season failed: “If I don’t plant next season — if I can’t — what would I do?” Ravela wanted to help people think instead about possible choices, different paths, and their respective risks.
When he asked himself what circumstances allow someone to think about risk, the answer began to take shape. “Well, roll a die. Toss a coin,” he thought. “And where do you do these things? In a game.”
How it works
The process the collaborating team developed takes place in three stages. The first is a “snakes and ladders” game, played with physical game pieces and tokens. The second is a mixed game that still uses the gameboard, but a computer generates events and manages portfolios, allowing the system to calculate risk percentages. Once players become comfortable with the mixed game, the final stage, developed by Ravela, abandons the board game and moves fully into a more detailed computer simulation that can be played on a cellphone app.
“We tried this in different stages in three places,” says Ravela. Two villages, Bally Island and Joygopalpur, are in India's Sundarbans region. The third is a village in Bangladesh just across the border. In each location, the work depends on collaboration with local residents, community organizers, and regional partners who help shape the game around local land, water, livelihood, and governance conditions. During development, informal community-engagement sessions helped the team refine and adapt the game. Those interactions also led to intriguing observations that are now helping the team formulate hypotheses for future formal research.
The three villages lie in a coastal region that faces numerous extreme weather events threatening water availability and agricultural productivity. As riverbeds rise from sediment accumulation over time and the land sinks from groundwater extraction, saltwater can more easily intrude into groundwater aquifers, while freshwater drainage, recharge, and flushing become increasingly difficult, intensifying waterlogging and drought.
“There’s a vicious cycle that’s happening with salinization of the soil,” Ravela explains.
One visible result is that Boro rice leaves now often begin browning far too early in the season, as salinity and water stress damage crops before they can mature. This cycle occurs in many coastal communities, suggesting to Ravela that the outcomes of the J-WAFS project could have applications around the world.
That broader potential comes from what the game is able to reveal. Instead of treating potential interventions — such as embankments, canals, recharge, crops, fisheries, and energy — as separate choices, the simulation lets players see how each intervention affects the coupled system of land, water, salinity, and livelihoods. When players test different options, simply raising embankments often proves less effective than expected, because it does not break the underlying cycle that causes the land to flood.
More-integrated strategies — combining mangrove restoration, canal excavation, groundwater recharge, diversified agriculture and fisheries, better water management, and merging solar panels into farming with agrivoltaics or aquavoltaics — can generate better long-term returns while also making the landscape more resilient.
The game also creates space to consider dramatic alternatives to embankment-based protection, including seasonal migration, livelihood shifts, and other difficult choices. These possibilities can be explored safely inside the game, even when they would be almost unimaginable to raise in real life. In this way, difficult questions that might otherwise be avoided can be explored, rather than ignored. And if the game reveals that a difficult choice could lead to better long-term outcomes, that result is not a prescription, but a basis for informed conversation between the community, government, and other decision-makers.
Competition or cooperation?
To make the game effective at developing strategies, Ravela’s team had to understand how many people should play at one time. Too few players may not generate enough diversity of ideas, while too many can slow the process significantly. During game development, groups of roughly ten to twelve people seemed especially workable: large enough to support active interaction, but small enough for practical discussion and learning.
“Once it crosses a dozen people,” Ravela explains, “it becomes very, very viable as a way to solve problems.”
The games have sparked interest and generated new strategies. People are often excited by the prospect of playing, and repeated play reveals different kinds of expertise. Some participants become especially engaged strategy-explorers; others contribute through discussion, critique, memory, and local knowledge. Together, the process helps identify players who are especially adept at thinking across different dimensions of the problem.
Ravela emphasizes the social aspect of the games as central to their efficacy. “Even though the game is on a phone,” he says, “players are within each other’s reach.” An emcee or facilitator encourages players to engage with one another by asking them to explain their gameplay, discuss their reasoning, and learn from one another’s choices.
While competition is not an explicit feature of the game, there can be zero-sum outcomes. One household’s decision about land, water, drainage, or energy may improve its own outcome while making conditions worse for others. Initially, players may aim for individual success. As they explore longer simulated time horizons, they often shift toward cooperative strategies.
After each game, the research team and local facilitators lead an educational session where people can learn from each other’s strategies. At first, players often attempt to copy the previous winner’s gameplay — usually, making as much money as possible and saving it in case of disaster. But some disasters are too large for one person to handle alone.
“That strategy is only optimal up to a certain horizon,” Ravela explains, “because when everyone replicates that strategy, the community doesn’t necessarily thrive.”
As players recognize this, they begin to evolve collective modes of behavior, such as creating a common insurance pool where everyone contributes money to a disaster relief fund. Through multiple iterations of the game, players often appeared to converge on cooperative solutions.
“The community in this way, playing a game against nature, simulated nature, comes upon solutions that work for them,” says Ravela. “We would love to formally explore this in the future,” Ravela adds.
Why the game works
Ravela’s team sees three advantages to game-based decision-making. First, the game brings new perspectives to the table that formal decision-making often misses. Many communities have strong hierarchies that can discourage women or less powerful community members from participating openly. The game allows people to offer insight without necessarily violating cultural norms. One recurring impression was that women — often responsible for managing family affairs — diversified their portfolios earlier, while men more often concentrated on a single livelihood strategy. The observation was striking enough that the team hopes to test and quantify it formally in future studies.
Second, in the game, all players begin on a level playing field, regardless of status, gender, or wealth. “It democratizes the process,” explains Ravela. In the simulation, a wealthy, influential community figure has no intrinsic advantage over a seamstress. The game reduces natural biases by giving everyone’s ideas a chance to be tested under the same conditions.
Third, because the game is a simulation, people can explore choices that might be too risky, too expensive, or too socially difficult to consider in real life. People may not want to discuss a large aquifer management system, a new land-use arrangement, or a difficult livelihood transition if the real-world implications feel too overwhelming. But inside the game, they can test possibilities without immediate consequence. “So, what, you lose? You start again,” says Ravela.
This is where the game becomes more than a communication tool. It turns uncertainty into a shared decision space. Players can test interventions, observe trade-offs, compare outcomes, and discover strategies before real disasters force those choices upon them. The game shifts the conversation from avoiding risk to reasoning about it, and from fatalistic thinking to collective agency.
Ravela and his collaborators also see the games as a way to address roadblocks in policy implementation by allowing community members to own the solutions they discover. Traditionally, donors may give money to a nongovernmental organization (NGO) that has proposed a project, and the NGO then distributes resources in the community. But it is not always obvious what has actually been implemented, or whether the community has had meaningful ownership of the decision. “In seeking solutions to problems, often the difficulty is developing the policy that provides metrics for the effectiveness of those solutions,” Ravela says. “Games enable people to quickly see the policy space, rather than approaching problems only reactively.”
When people test policies in the game, see how they work, and revise them through repeated play and refinement, they can begin to propose those policies themselves. The result is not simply a technical recommendation from outside experts, but a community-informed basis for action.
What's next?
The broader project, developed with collaborators and community partners in India and Bangladesh, has attracted interest in Bangladesh and Thailand, where similar game-based coastal agricultural resilience projects are being explored. Some customization is necessary to adjust the game to local conditions, but the simulations are highly adaptable. Between 75 and 80 percent of the game can remain the same across locations, while the rest can be tuned to local geography, livelihoods, hazards, and governance structures. Although each place brings its own challenges, “the way land and water and people interact is very similar,” says Ravela.
Building on insights from these game-development and informal community-engagement sessions, Ravela hopes the project can eventually expand to other locations, including members of the Association of Southeast Asian Nations and some places in Latin America. But he emphasizes the importance of establishing longitudinal outcomes before scaling. “The critical question is, does it answer real problems?” he says.
Future formal research will test these emerging hypotheses prospectively and longitudinally. The resulting evidence will help determine whether, where, and how to scale the approach.
If computationally assisted decision-making proves useful over time, the impact could spread far beyond the initial development locations. But the work is not only about finding an optimal solution. It is also about helping people work with one another. As Ravela puts it, “the process really is about helping the people work with each other as much as it is about finding an optimal solution, because part of finding the optimal solution is finding people to work with each other.”
How an MIT graduate student helped a team of young scientists test their experiment at CERNPhysics PhD student Manu Srivastava helped high school students in India develop a test that could contribute to one of the world’s largest neutrino experiments.This past spring, MIT physics graduate student Manu Srivastava opened an email from a group of high school students in India he had never met.
They were hoping to enter Beamline for Schools, an international competition that gives secondary school students the chance to design and carry out experiments using particle accelerator beams. And they were looking for a mentor.
Srivastava, who studies quantum gravity as a PhD student in the MIT Center for Theoretical Physics – a Leinweber Institute, with Professor Hong Liu, gets other requests to mentor students, often through companies charging families for access to scientists or students at prestigious universities. He usually declines, but this message came directly from the students.
“I've also cold-emailed a lot in my early career, and it usually never works,” he says. “But this email seemed very genuine. They wanted to do something nice and they just needed some guidance.”
Many months and many more emails and calls later, the students secured a place with Srivastava to attend CERN, in Geneva, where they spent two weeks turning their proposed idea into a real experiment.
Finding an experiment worth doing
Calling themselves Team attoPION, the students are one of five teams selected in the 13th annual Beamline for Schools competition from a record 712 teams representing 89 countries and more than 4,500 students. The six high schoolers met through a combination of science competitions and mutual friends, and attend four schools in four cities across India.
When they first met with Srivastava, the students already had several experimental ideas. His role, he says, was to help determine which directions were practical and scientifically interesting.
They settled on measuring pion charge exchange. Pions are short-lived subatomic particles that can carry positive, negative, or neutral charge. In the process the students want to study, a positively charged pion interacts with a neutron in a target material, producing a neutral pion and a positively charged proton. The team wants to characterize how often that reaction occurs.
Srivastava suspected such a measurement could have relevance to the Deep Underground Neutrino Experiment, or DUNE, a major international experiment designed to study neutrinos.
Dave Newbold, a co-spokesperson for DUNE, says understanding how pions interact with matter helps researchers quantify uncertainties in DUNE’s measurements. In particular, pion interactions can affect estimates of a neutrino’s flavor and energy, which researchers need to measure accurately to determine whether they have observed something new.
And although Beamline for Schools has an educational mission, Newbold says the students aren't simply reproducing a classroom demonstration. “The proposal is real experimental particle physics!” he notes.
If successful, Newbold believes the work could improve scientists' understanding of this particular interaction and potentially lead to a publishable result. Similar “test beam” experiments remain important tools in particle physics: DUNE's detector designs were themselves demonstrated using the (albeit much larger) ProtoDUNE experiments at CERN.
“This [proposal] stands out because of the work the students have put into motivating their measurement, and demonstrating that the experiment is feasible,” Newbold says. “It's certainly at a level far above anything I was thinking about at high school.”
Learning to navigate uncertainty
At CERN, the students worked hands-on with detectors and data-acquisition systems, collected and analyze data, and attended talks by CERN scientists.
In advance of the trip, the team worked with Berare Göktürk, one of the support scientists for Beamline for Schools. In their preparation sessions for the experiment, they realized that the charge-exchange process they hope to observe is extremely rare, forcing them to think through how they might reliably detect it.
With just a few months months to prepare and only 12 days of test-beam time, Göktürk cautioned that producing a result useful to a much larger experiment would be an ambitious outcome.
“We prepare in the best way possible, but we also stay humble and we are aware of the limitations we have,” she says. Her priority is for the students to “understand the journey of a scientist” as they encounter technical problems and work together to solve them.
For Srivastava, mentoring an experiment has also taken him well outside his own specialty. A theoretical physicist, he credits MIT's culture with encouraging him to follow questions beyond the boundaries of his research, including by attending seminars, colloquia, and research meetings across physics.
The experience has been personally meaningful for Srivastava, who grew up in India and sees the mentorship as a way to encourage young people there to pursue fundamental science.
“I didn't even know what CERN was in high school,” he says. “But these students, they are just that good. They deserve all the credit.”
Gage Coon: An Earth scientist exploring the power of microbesThe PhD student’s research on how microorganisms digest compounds in their environment could enable advances in wastewater treatment.Growing up in Waverly, Tennessee, Gage Coon spent much of his childhood outside. His family had everything from chickens to horses and even an emu named Big Bird. Coon and his cousins would explore the woods surrounding their home, and his father, a mechanic, taught him how to build and repair things around the house. His mother, a secretary at the local high school’s vocational school who loves gardening and birdwatching, encouraged him to experience as much of the world around him as he could.
That hands-on upbringing, which taught Coon to appreciate the natural world and the processes that sustain it, continues to influence how he approaches science today.
Now entering his third year as a PhD student in MIT’s Department of Earth, Atmospheric and Planetary Sciences, Coon studies some of the smallest organisms on Earth: microbes. His research focuses on how microorganisms cycle carbon and sulfur through the environment and how to leverage those processes to help address climate change. Though he studies organisms too small to see with the naked eye, the experimental nature of his work — whether in the lab or on a research vessel in the open ocean — is especially satisfying.
“I think I enjoy that physicality of seeing what I’m working with, seeing its change, and being able to touch it,” Coon says.
Coon did not initially set out to study microbiology. His interest in science began with chemistry. A high school chemistry teacher and a summer program introduced him to the subject. But later, at the University of Tennessee at Knoxville, he joined a lab focused on microbial biogeochemistry and was delighted to find a field that brought together the different areas that interested him: chemistry, the environment, and the larger climate processes shaping our Earth.
The transition from rural Tennessee to Cambridge, Massachusetts, and MIT has been a significant one. As a first-generation student, he did not learn about PhD programs until several years into college.
Once he discovered academic research, however, Coon was drawn to the possibility of spending his career learning.
“I discovered this world of academia, and so I was really excited when I learned about it,” he says. “I was like, ‘Oh my god, constant learning. That is exactly what I want to do forever.’”
Coon began studying the microbes that drive carbon and sulfur cycling in marine sediments as an undergraduate, eventually joining research cruises to investigate these processes firsthand.
His first research cruise, in 2022 after his second year of college, took him to the Atlantic continental slope to study methane seeps and how microbes prevent this methane from escaping to our atmosphere. For Coon, experiencing the ocean up close changed the way he understood the microscopic organisms he was studying.
“It is very powerful seeing yourself in the middle of the ocean, with a whole other world of complex life beneath you,” he says.
At MIT, working with his advisor Tanja Bosak, a professor of geobiology, Coon has continued studying microbial carbon and sulfur cycling, but with a greater emphasis on the applications. One of his major projects explores how microbes could be used to reduce methane emissions from wastewater treatment.
When wastewater is treated, microbes break down organic material in large tanks called anaerobic digesters. One of the final products of this process is the powerful greenhouse gas methane. However, Coon and his colleagues found a way to change what the microbes produce by adding gypsum, a waste product that is created from fertilizer manufacturing
The system uses the added gypsum to turn the methane into carbonate, which can be used to make cement, agriculture, and pharmaceuticals. The process also produces elemental sulfur, necessary for global fertilizer production, which is currently sources from oil and gas refinement. The approach effectively turns two waste products, sewage and waste gypsum, into useful materials while reducing greenhouse gas emissions.
For Coon, the possibility of creating a system that is both environmentally beneficial and economically useful is central to the project. Now that the laboratory experiments have ended, the researchers are looking toward conducting pilot-scale testing. Coon and his advisors have been communicating with companies interested in adapting the system to larger facilities, and hope the technology can eventually move beyond the laboratory.
“If enough small places start doing their pilot-scale studies, then hopefully you could convince some place like Boston or another big city to do this and really make a contribution to our global goal to decrease emissions on the gigaton scale,” he says.
The wastewater project is only one part of Coon’s PhD research. He also studies geological processes that could produce molecular hydrogen, a potential carbon-free energy source. His work examines how iron-rich rocks break down and generate hydrogen underground. He is continuing his thesis work by focusing on microbial competition for acetate, and what this means for global methane emissions from coastal wetlands. This work could improve future climate predictions and support engineered mitigation efforts to decrease emissions from these wetlands.
Across these projects, Coon is interested in the connection between the microscopic and the massive. But Coon’s PhD has also given him an opportunity to think about science beyond his own research.
One of the parts of graduate school he has enjoyed most is mentoring younger researchers. He has worked with a handful of students through MIT’s Undergraduate Research Opportunities Program and from Tufts University, teaching them laboratory techniques and experimental geobiology.
Outside the lab, Coon maintains some of the same connection to the natural world that characterized his childhood in Tennessee. He spends time hiking to explore local geology, playing bluegrass guitar, and speed-solving Rubik’s Cubes.
Looking ahead, Coon sees himself continuing in academia, working in government, or helping to bring environmental technologies into practice.
What matters most, he says, is continuing to produce knowledge that can help people understand and potentially improve the world around them.
“I do think, no matter what,” he says, “I’ll be somewhere thinking about how microscopic life connects to the global ecosystem and carbon emissions.”
Looking beyond natural sequencesA new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.A protein’s function is determined by its structure, and structure — the way a protein folds — is determined by its sequence of amino acids, the building blocks of proteins.
Many methods for designing novel proteins, including examples that could bind to a disease-causing molecule in our cells, involve a two-step process: The structure comes first, and then a machine-learning framework generates a repertoire of sequences that could potentially adopt that structure.
In nature, many different amino acid sequences can fold into the same structure. At the same time, one amino acid sequence can potentially adopt different structures depending on the protein’s flexibility or a functional trigger. Therefore, when researchers use artificial intelligence to design new proteins, the challenge is to guide AI to “see” that there are many potentially useful answers — that many sequences can adopt the same fold
“For years, the field has measured success by asking whether a model can reproduce the protein sequence that evolution happened to select — our work shows that this isn’t the best metric for protein design,” says Amy E. Keating, Department of Biology head, Jay A. Stein (1968) Professor of Biology, professor of biological engineering, and senior author of a paper recently published in PNAS.
PottsMPNN, a new machine-learning framework developed in the Department of Biology, incorporates the physical principles that govern protein structure and stability, improving sequence generation and the ability to predict how mutations will affect a protein’s stability. In other words, the model has a better understanding of the sequence-energy landscape, meaning the relationship between the identity of each amino acid and the stability of the protein.
Adding this framework to a protein design pipeline will allow researchers to design structurally feasible proteins with sequences that don’t resemble those of any native protein.
“If we’re thinking about a completely novel, designed structure, there would be no native sequence to compare it to,” says graduate student and lead author Foster Birnbaum. “What we actually care about is how likely the generated sequences are to fold into the desired structures, how well the model understands the sequence-energy landscape, and how well it can predict the effect of mutations on the stability of the protein.”
Beyond the noise
In the same way that AI has recently powered some dramatic social changes, so too has machine learning impacted the pace and breadth of fundamental biological research. Only recently has it become possible to reliably use a computational model to generate a protein structure or sequence. Perhaps the most widely used model today, however, was released in 2022.
“For a field that’s moving as fast as machine learning in biology, that model has not been surpassed — we’ve been trying to understand why that is, and what it is about that model that makes it so useful,” Birnbaum says.
Birnbaum was first interested in strategic applications of something researchers call “noise,” or adding variations to a protein structure during training. Noise decreases the tendency of the model to overly mimic native sequences, increasing the diversity of structures for which it’s able to generate sequences.
PottsMPNN also uses a pairwise distribution to capture interactions between amino acids. The ability to account for the physical interactions between all 20 possible sequence options at a pair of positions in the protein is a key reason that PottsMPNN more accurately models the sequence-energy landscape than other methods.
Finally, Birnbaum says, they introduced sets of evolutionarily related sequences into training the PottsMPNN framework to teach the model how different sequences can adopt the same folded structure.
Birnbaum acknowledges that in trying to shift away from adhering to native sequences, incorporating evolutionary information is, in some ways, still a reliance on them. But PottsMPNN succeeded in demonstrating that as the model depends less and less on native sequences, structural compatibility and energy prediction, including for novel proteins, improve.
Protein design in the age of AI
“Once we can design any protein we want, that enables us to do a potentially scary amount of biological engineering,” Birnbaum says. “It’s a difficult task, but I’m really optimistic about this century’s progress in biology.”
Birnbaum hopes that the model could be further improved and fine-tuned for a specific task, which has in the past led to better predictions, for example, on the outcome or consequence of a particular mutation.
Ultimately, according to Keating, “Our methods move the field toward designing useful new-to-nature proteins for diverse applications while providing a stronger foundation for future advances.”
AI helps design new materials that work in the real worldThe “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.Today, anyone with a large enough artificial intelligence model can generate millions of new material designs in minutes. Unfortunately, that hasn’t led to a huge leap in the number of new materials being used to improve the performance of products like computer chips and rockets.
One reason for the translation gap is that current models don’t reliably factor in the chemical stability of the materials they generate, and unstable materials aren’t very useful in the real world. That forces industries to allocate huge computational budgets to screening out all the unstable materials they generate, in some cases leaving behind a tiny fraction of usable options.
Now, MIT researchers have developed a framework that can be applied at the beginning of the materials generation process to vastly improve the stability rate while achieving targeted material properties. It works by ensuring every design satisfies certain key rules of chemistry relating to the electrons around the materials’ atoms before the expensive generation step begins. The researchers call their approach “crystal generator with valence-constrained design, or CrysVCD.
In a paper published today in Nature Computational Science, the researchers show how CrysVCD allowed several commonly used material models to meet those valence shell rules more often, and used it to achieve high lattice-dynamics stability — a stringent stability test — in nearly 70 percent of computational material generations. They also showed the approach could support the creation of materials with specific desired properties, like high thermal conductivity or high dielectric constant, which is important for computer chips and data centers.
A hint of how the researchers envision people using their system is in the name.
“If material-generating models are like DVDs, we are like the DVD player,” says associate professor of nuclear science and engineering Mingda Li. “You can plug this into any kind of model, not only existing diffusion models but also future models, where people can’t generate enough stable materials, and it can improve stability.”
Joining Li on the paper are Mouyang Cheng SM ’26 and Weiliang Luo, MIT doctoral students in materials science and engineering and chemistry, respectively; Hao Tang PhD ’26, a recent graduate in materials science and engineering; Bowen Yu, a senior undergraduate in physics; Yongqiang Cheng, a staff scientist at the Oak Ridge National Laboratory; Weiwei Xie, an associate professor at Michigan State University; Ju Li, MIT’s Carl Richard Soderberg Professor in Power Engineering; and Heather Kulik, MIT’s Lammot du Pont Professor of Chemical Engineering.
More efficient materials
Computational approaches to materials design have been around for decades, but recent advances in artificial intelligence have increased excitement about their potential. Of particular interest are models that can start with a desired material property and work backward to deliver a material that achieves that goal.
Some of those models use an AI technique known as diffusion, which is commonly used to generate images, while others use large language models like the one powering ChatGPT and Claude, but both approaches struggle to ensure their material generations achieve chemical stability or follow fundamental principles about how chemicals interact and behave.
The solution has been to add another layer of computing on top of the generative process to filter out unstable materials.
“It’s becoming easy to generate the material structure,” Cheng says. “But the validation process, especially the part where you test the stability, has a huge computational cost. It’s something like 90 percent of the computational cost for creating usable materials, and it can take weeks or months.”
Big companies with huge computing budgets can afford to run those processes, but many small companies and research labs can’t, potentially limiting innovation in the field.
“In academia, where we have fewer resources, I think we can still achieve strong performance with smarter designs and other approaches,” Kulik explains. “Generating a model and then down-selecting for stability is inefficient. There’s a high computational cost. But if we put a language model in the beginning of the process to constrain the generation, you can significantly enhance the ratio of stable materials generated.”
The new study involved MIT researchers affiliated with the departments of Materials Science and Engineering, Chemistry, Chemical Engineering, Physics, and Nuclear Science and Engineering. Together the researchers combined AI diffusion models with a language model. In the first stage of their process, the language model produces chemically valid formulas. In the second stage, the diffusion model uses that formula to generate the corresponding atomic structure of the crystal material in coordination with the underlying material generation model.
“Diffusion for typical material generation is a slow process — you can think of it like 1,000 steps to create one material,” Luo says.
“In contrast, when our model is used in the beginning, you can think of it like five steps. It allows you to screen out the unstable materials to generate higher quality materials. And it works with any models generating materials,” Tang adds.
The researchers showed their approach created more stable materials an order of magnitude more efficiently than approaches that rely on screening materials after they’re generated. When fine-tuned on stability metrics, their approach produced crystalline materials that achieved 68 percent mechanical stability and 85 percent metastability, which measures if a material stays in a stable state when undisturbed.
The researchers then used their approach to generate material candidates with high thermal conductivity and easy polarization in an electric field.
“These are materials useful for the semiconductor industry and high thermal conductivity materials relevant to data center cooling,” Ju Li says. “In principle, you could also use this to create other properties, but thermal conductivity has become really important for cooling data centers. There’s been a huge increase in energy use in that industry, and 30 percent of that energy goes to cooling. The industry needs materials with high thermal conductivity to more efficiently remove the heat.”
Democratizing material design
The new approach doesn’t work with every kind of material — it works best with solid structures with highly ordered internal arrangements. Still, the approach could be used to generate stable new crystalline materials with a host of important properties.
“We are not just generating stable materials, we’re also prioritizing performance,” Cheng says. “Any time you have two goals, achieving those goals with anything over 50 percent is hard in this field. In the past, people might have a goal for specific properties and not stability, or vice-versa, and get a single-digit percentage of materials that fit their goal.”
Ultimately the approach will enable more researchers to develop novel materials for a range of next-generation applications.
“This will save huge computation costs and time by removing downstream selection requirements,” Li says. “That will help not only large efforts that generate hundreds of millions of materials, but also smaller research groups with targeted applications.”
The work was supported, in part, by the U.S. Department of Energy, a Mathworks Engineering Fellowship, the National Science Foundation, and the U.S. Defense Threat Reduction Agency.
Cells use a little-known molecule to protect themselves from iron overloadThis discovery points toward new combination strategies against cancer, and may explain the iron buildup seen in disorders such as early-onset Parkinson’s disease.Iron is essential. Our cells need it to produce energy, carry oxygen throughout the body, and power countless chemical reactions that sustain life. But this metal has a dark side. When too much of it is left free inside cells, it can trigger destructive reactions that break down DNA, proteins, and even cell membranes.
Now, MIT associate professor of biology and Whitehead Institute for Biomedical Research member Ankur Jain; MIT assistant professor of biology and Koch Institute for Integrative Cancer Research member Whitney Henry; and Pushkal Sharma PhD '26 have discovered that cells rely on an unexpected protector against this threat: small molecules called polyamines.
The researchers’ detailed findings, published Aug. 14 in the journal Cell, reveal that polyamines act like storage lockers for iron, safely holding the metal in a non-reactive state until cells need it.
These findings solve a decades-old mystery about why cells maintain such extraordinarily high levels of polyamines and uncover a previously unknown defense mechanism that protects cells from toxic iron overload.
This work could also help scientists develop better cancer treatments, by allowing iron overload to trigger cancer cell death. It could also offer new clues about diseases like early-onset Parkinson’s disease, in which mutations affect polyamine levels within neurons.
The Jain Lab studies RNA, the intermediary between DNA and the tiny molecular machines called proteins that perform most of the essential tasks inside cells. The lab is particularly interested in how RNA folds, misfolds, and sometimes clumps inside cells.
Jain and Sharma first began studying polyamines because these molecules bind to RNA and help shape its structure. However, they suspected that polyamines must be playing other roles inside cells: they’re among the most abundant small molecules within cells, present at levels comparable to ATP, the molecule cells use as their energy currency.
“We’ve known that without polyamines, cells stop growing and dividing,” Jain says. “But their best-known function only requires a small fraction of the polyamine levels cells actually have.”
To uncover polyamines’ hidden function inside cells, the researchers used a large-scale genetic approach that allows them to screen the entire genome at once, rather than testing genes one-by-one, in order to find out which cellular processes are impacted when polyamine levels are changed within cells.
The screen revealed that when cells have reduced levels of polyamines, a protein called GPX4 becomes essential for survival. GPX4 is known to prevent harmful chemical reactions that damage the fatty molecules that make up cell membranes.
The team also found that cells with lower polyamine levels have higher amounts of another protein that acts as an iron sponge and keeps the metal in a mineralized form. Together, these findings led the researchers to hypothesize that polyamines might be helping keep iron in a safe, non-reactive state within cells.
To test this idea, they developed a new fluorescent sensor that would allow them to measure chemically reactive iron inside living cells. The new sensor causes living cells to glow based on the amount of chemically reactive iron they contain, allowing researchers to track any changes under a microscope in real-time.
The team paired the new iron sensor with another sensor they had previously developed that measures polyamine levels within cells. By employing them simultaneously, they observed a striking pattern: As polyamine levels dropped within cells, the amount of chemically reactive iron went up, offering new evidence that polyamines play a key role in preventing toxic iron build up inside cells.
Beyond answering a fundamental biological question, these findings could have implications for cancer treatment. Cancer cells often rely on high polyamine levels to support their rapid growth and division. However, cancer drugs designed to lower polyamine levels to stop cell division have had limited success.
“We saw that when polyamine levels fall, cells rely on GPX4 to protect themselves from iron toxicity,” says Sharma, who is also the first author of the study. “This could mean that combining drugs that lower polyamine levels with those that block GPX4 might be more effective for killing cancer cells than targeting either pathway alone.”
The discovery may also have implications beyond cancer. Mutations in genes that help move polyamines around cells are linked to a rare form of early-onset Parkinson’s disease, and scientists have long observed unusually high levels of iron in the brains of Parkinson’s patients.
While it is still unclear whether excess iron directly contributes to neuron death in Parkinson’s, the discovery that polyamines help buffer reactive iron inside cells offers a possible explanation for this link and opens new directions for future investigation.
In addition, the researchers expect the new iron sensor to be a valuable tool for other scientists. By allowing them to track chemically reactive iron inside living cells, it could power new discoveries in aging, cancer, and neurodegeneration.
“There are a lot of promising future directions for this work,” Jain says. “It’s exciting to think about how these tools and findings could help answer further questions about disease pathways and potentially help design better therapies.”
This work is supported by grants from the National Institutes of Health, Bumpus Foundation, and Pew Charitable Trusts. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Brain circuit keeps tabs on what just happened to aid judgment of what’s happening nowTo keep track of what’s going on in front of it, the brain relies not only on what it sees, but also a comparison with what it just saw. A new study pinpoints the circuit that provides that service.A brain must constantly cope with the highly variable, fast-paced nature of the world when trying to judge what’s going on around it. On one hand, it has to be open to whatever new sensory information may come its way, but on the other hand, just to keep up, it has to try to leverage prior experience to make predictions about what seems to be happening.
In a new study published in Science, MIT neuroscientists identify a circuit that links a sensory decision-making region with one that advises it on how much sensory information just changed.
“This circuit organizes a comparison between what has just happened versus what is happening now in the sensory world in a manner that can be used to act,” says study senior author Mriganka Sur, Newton Professor in The Picower Institute for Learning and Memory and MIT’s Department of Brain and Cognitive Sciences.
Study lead author Ning Leow Phd ’23, a former graduate student in Sur’s lab who is now a postdoc at A*STAR in Singapore, says the study in mice sheds light on closely analogous circuitry in humans, in which an area of the prefrontal cortex (the anterior cingulate cortex, or ACC) makes sensory decisions. The new study shows it bases those decisions on advice about immediate past history from an area of the thalamus called the pulvinar (though in mice, it’s called the lateral posterior thalamus, or LP).
“The brain does not evaluate each new event from scratch,” Leow says. “The pulvinar has traditionally been studied for its role in attention and filtering visual information, but we found that it was also important for comparing present information with the immediate past and highlighting meaningful changes to influence whether we maintain or update a decision.”
As part of Sur’s long-standing interest in how the brain’s cortex integrates sensory perception and learning to produce behavior, Leow and Sur began comprehensively mapping the copious inputs to the LP-ACC circuit, culminating in a paper in 2022. It was clear from that study how the circuit would seem well-positioned to help focus attention, which is what it was known for at the time.
But in thinking more deeply about what focused attention is for, and about how these well-connected regions seemed to sit at the center of not only attention but also perception and action, Sur and Leow hypothesized that they might also have a hand in guiding decisions based on sensory information. The new study presents multiple lines of evidence that it does.
The findings not only shed light on a fundamental function of the brain, Sur says, but could also be applicable to studies of autism, in which many patients show significant differences in the predictions they make about the sensory world. Often, this manifests as difficulty filtering out stimuli that neurotypical people are able to regard as recurring, and therefore mundane.
Which way?
To conduct the study, the researchers trained lab mice to play a video game in which dots on a screen would drift around, but at least some would move together in the same direction (left or right). In each trial, the mice had to discern that trend. From one trial to the next, then, the sensory cue could vary not only by the direction of movement, but also by how what proportion of dots were participating. For instance, on one trial maybe 64 percent of the dots would move left and on the next trial maybe 16 percent of the dots would move right. In this way, the researchers could measure a whole continuum of differences from one trial to the next.
Meanwhile, as mice played the game, the scientists used a two-photon microscope to record the activity of the LP-ACC circuit and the response of neurons in the ACC. In some experiments, they used a technique called optogenetics to artificially activate the circuit.
By tracking how mice performed the task trial after trial, the researchers were able to see that the mice indeed factored in not only what they were seeing in the moment, but also what they had just seen previously. For instance, when mice guessed right, they were very likely to repeat their guess if the new cue was very similar to the prior one, and very unlikely to if the cue was very different. But if they guessed wrong, then the opposite was true: They wouldn’t repeat that decision if the cue was similar to the last, but would if it looked very different.
Looking in the brain
Of course, behavioral observations only indicated that the mice indeed compared new cues to prior ones. Determining whether that was indeed because of the LP-ACC circuit required the researchers to use optogenetics to perturb it (by stimulating extra activity in the LP’s inputs into the ACC). For instance, optogenetic perturbation of the circuit in the left brain hemisphere made mice less likely to guess that dots were moving right, and perturbation in the right hemisphere made mice more likely to guess dots were moving to the right. But in both cases, the extent of these deviations from normal behavior was directly proportional to the difference between the current cue and the previous one. In other words, perturbing the circuit disrupted how mice used recent sensory history when evaluating new evidence, Leow says.
“That showed the pathway is causally involved in the comparison process that influences how current evidence is interpreted, rather than merely carrying the information,” Leow says.
Moreover, using the microscope imaging (which visualizes calcium levels in neurons, a close proxy of the electrical activity), the researchers extensively analyzed the activity patterns of the LP input into the ACC and how ACC neurons reacted to that input.
“The main takeaway is that the LP and ACC were performing different jobs,” Leow says. “The pulvinar doesn’t appear to be making the decision itself. Instead, it sends that history-referenced sensory comparison to the frontal cortex. The ACC then transforms that information into the neural activity that predicts the animal’s final choice.”
Essentially, the pulvinar advises the ACC on the degree of change so that the frontal cortex can consider whether it’s time to change a guess. After all, if a mouse is guessing right and little is changing, why not keep on trucking? But if there’s a big change, then it might make sense for the mouse to re-evaluate what it’s thinking.
It turns out, the brain has this dedicated circuit for doing so.
In addition to Leow and Sur, the paper’s other authors are Arundhati Natesan, Alexandria Barlowe, Sofie Ährlund-Richter, Tianyu (Cindy) Luo, and Mehrdad Jazayeri.
The National Institutes of Health, a MURI grant, the Simons Foundation Autism Research Initiative, A*STAR, and the Freedom Together Foundation funded the research.
Meteorite dust holds records of magnetism that may have helped form the sunThe discovery likely represents the earliest known evidence of a magnetic field in the infant solar system.Around 4.6 billion years ago, the solar system was little more than a giant ball of gas and dust. Over the next few million years, this “solar nebula” underwent a huge transformation, flattening into a disk of matter that then condensed to form the central sun and orbiting planets.
Scientists have assumed that the early solar system was shaped mainly through gravity. But a new study finds that magnetism also likely played a role.
MIT scientists have discovered records of ancient magnetism in the oldest samples of meteorites known today. The team analyzed microscopic grains embedded in a meteorite that was discovered in Antarctica in 2008. These grains, called calcium-aluminum-rich inclusions, or CAIs, originally formed during the solar system’s first 200,000 years, making the samples the oldest known solar system material.
The findings suggest that a magnetic field existed very early on, during the time of the solar nebula. The researchers estimate that this nebular magnetic field was stronger than Earth’s magnetic field today, and likely played a significant role in pulling together primordial matter to form the early sun.
“This transition, from a spherical cloud to a protoplanetary disk, is one of the most significant events in all of solar system history,” says Benjamin Weiss, the Robert R. Shrock Professor of Earth and Planetary Sciences at MIT. “It has long been theorized that gravity caused this, but our measurements show magnetism likely played a role.”
Weiss and his colleagues report their discovery in a paper appearing this week in the Proceedings of the National Academy of Sciences. The study’s MIT co-authors are first author Cauê Borlina PhD ’22, Elias Mansbach PhD ’24, and Nilanjan Chatterjee, along with Xue-Ning Bai of Tsinghua University, Po-Yen Tung and Richard Harrison of Cambridge University, François Tissot of Caltech, and Kevin McKeegan of the University of California at Los Angeles.
Spinning grains
Magnetic fields are generated by matter that is electrically charged and moving around. In the very early solar system, the collapsing cloud of gas and dust could have whipped up a plasma of charged particles. As these charges spun through the developing disk, they could have produced and sustained a magnetic field.
If this were the case, Weiss and his colleagues reasoned that such early magnetism would have affected material in the disk. As this material condensed, tiny magnetic minerals would have locked in the strength of the magnetic field, preserving its original intensity over billions of years. If these minerals somehow made it to Earth for scientists to measure, their “remanent magnetization” would be evidence that a magnetic field indeed existed and could have played a role in shaping the solar system.
In fact, the team has previously discovered evidence of a magnetic field, as early as 2 million years into the solar system’s formation. At that time, scientists believe that the sun was already in place, and that the planets were just starting to come together. Thus, the magnetic field Weiss measured likely played a part in the formation of the early planets.
“Nowadays people don’t debate whether magnetism is present when planets are forming. But the debate is around the very early solar system, before planets are forming, when there’s just a disk,” says Borlina, who led the new study as an MIT graduate student and is now an assistant professor at Purdue University. “That’s where the debate still resides, and that’s where we’re operating now.”
Magnetic records
For their new study, the team investigated whether a magnetic field could have existed even earlier in the solar system, when the sun was first coming together. They analyzed samples of DOM 08006, a meteorite that was discovered in 2008 in Dominion Range, a mountain range located along the East Antarctic Ice Sheet. Since it was first recovered, the meteorite has been studied extensively.
DOM 08006 is one of the most primitive meteorites discovered, and it contains mineral grains that date back to the earliest stages of solar system development, possibly even before the sun was formed. Surprisingly, the meteorite has managed to keep its original composition and minerals.
“Other meteorites went through many different processes over this 4.5 billion year history,” Weiss says. “They were formed in the solar nebula, then added to bodies with water, then got destroyed, moved to the asteroid belt, and then landed here. But somehow, DOM has experienced less alteration than any other meteorite.”
If the early solar system did harbor a magnetic field, records of that field could still be in place in some of DOM’s ancient mineral grains, including CAIs.
“We know they are the oldest things we have of the early solar system,” Borlina says. “But CAI’s are very complex and are not all the same, even within a 1-millimeter piece of the meteorite. So we have to carefully identify what types they are.”
From small samples of the parent meteorite, the team isolated tiny grains and identified a handful of CAIs that contained inherently magnetic minerals such as iron. They then put the grains through a series of tests to measure any magnetism they still carry.
The team identified traces of a magnetic field in the ancient grains. Based on their measurements, they estimate that a magnetic field, of about 150 to 600 microteslas, existed in the early solar system. This field strength is about three to 12 times greater than the Earth’s magnetic field today.
“We think these kinds of magnetic fields were helping to move gas from the protoplanetary disk, in toward this central star, the sun,” Borlina says. “Gravity is also playing a role. But we are now showing that, if you want to fully understand how the sun and planets formed, you should include magnetic fields in the ingredients that make them.”
This research was supported, in part, by NASA.
Language skills stay strong in older adults, even while other cognitive abilities declineMIT researchers find that activity in the brain’s language network is nearly identical in young and old people.As people age, many cognitive functions tend to decline. Brain scanning studies have revealed corresponding changes in the function of a brain network that is involved in many of these cognitive functions, including working memory and problem-solving.
When it comes to language skills, however, the picture is different. Unless impaired by a stroke or dementia, most older people retain their language skills and may even improve them as they steadily gain vocabulary throughout their lives.
A new brain imaging study from an MIT-Boston University collaboration now reveals the neural activity underlying this observation. The researchers found that in older adults, activity of the language processing network is nearly identical to that seen in the brains of younger adults during language tasks.
In contrast, the researchers found that activation patterns in the multiple demand network, a brain system involved in executive control tasks such as decision-making, were very different in older and younger adults.
“In the language network, we couldn’t find any differences between older and younger groups. In contrast, the executive system showed decline across almost all of the measures. The network synchronization declined in older adults, the extent of activation was reduced, and the magnitude of activation was reduced as well,” says Anne Billot, one of the lead authors of the new study, who carried out this work while doing her PhD at BU and is now a postdoc at Harvard University.
The findings suggest that parts of the brain that are specialized for specific functions, such as language processing, may be more resilient to aging than the multiple demand network, a more general-purpose network that has greater flexibility in its function, the researchers say.
Former MIT research assistant Niharika Jhingan is also a lead author of the study, which appears today in Nature Communications. Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences and member of MIT’s McGovern Institute for Brain Research, and Swathi Kiran, the James and Cecilia Tse Ying Professor in Neurorehabilitation at BU, are the paper’s senior co-authors.
The resilience of language
To study the effects of aging on the brain, the researchers looked at two groups of people, ages 17-39 and 41-80. Based on previous studies, they expected that the multiple demand network, which includes several regions in the frontal and parietal lobes of the brain, would look different in the brains of older people.
“It’s well known that executive functions, such as attention, working memory, and cognitive control, tend to decline with age. And it’s also known that in opposition to that, language skills typically tend to remain quite stable or even improve with age,” Billot says. “These two types of functions really go in opposite directions in healthy aging. In terms of behavior, that’s quite well-established, and we wanted to see if that was also the case in the brain.”
In previous studies of the multiple demand network, scientists have found that as people age network activity becomes less synchronized. Some neuroscientists have hypothesized that this may also happen in the language network, but studies haven’t found definitive evidence for this.
The MIT researchers were able to look at both networks by designing tasks that elicit responses primarily in either the language network or the multiple demand network. This allowed them to identify, for each participant, the brain areas that belong to each network.
During a spatial memory task — remembering the location of squares in a grid — the researchers confirmed that the multiple demand network showed altered activity in older adults. Compared to the younger subjects, their networks were smaller and less well-synchronized, and the overall activation level was weaker.
To identify the language network, the researchers had participants listen to stories and read sentences. They found that in both groups, brain activity in response to language showed similar levels and spatial distribution across the network.
They also found that younger and older subjects showed similar brain responses when they encountered an unfamiliar word or an unusual grammatical construction.
“We have previously used similar kinds of materials to show that young adults show strong sensitivity to these points of linguistic difficulty: activity in the language areas goes up. Here we found that in older adults, you also see this sensitivity, which suggests that there’s nothing fundamentally different about how they process language,” Fedorenko says.
A language boost
The researchers also showed that in older people, the language network did not show any signs of becoming less synchronized. Additionally, the network did not show signs that it was blurring together with the multiple demand network, as some neuroscientists have hypothesized might happen.
While this study did not evaluate language ability, other studies have shown that not only do language skills not decline with age, for some people, their language processing improves in older age. This might be because vocabulary and reading skill can continually grow over time, the researchers say.
“Vocabulary keeps increasing as long as people have been measuring, which makes sense. People get exposed to more and more language, and older people sometimes start reading more, so they get an extra boost — it’s like a large language model trained on increasingly more data,” Fedorenko says.
Given these findings, one possible generalization is that parts of the brain that are specialized for particular functions such as language may be less susceptible to age-related decline than the multiple demand network. That network is unique in its ability to give the human brain the flexibility to learn new skills and adapt to new situations.
“The multiple demand network is a different system in the sense that it’s not accumulating knowledge over time. It’s more like a flexible resource that you can deploy in all sorts of ways. And somehow that’s the thing that is more vulnerable to aging,” Fedorenko says. “Why it’s so vulnerable — that is a very good question.”
The research was funded by the National Institute on Deafness and Other Communication Disorders, as well as MIT’s McGovern Institute, Simons Center for the Social Brain, Poitras Center for Psychiatric Disorders Research, and Quest for Intelligence.
Paving the way for greener ammonia productionNew MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.Ammonia is one of the most important chemicals produced in the world, ranking second only to sulfuric acid in the total volume produced each year. It is used mostly to make fertilizer, which is essential to feeding the world’s population. Yet its production accounts for up to 2 percent of the world’s energy consumption and about 1.5 percent of greenhouse gas emissions, so the search has been underway for ways to produce ammonia more sustainably.
The traditional way of making ammonia, in use for more than a century and accounting for the vast majority of production, is the Haber-Bosch process, which relies on fossil fuels to provide the needed heat. Hydrogen used in the process is also largely produced from fossil fuels.
There is another way, using electrochemistry instead of heat and pressure, but so far this method has not been anywhere near economically competitive at the scales needed.
Now, researchers at MIT have developed a way to predict which materials could be most promising as catalysts in electrochemical ammonia production. Catalysts help drive chemical reactions, and their properties determine how efficiently those reactions proceed. Rather than using trial and error to test each possible combination out of the millions of possible alloys — which can take years — the new approach could greatly speed up the search for materials that could make this low-emissions method competitive with the Haber-Bosch process.
“Our approach identifies the key physical properties that drive catalytic activity in ammonia production,” says Bilge Yildiz, the Breen M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering (DMSE). The results can guide the search for new and more effective catalyst compounds.
The open-access findings were published Aug. 11 in the Royal Society of Chemistry journal EES Catalysis, in a paper by Yildiz and doctoral students Constantine Athanitis of DMSE and Filip Grajkowski of the Department of Chemistry.
The challenge of greener ammonia
As the world’s population grows, Athanitis says, “we’re just going to need more and more food, and the only reason why we’re able to sustain so many people is because of fertilizer.” But more than 90 percent of the ammonia needed for fertilizer is still made by that energy-intensive Haber-Bosch process, which “has been hyper-optimized since it first came out more than a century ago,” he says.
“If we’re trying to keep in line with society’s sustainability and energy targets and climate change targets, we really need to come up with another alternative,” he explains. The world currently uses about 200 million metric tons of ammonia each year, “so ideally we want to be able to find a way to produce the same amount of ammonia, or even more, but in a more energy-efficient way and also with lower CO2 emissions,” he says.
Using electricity to produce ammonia is not a new idea. “It’s really just the electrochemical reaction between proton-electron pairs and nitrogen gas. And these technologies exist,” he says. The approach uses the same basic principles as electrolyzers, which use electricity to drive chemical reactions in devices.
But while the process works, it’s not efficient enough for industrial-scale production. “Production rates and yields are still too low,” Athanitis says. “Even though a technology might be better for the world or for the climate, companies and capitalism won’t really allow it unless it’s cost competitive.”
How to make it more competitive? The key ingredient in the electrochemical process is a metallic catalyst, whose properties govern the reaction that takes place on its surface. “If we can somehow find a catalyst that reduces the energy needed and is more selective for ammonia production,” Athanitis says, “then we could essentially hit the jackpot.” A more selective catalyst would produce more ammonia while reducing unwanted side reactions.
Finding better catalysts
But finding that ideal catalyst is not simply a matter of identifying one perfect material. Different materials can improve different parts of the reaction, and researchers are seeking combinations that can make ammonia production efficient, affordable, and practical at large scale.
“Metal nitride compounds make an ideal material system for this reaction and for identifying the electronic, chemical, and structural properties that determine reactivity in nitrogen reduction and ammonia electrosynthesis,” Yildiz says.
Transition metals could form promising nitride alloys for this purpose, and historically, “materials research has been pretty much trial and error,” Athanitis says.
The usual process is to take some existing material and “tweak it in some way,” he says. “It’s all somewhat guided by scientific and chemical intuition.”
Now, increasingly, computational tools are being used to model the physical interactions and predict outcomes. A method called density functional theory uses quantum mechanics to simulate the properties and behavior of materials, allowing researchers to predict how different atomic arrangements may perform before making them in the lab. Rather than searching randomly through every possible alloy combination, Yildiz says, “we first assessed what microscopic properties of the material make them tick for nitrogen reduction.”
For ammonia-producing catalysts, “we’re looking at transition metal nitrides,” Athanitis says, because they have been found to be effective in these electrochemical nitrogen reactions. They are especially effective because “the nitrogen inherent to the catalyst itself becomes part of the reaction.”
This produces a series of chemical steps in which one step provides part of the energy needed to drive the next, reducing the amount of input energy needed. This helps solve one of the major bottlenecks in the nitrogen reduction reaction: the high energy required to break the strong bonds in nitrogen molecules, he says.
But the process is far from perfect, Athanitis says. It is “still limited by certain steps throughout the reaction pathway, including nitrogen dissociation and hydrogen transfer.” The study attempted to identify those bottlenecks and, with the help of machine learning, determine which alloys of these metals might overcome them.
With that understanding, “it can give us insights and open up potential strategies for how we can tune these materials to create next-generation better nitride catalysts,” Athanitis says.
Pushing past theory
The approach is “exciting work” that could help develop a foundation for designing new catalysts for ammonia production, says Dane Morgan, a professor of engineering at the University of Wisconsin who was not involved in this study.
“This work helps clarify how fundamental electronic properties of a material relate to its role as a catalyst in making ammonia,” Morgan says. “Such understanding can help guide researchers in designing new catalysts, both through better qualitative understanding and by accelerating computational screening.”
So far, the study is purely theoretical: The researchers have used computer models to identify promising alloys, but those materials still need to be made and tested. Morgan notes that “translating these calculations into practical catalysts will require many additional steps, so meaningful real-world impact is likely still some distance away.”
The next step will be to build a working reaction cell, a laboratory device that uses the catalyst to produce ammonia and test its performance under real operating conditions. “For this to really make an impact in society, we need to bring it to the experimental lab,” Athanitis says.
“There have always been pushes at the frontiers of what’s possible,” he adds. “We like to think we’ve pushed the boundary of candidate materials here beyond what was thought of before, and hopefully we’re almost there. But even if we’re not almost there, we’re still pushing in the right direction.”
How the Toxoplasma parasite adapts to crowded conditions in host cellsA new study by Whitehead Institute researchers identifies how the widespread parasite Toxoplasma gondii survives in crowded environments in infected cells, which helps it persist in long-lasting brain cysts.Toxoplasma gondii, or Toxoplasma, is a parasite that infects hundreds of millions of people around the world. Although cases are often mild, it can cause severe symptoms in in people with weakened immune systems, and in developing fetuses. It can also persist for years by forming long-lived cysts in tissues, allowing infection to become chronic.
During chronic infection, hundreds of Toxoplasma parasites can pack into a tissue cyst inside a brain or muscle cell. That crowded life carries a cost: Nutrients become harder to obtain, waste accumulates, and energy-producing reactions can become damaging.
How Toxoplasma reshapes its metabolism to keep growing under such strained conditions has been unclear. But a new study from the lab of MIT Associate Professor Sebastian Lourido, a member of the Whitehead Institute for Biomedical Research, identifies a parasite-specific protein that helps coordinate this response. The protein, named TgPRO, allows Toxoplasma to manage oxidative stress — the buildup of reactive oxygen molecules that can damage cells — by controlling genes involved in energy production and iron use.
The open-access findings, published on Aug. 11 in the journal Cell, reveal the first dedicated regulator of metabolic gene expression identified in apicomplexans, the group of parasites that includes Toxoplasma and the organisms that cause malaria. The study, led by co-first authors and Lourido lab affiliates Christopher Giuliano PhD ’26, a recent graduate student in biology, and Chinmay Kalluraya, a current graduate student in biology, reveals a previously unknown way that parasites regulate metabolism. The findings also point to a possible therapeutic strategy: Inhibiting pathways controlled by TgPRO could make Toxoplasma more vulnerable to antiparasitic drugs that induce oxidative stress, though this approach remains to be tested.
One gene at a time
To discover the genes that support Toxoplasma’s ability to live in crowded cells, the researchers used a genome-wide CRISPR screen to compare Toxoplasma growing at low and high densities. The screen tests the effects of turning off genes one by one at both population densities in order to determine which genes are essential specifically in crowded conditions. It highlighted pathways that make or recycle NAD and NADP, molecules important for energy production and defending against oxidative damage. It also pointed to TgPRO, a previously unstudied protein that was especially important when parasites became crowded.
“A genome-wide screen was a powerful way to ask how crowding affects parasite fitness,” Kalluraya says. “TgPRO emerged as very important at high density. Because almost nothing was known about it, we wanted to understand what it was doing.”
Parasites lacking functional TgPRO accumulated more reactive oxygen molecules and struggled to compete at high density. Experiments showed that the loss of TgPRO disrupted the mitochondrion — the structure that supplies much of a cell’s energy — and changed how parasites processed glucose and other nutrients. Providing additional iron or restoring an important chemical balance inside the mitochondrion improved parasite growth, connecting TgPRO’s effects to iron-dependent energy metabolism.
The team then traced the response to a molecular mechanism. TgPRO is an RNA-binding protein, meaning it attaches to the molecular messages (RNAs) that cells use to make proteins. The researchers found that it binds and stabilizes a select set of messages involved in nutrient use, mitochondrial activity, and the assembly of iron-sulfur clusters, small structures that many enzymes need to function. The experiments connected the original observation — that some parasites faltered only when crowded — to a precise interaction between a regulatory protein and its RNA targets.
“One of the really nice elements of the story is our ability to connect it all the way through — from the original observation and genome-wide screen to the metabolic consequences and the direct interaction between TgPRO and its target RNAs,” Lourido says.
The researchers found that lowering oxygen levels also reduced oxidative stress and partially restored the growth of parasites without TgPRO. Toxoplasma is commonly grown in laboratories at atmospheric oxygen levels, which are considerably higher than those found in most animal tissues. The result suggests that oxygen conditions can strongly shape parasite metabolism, and the researchers caution others studying Toxoplasma to take this into consideration.
Connecting TgPRO to chronic infection
After testing the role of TgPRO in artificially crowded settings, the team also tested whether TgPRO matters during chronic infection, when Toxoplasma forms cysts in the brain. Mice infected with parasites lacking functional TgPRO developed smaller brain cysts, suggesting TgPRO supports parasite growth in the naturally dense environment of a chronic-stage cyst.
“The chronic stage is still somewhat elusive,” Giuliano says. “Showing that TgPRO affects cyst growth suggests that these same metabolic changes are needed in the brain and gives us clues about how the parasites persist there for months or years.”
TgPRO bears little resemblance to the proteins that regulate similar metabolic programs in mammals, yeast, and bacteria, yet it controls many of the same kinds of genes that these organisms adjust when cells face oxidative stress or changing nutrient conditions. This is an example of convergent evolution: Distantly related organisms evolved different molecular machinery to solve a similar biological problem.
That convergence suggests that coordinating these metabolic pathways may be a fundamental requirement for cells adapting to stress.
Altogether, the study establishes a new paradigm for how apicomplexan parasites regulate their metabolism, and advances the foundation for investigating how Toxoplasma persists inside its hosts.
This work was supported by National Institutes of Health grants and by a Burroughs Wellcome Fund grant awarded to S.L. M.A.S is funded by an Early Career Award from the Wellcome Trust. C.R.H. is funded by a Sir Henry Dale Fellowship from the Wellcome Trust and the Royal Society. J.K. is supported through funding by a generous donor advised by CARIGEST SA and acquired by D.S.-F.
Tackling rare genetic disorders with patient-focused scienceShannon Knight, a brain and cognitive sciences PhD candidate and McGovern Institute researcher, focuses on developing a novel gene therapy.Shannon Knight attributes her interest in neuroscience to an experience she had in high school. She and her sister attended a medical day for students at the nearby University of Illinois Chicago. As they were on their way out of the event, they walked past a room with a person holding a brain.
“We stopped and backpedaled into the room, and I was so fascinated,” says Knight. “I was able to hold the brain of a patient who had passed away of Alzheimer’s. The brain holds so much emotion, decision-making — everything. I realized that this man’s entire memory was in my hands, and something clicked for me. I decided that I really wanted to learn much more about this organ.”
Now in her sixth year of doctoral studies at MIT’s McGovern Institute for Brain Research, Knight is working on developing a novel gene therapy for childhood-onset epilepsy, specifically SYNGAP1 haploinsufficiency. This rare genetic disorder is caused by a mutation in the SYNGAP1 gene, rendering one of the two copies of the gene nonfunctional.
SYNGAP1 is important for brain development and neuronal communication, and the disorder leads to seizures in children starting as young as 4 months old. Other symptoms include intellectual disabilities, challenges with eating and sleeping, and difficulties with movement.
While there are currently methods to address the symptoms of the disorder, such as anti-seizure medications and dietary restrictions, as the child ages, the seizures often become resistant to medications. Knight is working to develop a therapeutic using CRISPR, a biotechnology tool used to edit genes. This therapeutic aims to address the root cause of this medication resistance by focusing on the gene itself.
“The idea of leading science with empathy is something that I feel very deeply,” she says. “I hope my efforts in the lab work toward the benefit of the people affected, rather than just for the benefit of my own science.”
Researching gene therapies
Knight’s interest in the brain flourished as a neuroscience major at Bowdoin College, working with Professor Hadley Horch. While she had originally planned to be pre-med, Knight ultimately decided that it wasn’t the best fit. She enjoyed the research she did as part of her honors thesis, exploring the regeneration of neurons in the auditory system of crickets, and decided that she wanted to pursue more research in molecular neuroscience, as well as genetics.
After graduating, Knight worked at the Perrimon Lab at Harvard University, where she first learned about CRISPR, applying it in a fruit fly model. She worked for two years in the lab, co-authoring a few papers and applying to graduate schools.
She ultimately landed in the lab of MIT Professor Guoping Feng, studying the potential of utilizing CRISPR to develop a gene therapy treatment for Phelan-McDermid Syndrome, a rare genetic disorder caused by a deletion or mutation on the 22nd chromosome.
“Many of our graduate students are passionate about making a positive impact to society through cutting-edge research, and Shannon is a perfect example,” says Feng, the James W. and Patricia T. Poitras Professor and associate director at the McGovern Institute. “She is developing gene therapy technologies that have the potential to help many kids with devastating neurodevelopmental disorders.”
Building off of the gene therapy research around Phelan-McDermid syndrome, which is now in clinical trials in patients, Knight is now in the early phases of testing gene therapy for SYNGAP1 disorder. The goal is to go through the same process for the SYNGAP1 gene therapy as for the Phelan-McDermid gene therapy — eventually obtaining U.S. Food and Drug Administration approval and beginning clinical trials.
The testing of the gene therapy on mice with a version of SYNGAP1 disorder has shown promising preliminary results in alleviating seizures and all of the behavioral phenotypes. This work is being accelerated by the Rare Brain Disorders Nexus, an MIT initiative that launched in the fall of 2025.
“Something I think about a lot is the idea of who ‘deserves’ the attention of a gene therapy. I feel that, regardless of how rare a genetic disorder might be, it still deserves care,” says Knight. “SYNGAP1 disorder is extremely rare, only impacting one to four out of every 10,000 children. I am very fortunate to be at an institution like MIT that has so many labs and brilliant researchers working on diseases that impact large portions of society, and it was really important to me to spend my PhD years helping a small, often unseen population. Although I don’t actually have a relationship with someone who has SYNGAP1 disorder, I know so many people who feel invisible in systems, and it is really important to me to be able to focus on people who feel unseen and give them hope.”
Inspiring others in the lab
In addition to her passion for neuroscience and genetic research, Knight has also developed a love of teaching. She has been a teaching assistant for 9.12 (Experimental Molecular Neurobiology), leading the lab portion of the course. She has enjoyed working closely with small classes of students, introducing them to the fundamentals of neuroscience lab research.
“We walked through the process of looking at a specific protein in neurons, and talked about how you can go from cell culture all the way up to a mouse brain — and all the steps in between. It was so important to me to be able to teach the students and help them to consider all of the different types of experiments they could do,” she says. “I’ve talked to many of the students since then, and many said it was one of their favorite classes.”
Knight received the Goodwin Medal in 2025 in recognition of her commitment to excellent teaching.
“Shannon has a rare combination of scientific excellence, teaching talent, and compassion,” says Laura Frawley, senior lecturer and teaching and curriculum development specialist in the Department of Brain and Cognitive Sciences. “Students trust her because she is approachable and invested in their success, and they learn from her because she has an exceptional ability to make complex ideas accessible and engaging. Her influence extends far beyond the laboratory skills she teaches.”
Knight has also invited high school and other college students into the lab and worked with them during the summers.
“It’s so exciting to bring in kids with no previous experience in a wet lab, and watch them be so amazed by all of the things that you can do,” she says. “Experiments that might seem so routine and relatively simple to me, at this point, are so exciting for them.”
Following the completion of her PhD program, Knight plans to do postdoctoral research and would ultimately like to be a faculty member at a small liberal arts college.
“It’s amazing to see students gain confidence over time, and then seeing them progress in their careers as scientists,” she says. “That’s very rewarding for me.”
DNA shaper steers nervous system developmentA study of worms finds a protein that helps shape the structure of the genome is critical for establishing the identity of some neurons.A functional nervous system depends on the cooperation of many kinds of cells. So as developing organisms build their nervous systems, their neurons must take on different forms and functions to fulfill their designated roles. That carefully orchestrated process gives rise to thousands of different cell types in the human brain.
In the tiny worm known as C. elegans, the nervous system is far simpler, comprising a mere 118 classes of neurons.
At MIT, scientists in H. Robert Horvitz’s lab are studying the worms to learn about how nervous systems develop. Horvitz is the David H. Koch Professor of Biology at MIT, an investigator at the McGovern Institute for Brain Research at MIT, and an investigator at the Howard Hughes Medical Institute. His team has just discovered that a protein complex called cohesin, which helps shape the three-dimensional structure of the genome in both worms and humans, is critical for establishing some neurons’ identities as development unfolds.
The open-access findings, reported July 31 in the journal Science Advances, could help scientists find a way to treat a rare developmental disorder called Cornelia de Lange syndrome, which is caused by mutations that interrupt the cohesin complex.
Model organism
MIT postdoc Dongyeop Lee explains that C. elegans is a powerful model for studying neurodevelopment not just because its nervous system has been comprehensively mapped, but also because of the ease and speed with which scientists can study the function of its genes.
Because many of the worm’s genes have been retained through evolution, findings from studies of C. elegans often reveal important aspects of human biology. The current study began with worms that, because of a genetic mutation, make too many neurons of a certain type.
Adrenergic neurons, named for the kind of neurotransmitter they use to communicate with other neurons, are vital for enabling worms to respond to both their environment and their own internal state. Normally, C. elegans has just two pairs of adrenergic neurons: two RIM neurons and two RIC neurons. But the worms Lee studied had extras of both.
Takashi Hirose, a former member of the Horvitz lab, first observed this change in 2007.
Lee later continued the study and discovered that worms carrying a mutation in a gene called coh-1 have extra adrenergic neurons. The coh-1 gene encodes one part of the cohesin complex.
When Lee tested other mutations that disrupt cohesin, he found the same effect: Worms without fully functional cohesin had too many RIM neurons and too many RIC neurons.
Molecular switch
With a series of experiments designed to tease apart how cohesin impacts neurons’ identities, Lee discovered that cohesin cooperates with a gene-regulating protein called EOR-1 (known in humans as PLZF) to direct some neurons to develop into neurons that communicate with the inhibitory neurotransmitter GABA.
By reorganizing the structure of the genome, cohesin can change the way gene regulators like EOR-1 interact with DNA. Lee’s experiments showed that when either cohesin or EOR-1 couldn’t do its job, cells that should have become GABA-producing neurons become adrenergic neurons instead.
“What we found is that there are two alternative possible fates of certain neurons, and cohesin acts as a molecular switch that decides one of the possible neuronal fates,” Lee explains. “This means the structure of genomic DNA in the nucleus is important for neuronal fate determination.”
Disease connection
Lee adds that extra adrenergic neurons were not the only abnormality he observed in worms with cohesin mutations. Cohesin is important for shaping cells and tissues throughout the body. “The mutants have severe developmental defects,” Lee says. “They grow slowly. They don’t move well, and they also have defects in reproduction.”
Notably, the problems Lee saw in the worms echo aspects of Cornelia de Lange syndrome, a rare genetic disorder that impacts physical, cognitive, and behavioral development. Cornelia de Lange syndrome can be caused by mutations in cohesin genes, and Lee says that the discovery of how cohesin mutations affect worm development and behavior opens new opportunities to study the disease and search for potential therapeutic targets in C. elegans.
The Horvitz lab already has some promising leads. Taking advantage of the quick genetic screens that are possible in worms, Lee has found additional mutations that can counteract impaired cohesin, improving the health of worms with cohesin mutations. The team is now working to identify the genes where these suppressor mutations occur, so they can investigate whether they might make good therapeutic targets in humans.
Meanwhile, the team is also exploring a potential role for cohesin in shaping the fates of other neuron types, as well as searching broadly for additional molecules that work with cohesin to guide development. “We expect we have opened up a new biology,” Lee says. “This paper is just the beginning.”
Flexible brain circuits can switch between different tasksNeuroscientists have discovered circuits in the prefrontal cortex that can be repurposed to store different types of information.As we move through everyday life, our brains engage in a huge variety of cognitive tasks. For example, during a grocery run, we might have to recall the items for a recipe, remember where the clerk said the flour was located, and count out money to pay.
Scientists have long theorized that the brain contains modules, or clusters of neurons, that perform the same computation across many different types of tasks. This type of modularity could help explain why our brains are able to take on so many functions, with little difficulty.
In a new study of mice, MIT neuroscientists have found the first evidence for the existence of these flexible modules. They identified neurons in the prefrontal cortex that can be used to store either a sensory input or an action plan in working memory.
“We found that the brain doesn’t dedicate a separate group of neurons for every type of information. Instead, it uses the same populations of neurons to perform the same computation on different kinds of information, which means the same subset of neurons can hold both an action and a sensory stimulus in working memory,” says Yuma Osako, an MIT postdoc and the lead author of the new study.
The discovery supports the theory that reusable circuits allow the brain to mix and match components to generate a rich variety of behavior, the researchers say.
Mriganka Sur, the Newton Professor of Neuroscience at MIT’s Picower Institute for Learning and Memory, and Timothy Buschman PhD ’08, a professor at the Princeton Neuroscience Institute, are the senior authors of the paper, which appears today in Nature Neuroscience. MIT graduate student Greggory Heller and postdoc Sofie Ahrlund-Richter are also authors of the study.
Cognitive building blocks
Dating back to his time as a graduate student at MIT, Buschman has been interested in understanding how the brain is able to perform so many different kinds of behavior.
“One of the solutions that’s always been proposed has been this idea of compositionality — that you can take pieces of cognition that perform part of a task and reuse them in another task,” he says.
In a study published last year, Buschman’s lab at Princeton showed that when animals perform a task such as categorizing objects based on their shape or color, they assemble neural circuits that perform different pieces of the task. Just like “cognitive Legos,” these building blocks can be flexibly combined to generate new behaviors.
Osako, who joined Sur’s lab several years ago, was also interested in studying cognitive flexibility. He and Sur teamed up with Buschman to explore a related question: whether individual neural circuits can be repurposed to perform different functions.
“Our everyday life requires us to temporarily hold many different kinds of information. One big question is how the brain can represent an unlimited variability of information using only a finite number of neurons,” Osako says.
To get at that question, the researchers trained mice on a task in which they have to determine whether two sensory stimuli (high or low pitched tones) are the same, and respond accordingly.
The researchers recorded electrical impulses from the brain while the mice performed this task, focusing on the prefrontal cortex, which is involved in executive functions such as planning and decision-making, and the parietal cortex, which processes sensory information and plans movement.
After measuring electrical activity from thousands of neurons, the researchers performed computational analyses that allowed them to identify groups of neurons that encode specific pieces of information.
They focused on two time periods — the time between the first and second tone, when the animals are holding a memory of the first tone, and the time between the second tone and the point where they have to decide on an action. During that second period, the animals are holding their decision and action plan in their working memory.
Within the parietal cortex, the researchers found that neurons appeared to exclusively store memory of the tone. But in the prefrontal cortex, they identified a cluster of neurons that could switch between the two types of memory. During the first period, they stored a memory of the first tone, but during the second, they were responsible for remembering the plan of action.
Re-using these clusters for different purposes allows the animals to flexibly store different types of information, the researchers say.
“When mice do tasks that test whether memory computations can be reused, the answer is they are. There are subspaces of functional activity in the prefrontal cortex that can be the substrate of mixing and matching toward flexible cognition,” Sur says.
Computational flexibility
The new findings offer support for the idea that the same computational circuits can be used for different purposes, Buschman says.
“The main result from this study is that there’s a circuit in the brain that maintains items in working memory, and you can put either sensory or motor information into it, and flexibly reuse it depending on what your current task is,” he says. “This means you do not have to build an entire new circuit for holding information in mind every time you want to learn a new task.”
The researchers now plan to study whether inhibiting these modules during different parts of the task affects the animals’ behavior, which could offer additional evidence that the flexible modules they identified participate in a variety of functions.
The research was funded by the National Institutes of Health, a MURI Grant, the Picower Institute Innovation Fund, the Japan Society for the Promotion of Science Overseas Research Fellowships, and the Uehara Memorial Foundation Postdoctoral Fellowship.
High-speed microscopy reveals electrical activity across the brainThe new technique could help scientists learn how the entire brain works to generate decisions and emotions.Within the brain, neurons compute by generating electrical impulses. These signals travel throughout neurons, which are in turn connected in vast networks that control brain functions such as sensory perception, memory formation, and control of movement.
In an advance that could help neuroscientists map those neural networks, leading to a better understanding of how neural activity underlies behavior and other brain functions, MIT engineers have invented a new microscope that can image electrical activity in neurons distributed across the brain of an entire organism, the experimental model Danio rerio (zebrafish).
Using a microscope that they adapted for fast, high-volumetric rate imaging, the researchers were able to track electrical activity across the brain on the scale of milliseconds. This method revealed patterns of neural activity from neurons throughout the brain that were activated in response to ultraviolet light.
“All of the parts of the brain are connected together, so if you want to truly understand the brain, you have to understand how all the neurons work together as an emergent whole,” says Ed Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT; a professor of biological engineering, media arts and sciences, and brain and cognitive sciences; and a member of MIT’s McGovern Institute for Brain Research, Yang Tan Collective, and the Koch Institute for Integrative Cancer Research.
Boyden is the senior author of the study, which appears today in Nature Methods. Former J. Douglas Tan Postdoctoral Fellow Zeguan Wang PhD ’24 and former MIT research scientist Jie Zhang are the lead authors of the paper. Other authors include former MIT postdoc Panagiotis Symvoulidis, Picower Institute research scientist Wei Guo, graduate students Davy Deng and Lige Zhang, Koch Institute research scientist Adam Amsterdam, Picower Institute research scientist Takato Honda, Boston College undergraduate Steven Roche, and Matthew Wilson, the Sherman Fairchild Professor of Neuroscience at MIT and a member of the Picower Institute.
High-speed imaging
One technique often used to measure neuron activity in the brain is calcium imaging. Calcium flows into neurons after they fire an electrical impulse, so measuring calcium levels in the cells can serve as a proxy for neural activity. However, this type of imaging isn’t fast enough to capture single spikes of activity.
“Calcium imaging inherently is very slow, so you’re talking about imaging activity on the order of seconds or even minutes. Typically that is too slow for us to be able to see a lot of these high-speed neural activities,” Zhang says. “Neurons compute using electrical activity, so with voltage imaging, you can get direct observation of that.”
To enable direct imaging of voltage, researchers have developed proteins called genetically encoded voltage indicators — fluorescent proteins that can be genetically expressed in neurons. When a neuron fires an impulse, the protein fluoresces, which can be detected with a fluorescence microscope.
In previous work, researchers have used these proteins to image small populations of neurons, usually focusing on one localized part of the brain. Until now, there hasn’t been a way to image a large volume, such as the entire brain, with the millisecond-scale resolution needed to see electrical impulses from individual neurons.
To achieve that, the MIT team decided to modify a commonly used microscope known as a light sheet microscope. This type of microscope uses a sheet of laser light to illuminate a thin slice of a sample. By imaging many layers in sequence, this technique can generate 3D images of a large volume. However, with previous microscopes, the scanning of an entire volume would take too long to be able to capture neuronal impulses across the volume at single cell resolution.
“Different groups of neurons that are distributed across the brain coordinate together at millisecond timescales to generate a lot of behaviors and brain computations,” Wang says. “To understand the principles, we need the technology to observe their activity at the same time, across the whole brain, so we are not missing any important participant neurons.”
To make the imaging process fast enough to image millisecond-scale activity, the researchers increased the image acquisition speed of the microscope’s camera, and they also boosted the scanning speed of the microscope using a technique called remote refocusing.
Using this approach, the researchers showed that they could scan the entire zebrafish brain 200 times per second, or once every five milliseconds.
Mapping brain activity
To test the new microscope, the researchers engineered neurons in larval zebrafish to express a voltage indicator called Positron2-Kv. Although they had hoped that the indicator would end up in every neuron, it produced signals in neurons distributed throughout the brain, with about one quarter of the neurons exhibiting acceptable signals. This was enough, however, to observe patterns of activity across the brain. The researchers imaged the brain as the fish were resting, and they were able to observe single voltage spikes from neurons, as well as rapid bursts of spikes.
Additionally, this technique revealed patterns in how the brain is activated following a stimulus such as ultraviolet light. Immediately following the stimulus, activity was seen in the optic tectum, which receives and processes visual input from the retina. This activity propagated from one side of a part of the brain called the tectum to the other. Stimulus-independent activity also occurred in sequences across sets of neurons in the cerebellum and hindbrain.
The researchers now hope to increase the percentage of neurons that they can image across the brain, as well as the microscope’s speed and resolution. They are also working on expanding the use of this technique to other experimental models, including mice.
This approach, they say, could offer neuroscientists a new way to generate hypotheses about what happens in the brain when it engages in specific behaviors, or about how brain activity is linked to states of mind such as daydreaming.
“A big question is simply to understand how neurons work together as a network. And this might be the first time that you could do that, because you can image the voltage of neurons distributed throughout the network,” Boyden says.
The research was funded by the National Institutes of Health, the BRAIN Initiative, the Picower Institute Innovation Fund, K. Lisa Yang, Ashar Aziz, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience at MIT, the Hock E. Tan and K. Lisa Yang Center for Autism Research, the Alana Down Syndrome Center, John Doerr, Jed McCaleb, James Fickel, and the Howard Hughes Medical Institute.
MIT selected to lead new NSF materials research centerThe Materials Research Science and Engineering Center unites researchers across disciplines to develop technologies for medical imaging, sustainable metals production, and next-generation electronics.The National Science Foundation (NSF) has selected MIT to establish and lead a new Materials Research Science and Engineering Center (MRSEC) focused on materials technologies for medical imaging, sustainable metals production, and next-generation semiconductors, according to an NSF announcement released July 30.
Expected to provide $18 million in research funding over six years, the award brings together 16 research groups from nine departments across four institutions, including five MIT departments, three collaborating universities, and a teaching hospital. The award is pending MIT’s negotiation of a formal research agreement with the NSF.
The MIT Materials Research Science and Engineering Center will be directed by Associate Professor Rafael Jaramillo of the Department of Materials Science and Engineering (DMSE), with Professor Caroline Ross of DMSE serving as associate director. The center will be housed administratively within the MIT Materials Research Laboratory.
The center will have two main research thrusts. One will engineer specialized materials to advance X-ray detectors used in medical imaging, potentially leading to better cancer diagnosis, lower radiation exposure, and improved industrial and security imaging. The other will explore high-temperature sulfur-based molten materials to transform how metals and semiconductors are made, opening a path to more efficient metal production, improved access to critical materials, and new thin-film semiconductor technologies.
The expected funding will also support a new shared laboratory for testing magnetic materials and materials under extreme conditions, managed by MIT.nano. This facility will be available to academic and industry users, expanding the nationwide portfolio of NSF-supported research facilities.
“The long-term goal is for the broader materials and engineering community to see the disruptive potential of bringing researchers together across disciplines to solve complex challenges,” says Jaramillo, the Stavros V. Salapatas Career Development Professor of Materials Science and Engineering. “And that includes specifically in medical diagnostics and metals production, where entirely new things will be possible that aren’t considered possible today.”
A legacy of collaboration
The selection of MIT’s MRSEC is part of a $108 million NSF investment in six research centers that will explore a range of topics, including artificial intelligence-driven experimental laboratories and hybrid quantum materials that combine light and matter. NSF’s MRSEC program brings together interdisciplinary teams of researchers to push the boundaries of materials science and engineering and tackle complex scientific challenges.
The MIT center builds on nearly 60 years of interdisciplinary materials research at the Institute, extending a legacy that began with U.S. Department of Defense-supported laboratories in the 1960s and continued through NSF-funded centers in subsequent decades. Past MRSEC investments helped build research communities that enabled MIT centers of excellence such as the MIT Microphotonics Center and the Microsystems Technology Laboratories.
“We were inspired to continue that legacy of collaborative research in materials science,” Jaramillo says. “It’s mainly the mode of working — the mode of working in a very intentional way as a team across disciplinary boundaries and having this program that brings people together.”
MIT departments involved in the MRSEC include DMSE; Chemistry; Chemical Engineering; Earth, Atmospheric and Planetary Sciences (EAPS); and Physics. Collaborating institutions identified in the MRSEC proposal are Yale University, the University of California at Santa Barbara, and the Department of Radiology at Massachusetts General Hospital and Harvard Medical School.
The first research group will focus on re-engineering scintillators — materials that convert X-rays into visible light — at the nanoscale, with the goal of improving resolution, speed, and energy sensitivity.
“My vision for that is really Marin and JJ’s vision. So I'm basically cheerleading for them,” Jaramillo says, referring to optical materials experts Professor Marin Soljačić of Physics and Professor Juejun Hu of DMSE, who are expected to lead the effort.
The second group is closer to Jaramillo’s own research in semiconductor and advanced electronic materials. It seeks to develop a deeper understanding of high-temperature sulfur-based liquids to improve the yield and efficiency of producing critical metals such as copper. Expertise in these types of materials has become increasingly rare in U.S. academia, Jaramillo says, and one goal of the center is to rebuild that capability at MIT. “I’m very excited about that being a new intellectual center of gravity.”
Telling stories about materials
Beyond research, the center is also expected to develop outreach activities highlighting the importance of materials science in society, particularly in the Boston region, where Jaramillo said industries need more workers with backgrounds in materials processing.
“For example, our community colleges don’t offer it,” Jaramillo says. “If you were looking at a community college in Michigan, everyone would know what materials science is.”
One initiative, DISASTER! — “with all caps and an exclamation mark,” Jaramillo says — will tell stories of real-world catastrophes and the materials failures that contributed to them.
A major part of materials science over the last century has been understanding why things fail, Jaramillo says. “It’s also a tremendous foot in the door for introducing the field. Because frankly, ‘if it bleeds, it leads.’ If you have giant disasters, then suddenly people are like, ‘Why did the bridge fail?’”
The program will encourage MIT undergraduates to research and tell these stories, illustrating how forensic materials science has helped prevent future failures.
Among the examples Jaramillo cited are the rivets used to assemble the RMS Titanic, whose impurities made the rivets more brittle in the freezing North Atlantic, and the crashes of the world’s first commercial jetliner, the de Havilland Comet, which revealed the dangers of metal fatigue.
“There are so many other stories that need to be told around how a material failed,” Jaramillo said. “It really cost people money and time and lives. And then through forensic materials science, we understood why it failed and we avoided future failures.”
The MRSEC team is planning to stage public outreach events at the MIT Future Fest.
Looking ahead six years, Jaramillo hopes the center will have become a self-sustaining hub for materials research.
“I hope that we will have rebuilt the muscle memory to come together in an interdisciplinary way around materials science, and that it should have a bit of a self-sustaining element to it. I hope that we then compete successfully for the next center, and lay the groundwork for the next 60 years.”
MIT Research Administration Services supported the MRSEC proposal development through its Research Development team, which specializes in providing substantive assistance for large and complex research proposals, and in supporting early-career faculty.
MIT faculty expected to be involved in the MRSEC are Rafael Jaramillo, Caroline Ross, Juejun Hu, and Antoine Allanore of DMSE; Moungi Bawendi of Chemistry; Martin Bazant of Chemical Engineering; Nicole Nie and Shuhei Ono of EAPS; and Marin Soljačić, Riccardo Comin, Nuh Gedik, and Long Ju of Physics.
Astronomers discover a brand-new type of astrophysical object: A black hole starThe mashup of a black hole and an enormous star has never been seen before and could explain the mysterious little red dots often found in deep-space images.Astronomers at MIT and elsewhere have spotted an extremely bright red spot in the early universe. The object resembles an enormous star, spanning the size of our solar system. But it also is putting out 100 billion times more energy than any known star can physically produce. In fact, such energies are closer to what a black hole might generate.
The curious combination suggests that the red spot is an entirely new type of astrophysical source. The astronomers are calling it a “black hole star.”
In a paper appearing today in the journal Nature, the team presents their analysis of the new object, which they discovered using NASA’s James Webb Space Telescope (JWST). The telescope spotted the bright red dot in the very early universe, just a few hundred million years after the Big Bang.
The scientists conclude that the most likely explanation for the strange red dot is that it is a mashup of a black hole and a star — a combination that has never been observed until now. The object is likely a hugely dense cloud of gas, powered not by standard nuclear fusion, but by a central black hole.
“Our picture of this object is evolving very rapidly,” says lead author Rohan Naidu, a NASA Hubble Fellow and Pappalardo Fellow at MIT’s Kavli Institute for Astrophysics and Space Research (MKI). “We think there is a central black hole that is 100,000 times as massive as the sun. And around this black hole, there would be this very extended envelope of gas that looks like a star the size of the solar system. It’s huge.”
If the bright red dot is indeed a black hole star, it would help to solve the identity of other mysterious “little red dots” that have appeared in nearly every deep space image JWST has taken to date.
“These little red dots seem to be everywhere in the early universe but essentially disappear by the present day,” Naidu says. “What exactly these objects are has been one of the most debated topics of the JWST era.”
The study’s MIT co-authors are MKI Director Robert Simcoe, the Bruno B. Rossi Professor of Experimental Physics; and Wendy Sun ’26, along with collaborators from multiple other institutions.
A singular source
Naidu and his colleagues didn’t intend to find a black hole star. They were looking for the most distant, earliest galaxies, as part of a survey that they named “Mirage or Miracle” (MoM). The team used the JWST to look into deep space, back when the universe was a few hundred million years old. Their goal was to look for galaxies that actually formed at those early times.
“There’s been this puzzle of many bright galaxies showing up at extremely early times,” Naidu says. “What we found was that what looks like an extremely bright early galaxy, aka a ‘miracle,’ in some cases actually could be a ‘mirage.’”
As they looked through JWST’s images for intriguing sources to target with their survey, they noticed a feature that stood out from the rest: a dot that was very red, and very bright.
“When we see something very red in the universe, we often assume that it is surrounded by dust, like soot or ash,” Simcoe explains. “The same way that the wildfire smoke from Canada recently made the sky in Boston look bright red, astronomical objects can also appear redder than their intrinsic color when you see them through a veil of dust.”
But there were other signatures in the light that didn’t quite match up with what physicists expect from dust. The team also observed another strange pattern: The dot’s light was extremely bright, except below certain wavelengths, where the light completely disappeared.
This spectral drop-off is known as a “Balmer break” — a signature traditionally associated with dense gas soaking up photons in the atmospheres of stars that are a few hundred millions of years old. Vega, one of the brightest stars in the night sky shows exactly this pattern.
“The break we observed in this object is the deepest break we have ever observed in any object, ruling out ‘ordinary’ stars as the source,” Naidu says. “But it made us wonder if we were seeing a new kind of ‘stellar atmosphere,’ but on a spectacular scale.”
What’s more, the red dot’s light contained almost no signature of metals or any elements other than hydrogen and helium. “It was truly singular in so many ways,” Naidu says.
Pure light
To puzzle out what the source of the red dot could be, the team ran simulations of different scenarios to see what combination of astrophysical features could produce the red dot’s distinctive color.
“We started to ask: Could you make something that red using just hydrogen, without any dust?” Simcoe says. “To our surprise, it turns out you can, if you have an extremely dense screen of hydrogen, so dense that it looks more like the surface of an enormous star than a wispy interstellar nebula.”
Their simulations pointed to the red dot possibly being some powerful enshrouded energy source, surrounded by an extremely dense cocoon of hydrogen. If this were the case, it would explain the light-blocking Balmer break and the lack of anything other than hydrogen and helium that the astronomers observed. But it still wouldn’t explain the object’s extreme brightness.
“You have something that looks a bit like a star but is 100 billion times brighter,” Naidu says. “That means you can’t be powering this by nuclear fusion, which is the energy source that sits at the heart of all the stars we have.”
Black holes, however, routinely produce energy at the scales the team observed. Naidu and his colleagues incorporated an active, accreting black hole into their simulations of the hydrogen-cocooned star and varied the black hole’s mass, along with other parameters. They then compared the resulting brightness of the simulated “black hole star” with the brightness that JWST observed from the red dot.
From these simulations, they found the closest match, and concluded that the most likely scenario to explain the red dot, is a black hole star. Specifically, the object likely contains a central black hole that is about 100,000 times as massive as the sun. This powerful core is surrounded by a dense, star-like cocoon of hydrogen that is roughly the size of the solar system.
The team has named the object MoM-BH*-1, after the survey that detected it, as well as the moniker “black hole star – one,” which implies that the object is the first of others. The researchers suspect that black hole stars could explain many of the other little red dots that appear in JWST images. Those objects are not as bright as MoM-BH*-1.
“Every little red dot is consistent with being a black hole star, embedded in a generic early galaxy,” Naidu says. “But what is special about MoM-BH*-1 is, the black hole star is essentially completely outshining its surrounding host galaxy, such that we’re seeing pure black hole star light.”
This research was supported, in part, by the MIT Department of Physics, NASA, and the Space Telescope Science Institute.
Physicists watch a material’s electrons assemble, and reassemble, into coexisting phasesThe study could help scientists understand how superconductivity and other more complex phenomena emerge in quantum materials.A tall glass of ice water isn’t just a thirst quencher; it’s also an everyday example of coexisting phases. Water’s molecular makeup can exist simultaneously in both a liquid and solid phase. And as it turns out, this phase duality can exist in more exotic, quantum materials, and in ways that are far more complicated to tease apart.
A new study by MIT physicists sheds light on how two different phases of electron behavior can emerge and coexist in the same quantum material.
Their results, reported today in the journal Nature Physics, can help to explain how some materials host superconductivity, magnetism, and other electronic phases. Untangling such phases, and understanding how they emerge, will help engineers control electronic behavior and design high-performance quantum devices.
“People believe the cornerstone of replacing silicon lies in quantum materials that have multiple coexisting phases,” says co-author Alfred Zong PhD ’20, who co-led the study as an MIT graduate student and is now an assistant professor at Stanford University. “Our experiment provides a very neat way to study these multiple phases.”
The team, led by Nuh Gedik, the Donner Professor of Physics at MIT, studied the rare-earth material erbium tritelluride. As with most materials, erbium tritelluride’s electrons are normally scattered uniformly throughout the material. But when cooled to certain temperatures, the electrons suddenly organize into a wave-like pattern, which physicists term a “charge density wave” (CDW) phase. When cooled even further, electrons coordinate again as a second wavy phase that criss-crosses the first. The effect is of an atomic checkerboard of co-existing electron phases.
Now, Gedik and his colleagues have teased apart erbium tritelluride’s phases and observed how each phase emerges. They found that one phase forms gradually, similar to how liquid water transitions uniformly into vapor. This is the classic, textbook way in which electronic phase transitions are thought to occur.
But the second phase came about in an entirely new and unexpected way: Instead of emerging gradually, the electrons organized first in pockets that eventually expanded, similar to how liquid water crystallizes into ice.
“The mechanism responsible for the emergence of this second phase has long been debated, and our approach provides a powerful new way to uncover the hidden physics behind phase transitions in quantum materials,” Gedik says.
The study’s other MIT co-authors are first authors Yifan Su PhD ’24 and Bai-Qing Lv, a former postdoc; Dongsung Choi SM ’17, PhD ’24; and former postdocs Doron Azoury and Masataka Mogi; along with collaborators from multiple other institutions.
A clear view
A charge density wave is made up of charges, such as electrons, that spontaneously organize as a wave. The wave’s crests hold the highest density of electrons, and the lowest are found in the troughs. In some materials, electrons transition into this strange coordinated phase at super-cold temperatures.
Scientists have observed charge density waves for decades, and most recently in materials that also host other, more complicated forms of electron coordination, such as various forms of magnetism, and superconductivity, in which electrons pair up and flow through a material without friction.
“Just like superconductivty, charge density waves are a collective phenomena where electrons move together in certain ways,” explains lead author Yifan Su. “The power of CDWs is that they are a much simpler form of matter compared to superconductivity. They offer a playground for fundamental understanding.”
Su and the team looked to get a clear view of charge density waves in a material that hosts two CDW phases simultaneously. How these waves emerge and coexist in a single material could shed light on how superconductivity and other more complicated phase transitions occur.
“One of the biggest questions in physics is why some materials host multiple phases while others do not. And when multiple phases do exist, how do they interact? Do they reinforce one another, compete, or coexist independently?” Gedik says. “This is like a case study for us to understand much more complicated materials.”
Shake, then listen
Scientists have observed two different charge density waves in erbium tritelluride — a rare-earth material that can be synthesized in the lab, in atomically thin sheets that can then be probed for unique, quantum-scale properties.
In previous experiments, physicists have found that when erbium tritelluride is cooled down to -8 degrees Celsius, the first of two charge density waves forms among the material’s electrons. This “dominant” wave stretches across the material in one direction. When the material is further cooled to -113 degrees Celsius, a second, “subdominant” charge density wave emerges, perpendicular to the first, creating a checkerboard of coexisting electronic phases.
In their new study, Gedik and his colleagues sought to tease out how each phase emerges in erbium tritelluride. The team obtained small, atomically thin samples of the material, which were synthesized by collaborators at Stanford. In Gedik’s lab, the researchers then cooled the samples down to about -230 degrees Celsius — temperatures at which the material should host both charge density waves, in a simultaneous, checkerboard pattern. They then either destroyed or weakened the checkerboard, and watched how both types of waves reemerged.
To do so, they exposed each cooled sample to a one-two punch of laser pulses.
“This is how we ‘shake’ and then ‘listen’ to the system,” Gedik says.
The first pulse was the “shake” that dissolved the checkerboard. The researchers could control the intensity of this kick to vary the degree to which the waves were disturbed. They then delivered a second laser pulse, of high-energy photons, to kick out electrons from the material. This second pulse was sent in at various times after the first pulse. The researchers then measured the energy and momentum of the kicked-out electrons, to get snapshots of how the material’s electronic phases recovered.
“We see the destroying of these phases, and then if we wait long enough, they come back,” Gedik explains. “And depending on how you hit them, the two phases respond differently.”
From their experiments, the team found that the first, dominant phase of charge density waves reemerges gradually and uniformly, no matter how hard the material was initially “kicked.” This smooth restoration is a textbook, “second-order” phase transition, similar to a magnet gradually losing its magnetism as it is heated.
What was more surprising was how the second wave pattern reemerged. This subdominant phase reformed more like water into ice. The electrons reassembled the wave in isolated pockets that spread, like crystals of ice. This more rare, “first-order” transition was not expected. The team’s study captured the the long-debated mechanism underlying the emergence of the subdominant CDW phase.
“In systems that are much more complex, like high-temperature superconductors, you see there are multiple phases — magnetism, superconductivity, charge density waves, and they all exist together,” Gedik says. “One of the theories is that, the way they interact with each other is key for their exotic properties. The lessons we learn here can be applied to much more complex materials.”
This work was supported by the U.S. Department of Energy, the U.S. National Science Foundation, and the Gordon and Betty Moore Foundation’s EPiQS Initiative grant.
Researchers make air-stable, ultrathin superconductors, for more scalable quantum devicesA new technique produces wafer-scale samples, overcoming a major roadblock to using these materials in quantum technologies.Super-thin superconducting materials, which are only one or a few atoms thick, have unique properties scientists can leverage to produce more compact, scalable, and efficient quantum devices. But these fragile materials degrade so rapidly in air that they are difficult to study or manufacture.
Now, researchers from MIT and elsewhere have discovered and harnessed a method to generate a large, uniform area of ultrathin superconducting material that remains stable in air.
They “grow” the superconducting material, called niobium diselenide, underneath another atomically thin material, carbon-based graphene. The graphene layer protects the fragile superconductor from oxidation, while guiding it to grow in a smooth layer over a large wafer-scale area.
The researchers further integrated this air-stable superconductor into a superconducting microwave circuit. When tested, the material maintained its superconducting properties and exhibited high kinetic inductance, which is a resource for many quantum devices.
In the long run, this advance could help miniaturize superconducting quantum computing hardware, as well as technologies like ultrasensitive quantum detectors for communications or cosmology.
“Emerging superconductors that are only a monolayer thick have a lot of potential. Thanks to our new process, they are no longer materials that can only be made at a very small scale. There are now exciting opportunities for scientists to study these materials, utilize them in circuits, and explore their practical applications,” says co-lead author Xudong Sheldon Zheng, a graduate student in the MIT Department of Electrical Engineering and Computer Science (EECS).
He is joined on the paper by co-lead authors Sameia Zaman SM ’24, an EECS graduate student, and Kenan Zhang, a recent postdoc in the MIT Research Laboratory of Electronics (RLE); corresponding authors William D. Oliver, the Henry Ellis Warren (1894) Professor of EECS and professor of physics, director of the Center for Quantum Engineering, and associate director of RLE; Joel Î-j. Wang, an assistant professor at New York University; and Jing Kong, the Jerry Mcafee (1940) Professor in Engineering at MIT and a member of RLE; as well as others at MIT and Lincoln Laboratory, Rice University, Yale University, and Pohang University in South Korea. The research appears today in Nature.
Powerful properties
Superconductors are materials that can conduct electricity without resistance, and they are essential for some types of quantum devices.
Two-dimensional superconducting materials retain their superconducting properties despite being only a few atoms thick. These materials hold the promise to miniaturize superconducting circuitry.
Niobium diselenide, an ultrathin superconductor composed of a single, closely packed layer of niobium atoms sandwiched between a single layer of selenium atoms on either side, has a very high kinetic inductance, as members of the research team recently reported.
This enables the material to store a great deal of inductive energy in a very small area. Large kinetic inductance in a small form-factor is a desirable design element in many quantum devices.
One commonly used approach to realizing a large kinetic inductance is to string together an array of devices called Josephson junctions.
If scientists could incorporate materials such as thin niobium diselenide with sufficiently large kinetic inductance into a quantum circuit, they could replace the large area of electronic junctions with a tiny piece of thin-film material, making the circuit more compact. But because niobium diselenide degrades rapidly in air, scientists have not been able to reliably fabricate devices at the wafer scale. Instead, they rely on exfoliation techniques that yield small flakes. Furthermore, researchers have struggled to grow material with uniform monolayer thickness. Consequently, it has been challenging to fully probe its properties or test it in practical applications.
“Typically, once we make the material and remove it from its inert environment, it immediately starts to oxidize and degrade, ultimately becoming damaged,” Zheng explains.
Scientists usually grow niobium diselenide by depositing chemical precursors onto a silicon dioxide substrate. Then they place another layer of two-dimensional material, like graphene or hexagonal boron nitride, on top to protect the fragile superconductor from air.
But such postgrowth protection presents a challenge. The superconductor begins to oxidize almost immediately after synthesis, degrading its properties before it is protected. Meanwhile, the protection process requires a stringent inert environment and delicate processing.
Mind the gap
The MIT researchers used a different tactic. They put the layer of graphene on top of the silicon dioxide substrate first. Then they deposited the precursors and grew the superconducting material in the tiny gap between the two layers.
“It took a long time for us to understand how the growth could happen underneath the graphene. Through collaboration and discussion, we eventually uncovered the mechanism for growing the material at the interface, and this solves a lot of problems and allows us to simplify our fabrication steps,” Zheng says.
The silicon dioxide substrate helps trap the precursors long enough for the crystal to begin forming, while the graphene layer allows them to move around easily and spread into a continuous monolayer.
The researchers used this technique to generate a perfectly smooth layer of niobium diselenide more than an inch in size.
“By carefully tuning the growth conditions, we can ensure the material grows between the layers in exactly the way we’ve designed,” Zheng says.
Even though the graphene is placed on top of the silicon dioxide, the weak adhesion between these materials leaves a gap between them less than 1 nanometer thick. The niobium diselenide grows only within that gap. Then, since it is already encapsulated by graphene, the researchers can safely remove it into the ambient environment without causing degradation.
Careful connections
The researchers also designed an oxidation-free transfer technique to peel the graphene-niobium diselenide structure from its growth substrate, building on prior work by members of the team.
Then, they developed a method to integrate the thin film into a quantum circuit without hampering the fragile superconductor or its properties.
“It is challenging to make a good electrical connection between this very thin material, which is only about 1 nanometer in thickness, and our electrodes, which are a few hundred nanometers in thickness,” Zaman says.
They carefully etch the side walls of the thin-film superconductor in a vacuum chamber, which preserves the smooth edge of the material. When they integrate the prepared niobium-graphene structure into a conventional superconducting circuit, it forms a reliable electrical connection. Importantly, the material maintained its superconducting properties and exhibited high kinetic inductance after clean room fabrication and integration into the circuit. This makes it particularly attractive for fabricating compact superconducting quantum devices and other quantum technologies.
Furthermore, the growth strategy is not limited to monolayer niobium diselenide. The researchers demonstrated that it can be extended to a broad family of monolayer quantum materials with diverse and technologically important properties.
In the future, the researchers aim to integrate these ultrathin superconducting materials into functional device architectures to enable the exploration of fundamental physics and the prototyping of quantum devices and other advanced technologies.
“We’ve taken a very good step toward exploring both the physics and the application side of this thin, monolayer superconductor, which we can now grow in wafer scale or in even larger areas. There are a lot of directions we can go in the future,” Zaman says.
This research was funded, in part, by the U.S. Army Research Office, the U.S. National Science Foundation, the Schlumberger Foundation, the U.S. Department of Energy, the U.S. Air Force Office of Scientific Research, the Semiconductor Research Corporation Center, the MIT Institute for Soldier Nanotechnologies, and the National Research Foundation of Korea. This work was carried out, in part, using MIT.nano facilities.
Alexander Rakhlin named director of the MIT Statistics and Data Science CenterAn expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra.Alexander “Sasha” Rakhlin PhD ’06, the Distinguished Professor in Data, Systems, and Society at the MIT Institute for Data, Systems, and Society (IDSS); a professor of brain and cognitive sciences at MIT; and a principal investigator in the MIT Laboratory for Information and Decision Systems (LIDS) has been named the next director of the MIT Statistics and Data Science Center (SDSC).
Rakhlin succeeds Ankur Moitra, the Norbert Wiener Professor of Mathematics, associate director of the IDSS, and a faculty member in the MIT Department of Electrical Engineering and Computer Science (EECS) who has been SDSC director since 2021. Philippe Rigollet, the Cecil and Ida Green Distinguished Professor of Mathematics and a core faculty member in IDSS, also served as interim director in 2024-25.
“Sasha is one of the sharpest theoretical minds working in statistics and machine learning today, and also one of the most devoted mentors I know,” says Fotini Christia, the Ford International Professor of the Social Sciences and director of IDSS, which houses SDSC. “He has helped train an entire generation of interdisciplinary scholars through the Interdisciplinary Doctoral Program in Statistics (IDPS), while his own research keeps pushing the boundaries. The SDSC could not ask for a more fitting leader.”
Rakhlin is the inaugural holder of the Distinguished Professorship in Data, Systems, and Society, an endowed chair created in 2025 by the generosity and vision of IDSS professor Richard “Dick” Larson, an “MIT lifer” and pioneer in operations research, queueing theory, and system optimization.
“I am honored to take on this role,” says Rakhlin. “The strength of the Statistics and Data Science Center has always been its people — students, postdocs, and faculty from across MIT who bring sharply different perspectives to the most interesting problems of the day in statistics, machine learning, and AI. My goal is to support that community as it takes on the constantly evolving questions reshaping the field.”
Rakhlin has been connected to the Statistics and Data Science Center as a visiting professor since 2016, before formally joining MIT in 2018 in the Department of Brain and Cognitive Sciences and IDSS. As the initial chair of the Interdisciplinary PhD in Statistics program at the SDSC, Rakhlin has seen the successful defense of over 75 IDPS PhD students across a variety of departments at MIT, including IDSS’ own Social and Engineering Systems program.
“I have been fascinated by machine learning since my PhD work more than 20 years ago, drawn by its beautiful connections to statistics, probability, algorithms, optimization, and game theory,” says Rakhlin. “At the Statistics and Data Science Center, I work alongside colleagues who share this fascination and pursue these connections in many directions. The recent revolution in AI is extending this web into the sciences; it promises to accelerate discovery, and it raises new questions for statistics. Answering them demands a rigorous science of the tools themselves. As AI enters medicine, energy, and public life, its safety and security are, at their core, statistical and mathematical questions: quantifying uncertainty, providing guarantees, understanding failure, and resisting manipulation.”
As Rakhlin puts it, the SDSC is built for this moment. “Statistics is a shared language across MIT,” he adds. “Through the Interdisciplinary Doctoral Program in Statistics, the center connects students and faculty from economics and political science to physics and engineering. Collaborations in areas from biology to nuclear fusion have shown how statistical thinking accelerates science itself.”
As director, one of his goals is to deepen these interdisciplinary connections. He hopes to help make SDSC the Institute’s home for the rigorous foundations of data science and AI, and a bridge to the scientific and societal questions where those foundations are most needed.
Rakhlin received his bachelor’s degrees in mathematics and computer science from Cornell University, and doctoral degree from MIT. He was a postdoc at the University of California at Berkeley in EECS before joining the University of Pennsylvania, where he was an associate professor in the Department of Statistics and co-director of the Penn Research in Machine Learning center.
Molecules are among the smallest building blocks available for making next-generation devices. Their unique, customizable properties enable promising applications in emerging computing, sensing, optical, and quantum technologies.
But integrating molecules into functional devices at scale remains a challenge. Traditional semiconductor manufacturing processes can damage small and fragile molecular materials. Now, MIT researchers have developed a scalable fabrication technique that incorporates delicate molecular materials into electronic devices on a chip without causing damage.
Their method extends the capabilities of standard semiconductor manufacturing processes to accommodate molecules. The researchers first prefabricate the device components using traditional processes. Then, they introduce the molecules and harness nanoscale surface forces to mechanically transform the fabricated device, which self-assembles without damaging the molecules.
The team demonstrated the robustness and scalability of their technique by fabricating more than 1,000 devices using sub-nanometer molecular layers.
“Our platform combines the scalability of conventional semiconductor manufacturing with the precision and control of self-assembly. This establishes a new fabrication framework for the scalable, high-throughput integration of emerging nanoscale and quantum materials, including molecules, into functional devices with architectures and capabilities that were previously infeasible,” says Farnaz Niroui, an associate professor of electrical engineering and computer science (EECS), a member of the Research Laboratory of Electronics (RLE), and senior author of a new paper describing the work.
She is joined on the paper by co-lead authors Sarah Spector and Peter Satterthwaite, EECS graduate students; Jeremiah A. Johnson, the A. Thomas Guertin Professor of Chemistry at MIT; and others at MIT. The research appears today in Nature Nanotechnology.
Building with molecules
Molecules are small clusters of atoms with structures and chemistries that can be precisely designed. This allows their properties to be engineered across a wide design space.
Once integrated into device architectures, these molecules could enable next-generation electronics and computing platforms that are smaller, faster, and more adaptable, as well as higher-performance photonic devices and emerging quantum technologies.
To build a functional system, molecular building blocks need to be integrated with other device layers. In electronic systems, a critical step is making electrical contacts to the molecules by interfacing them with metallic surfaces. However, the harsh chemicals and processes needed for traditional chip manufacturing damages these fragile molecular materials, reducing reliability and performance.
To leverage the scalability of standard fabrication techniques while achieving the precision needed for handling molecules, the MIT researchers developed a decoupled, two-step approach.
They first fabricate all the device components using standard semiconductor manufacturing, then incorporate the molecular material after-the-fact to finish building the device.
“By bringing the delicate materials into the process only after we have fabricated the main device elements, it allows us to use conventional processes that are normally not compatible with these nanomaterials,” Satterthwaite says.
In their demonstration, the researchers fabricated a scaffold with two metal electrodes separated by a precisely sized gap. Then, they deposited the molecular layer on the electrode surfaces.
Finally, the researchers leverage nanoscale forces to gently pull the top electrode onto the molecules, forming the final device in a nondestructive way. This creates a self-aligned, damage-free electrical contact to the molecules.
Using the forces
While gravity is a dominant physical force that holds our world together, different forces dominate at the nanoscale. One, called the capillary force, causes liquid to get sucked into small spaces. (Plants rely on capillary forces to draw water into their stems.)
By carefully engineering the stiffness of the electrodes, when the solution containing the molecules evaporates, capillary forces gently pull the two metal surfaces together with the molecules sandwiched in between.
Once the two electrodes are in place, the researchers must hold them in a stable state. To do so, they rely on another nanoscale force known as the van der Waals force.
Van der Waals forces cause surfaces to attract one another. By controlling the device surface area and molecules properties, the researchers ensure these forces will be strong enough to hold the electrodes in a stable structure without damaging the molecules.
“Nanoscale forces play a critical role in our approach. Instead of fabricating exactly the structures we ultimately want, we make something mechanically mobile and use forces to transform it into an architecture that would otherwise be impossible to fabricate,” Spector explains.
They used this technique to fabricate more than 1,000 devices with molecular layers less than 1 nanometer thick. Even at this tiny scale, the fabricated chips comprised a high yield of working devices, 96 percent on average. The robust devices also endured tens of thousands of electrical cycles without showing any sign of degradation.
“The stability really stands out. This is a critical feature for moving molecular devices toward practical applications, but it has been a persistent challenge in the field,” Satterthwaite says.
Importantly, this versatile technique allows circuit- and system-level integration of molecular devices, pushing the field beyond the study of isolated devices, the researchers say. They demonstrated this by building an interconnected array of molecular memory devices which could have applications in next-generation computing platforms.
Their technique can also be extended to other materials and device architectures.
In the future, the researchers want to build on this platform to investigate and develop new classes of multifunctional computing and sensing devices and systems.
“By enabling the pristine integration of emerging molecular materials and other atomic-scale matter into functional devices at scale, our platform accelerates discovery and design of these materials with tailored functionalities and their deployment in emerging technologies,” Niroui adds.
This research was funded, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA), the Semiconductor Research Corporation, the U.S. National Science Foundation (NSF), the MathWorks Fellowship, and the Netherlands Organization for Scientific Research. Device fabrication was carried out, in part, using MIT.nano facilities.
Why some nitrogen-processing enzymes are more efficient than othersNew findings could help researchers design synthetic catalysts that convert nitrogen gas to ammonia, a key step in fertilizer production.Nitrogen gas is abundant in Earth’s atmosphere, but most living organisms can’t readily use this nitrogen. Only a subset of microbes that have enzymes known as nitrogenases can break nitrogen gas apart and convert it into ammonia.
There are three different classes of nitrogenases found in nitrogen-fixing microbes, which vary based on the types of metal that they contain. Nitrogenases that contain the metal molybdenum are the most efficient, and two new studies from MIT offer an explanation for why that is.
The findings could help guide the design of engineered enzymes or synthetic catalysts that can convert nitrogen gas to ammonia, the researchers say.
The team found that while molybdenum doesn’t directly bind to nitrogen, it helps nearby iron atoms bind to nitrogen more strongly. This is a critical first step in breaking the bond between the two nitrogen atoms that form nitrogen gas.
“It’s that initial binding step that’s really the hard part. Once you’ve started to break the nitrogen-nitrogen triple bond and make some new nitrogen-hydrogen bonds, it’s pretty easy to get the rest of the way,” says Daniel Suess, the Arthur Amos Noyes Associate Professor of Chemistry at MIT and a senior author of both papers.
MIT postdoc Tong Wu and former postdoc Madeleine Ehweiner are the lead authors of one of the papers, and Alexandra Brown PhD ’23 is the lead author of the other. Kyle Lancaster, a professor of chemistry at Cornell University, is a senior author of the latter paper, along with Suess. Both papers appear today in the journal Chem.
Efficient enzymes
Before microbes evolved the ability to fix nitrogen around 3 billion years ago, the strong triple bond between atoms of N2 could only be split with high-energy events such as a lightning strike.
“Once an enzyme came along that could convert dinitrogen to ammonia, that changed the game because now cells could harvest nitrogen from the air for biomass,” Suess says.
Within the active site of nitrogenase is a catalytic cofactor that typically consists of a cluster of iron, sulfur, carbon, and in some cases another metal. Nitrogenases whose cofactors contain molybdenum are the most efficient, followed by those containing the metal vanadium. Nitrogenases that don’t have any metal other than iron are the least efficient.
Why the molybdenum-containing enzyme is more efficient has been a puzzle, especially because it’s thought that molybdenum itself doesn’t bind directly to nitrogen gas.
“In all cases, iron is thought to interact with N2, so it’s a bit of a mystery,” Suess says. “If all the chemistry is happening at iron, why is it that this molybdenum is affecting catalysis?”
To answer that question, Suess’s lab has developed simpler versions of iron-sulfur clusters that they can use to model the naturally occurring cofactors. These can be modified by adding different metal atoms, allowing the researchers to study how those metals change the cofactors’ properties.
In the first paper, led by Wu and Ehweiner, the researchers swapped in different metal atoms and then measured the ability of the iron in the cofactor to bind to nitrogen. They found that only cofactors with a large metal atom, such as molybdenum or tungsten, were able to strongly bind N2. With vanadium, chromium, or iron, which are smaller, the cofactors did not bind N2 and performed other reactions instead.
“That paper essentially recapitulates what you see in biology, which is that the iron-sulfur clusters that have molybdenum in them seem to be better at binding dinitrogen than those with lighter metals,” Suess says.
Sharing electrons
In the second paper, led by Brown, the researchers uncovered a possible mechanism that explains that phenomenon.
In that paper, the researchers studied how cofactors containing different metals interact with compounds called N-heterocyclic carbenes. These molecules behave similarly to N2 in some ways, making them a good model for this type of study. Like N2, they are resistant to accepting any electrons from another molecule, which is an essential step to breaking chemical bonds.
The researchers found that when molybdenum was included in the cluster, it became easier for iron to donate some of its electrons to the N-heterocyclic carbenes, in a process known as back-bonding. This occurs because molybdenum, a large atom, has large orbitals that can overlap with the orbitals of the nearby iron atom. That alters iron’s electron density in ways that make it easier for iron to pass electrons to N2.
“Without these direct metal-metal interactions, the iron has to do all the work, but adding the molybdenum allows for this electronic cooperativity,” Suess says.
Once N2 is bound to an iron atom, the rest of the reaction can proceed. A proton can come in from water or another source to create an N-H bond, which then makes it much easier for the remaining N-N bonds to be broken and bind to protons, forming NH3.
The findings could help guide scientists who are working on designing enzymes that could be engineered into organisms that help them generate their own NH3, eliminating or reducing the need for fertilizer. The results could also help chemists to design synthetic catalysts that could produce ammonia industrially, using less energy than the Haber-Bosch process.
“The general principle is that you can make an iron site in any context behave differently when you have these metal-metal interactions than when you don’t have these interactions,” Suess says. “The primary result of these findings is to teach us about the natural world and how nature accomplishes this really important and miraculous reaction. And, maybe that can be translated into new processes.”
The research was funded primarily by the U.S. Department of Energy, the National Science Foundation, and the National Institute of General Medical Sciences.
MIT and Broad Institute researchers break diffraction barrier in super-resolution microscopyNew U-STORM imaging technology lets scientists view molecular structures in subatomic detail — about 1,000 times clearer than traditional dyes — while making the microscope process much simpler.Researchers in the lab of Sam Peng, the Pfizer Inc. - Gerald Laubach Career Development Assistant Professor of Chemistry at MIT and a core institute member of the Broad Institute of MIT and Harvard, have developed a groundbreaking super-resolution imaging technology that allows scientists to visualize molecular structures with sub-angstrom-level localization precision — three orders of magnitude beyond the nanometer limits of standard fluorescent dyes — while drastically simplifying the imaging process.
Unlike traditional dyes that fade rapidly under illumination and limit data collection, the platform, called U-STORM (Upconversion enabled Stochastic Optical Reconstruction Microscopy) utilizes a new class of compositionally engineered upconverting nanoparticles (UCNPs) that blink spontaneously and indefinitely.
This work represents a fundamental shift in both optical materials and biological imaging. An open-access description of the study was published July 27 in Nature Nanotechnology.
Overturning a decades-old paradigm
For decades, the scientific community widely considered upconverting nanoparticles to be completely photostable and non-blinking. Because localization-based super-resolution microscopy techniques like STORM rely entirely on the stochastic “blinking” (switching between “on” and “off” states) of light emitters to distinguish closely packed molecules, UCNPs were historically deemed unsuitable for this type of imaging.
“Our laboratory has long been interested in overcoming these limitations,” says Peng. “Our work began with a question: Can we develop a super-resolution imaging platform that is simultaneously long-term, multicolor, simple to operate, and capable of achieving extremely high localization precision without using imaging buffers or additional optical control?”
By meticulously controlling nanoparticle composition, the MIT and Broad Institute team discovered that these small (~10nm) core-shell particles could actually be coaxed into spontaneous blinking under continuous near-infrared excitation. Remarkably, this blinking behavior continues indefinitely without the need for complex imaging buffers, oxygen scavengers, or external optical modulation.
U-STORM’s key breakthroughs
An angstrom is a tiny unit of measurement used by chemists to measure size and distances at the atomic level. U-STORM’s ability to blink indefinitely has afforded researchers the opportunity to collect over 88,000 localization events from the same particle, sharpening the localization precision down to an unprecedented 0.6 Å.
Unlike conventional multicolor super-resolution imaging, which requires multiple expensive lasers and meticulous optical alignment, U-STORM can operate with just one near-infared laser, which works to simultaneously excite nanoparticles emitting different colors. This results in a drastic reduction of an experiment’s complexity.
To obtain images with multiple colors, rather than capturing images sequentially over multiple rounds, U-STORM captures multiple colors simultaneously. Researchers have successfully demonstrated this by mapping epidermal growth factor receptor dimers and multimers in biological samples under physiological conditions without any specialized imaging buffers.
Broader impact
Beyond expanding the boundaries of microscopy, this research establishes an entirely new design principle for lanthanide nanomaterials. The team is already working to expand the color palette, make the particles even smaller and brighter, and deploy U-STORM to investigate complex nanoscale protein organizations and cellular signaling pathways.
Ultimately, U-STORM promises to provide laboratories worldwide with an accessible, easy-to-implement, yet incredibly powerful route toward high-precision molecular imaging.
Yu Deng ’11 and Hong Wang PhD ’19 awarded Fields MedalMIT-trained mathematicians earn the honor, one of the most prestigious in the field, for their significant achievements.MIT alumni Yu Deng ’11 and Hong Wang PhD ’19 were among the four young mathematicians awarded Fields Medals on July 23 at the 2026 International Congress of Mathematicians (ICM). The other two honorees were John Pardon and Jacob Tsimerman.
The Fields Medal is awarded once every four years at the ICM, and is regarded as one of the highest honors a mathematician can receive.
Yu Deng received his BS in mathematics at MIT in 2011, and was a Putnam Fellow in 2010. He earned his Fields Medal for his work in partial differential equations (PDE), including the rigorous derivation of the Boltzmann equation from hard-sphere dynamics for rarefied gases, the derivation of wave kinetic equations from nonlinear dispersive systems, and probabilistic approaches to nonlinear Schrödinger dynamics. His first published paper (in Analysis & PDE) was on the latter topic, and stemmed from summer research conducted at MIT on a problem suggested by Abby Rockefeller Mauzé Professor of Mathematics Gigliola Staffilani. Deng is currently a professor at the University of Chicago.
Hong Wang received her PhD at MIT in 2019 under the supervision of Larry Guth PhD ’05, the Claude E. Shannon Professor of Mathematics. She is awarded the Fields Medal for her work in harmonic analysis and geometric measure theory, including applications of multiscale and decoupling techniques to the local smoothing conjecture for the planar wave equation, and other major advances such as the solution of the Kakeya problem in three dimensions (with Joshua Zahl). As a student in the department, she was a graduate mentor in the Summer Program in Undergraduate Research (SPUR) and, alongside her mentee, was awarded the Hartley Rogers Jr. SPUR Prize, presented to the best student-mentor team. Wang, a Silver Professor of Mathematics at New York University and a professor at the Institut des Hautes Études Scientifiques in Paris, is the third woman ever to win a Fields Medal.
“The achievements of Yu Deng and Hong Wang are truly monumental, and we are all elated that they were awarded Fields Medals,” department head and RSA Professor of Mathematics Michel Goemans says. “Their success is a testimony of the amazing mathematical talent we have at all levels at MIT, and the top-quality education, mentorship, and research opportunities we provide to both our large pool of math majors and our PhD students, during their lifelong mathematical journey.”
Goemans adds, “MIT is a unique and exciting place to learn mathematics, and I am sure we have more future Fields medalists among our students and junior members of the department.”
Looking beyond researchProfessor Anna-Christina Eilers is “Committed to Caring” for building a culture around attentiveness and community.In Professor Anna-Christina Eilers’ research group, mentorship happens through small, meaningful gestures: thoughtful feedback on a draft, a check-in after a rough week, and a readiness to help when things get tough. For her students, these everyday moments have become a defining feature of her approach.
An observational astrophysicist, Eilers studies how the universe evolved from its earliest beginnings. Her research investigates the formation and growth of black holes across cosmic time, particularly during the “cosmic dawn,” when the first stars, galaxies, and quasars illuminated the young universe.
Working alongside her in this field, graduate students describe a mentor who pairs high expectations with genuine attentiveness, encouraging both scientific independence and a strong sense of community. This approach has earned Eilers recognition through MITs Committed to Caring initiative — a student-driven program honoring exemplary mentorship within the graduate community.
Showing up in the everyday moments
Students say one of Eilers’ defining qualities is her consistency. No matter how busy her schedule, they know they can count on thoughtful feedback, productive meetings, and regular conversations about both research and broader career development. While those practices may sound routine, her mentees emphasize that they are anything but guaranteed within many academic spaces.
“As Christina's advisees,” two students wrote in their joint nomination, “we are both extremely grateful for the professional and emotional support we constantly receive. She always keeps an eye out for us.”
Eilers’ support takes many forms. Students describe an advisor who carefully reads every draft, provides timely and detailed feedback, and creates space for conversations that extend beyond immediate research questions.
Students also reflect on the manner in which Eilers celebrates their wins alongside them. “She brings our favorite desserts to group meetings when we publish a paper,” shared one nominator.
Her attentiveness becomes especially meaningful when challenges arise. Students note that she regularly checks in on them and does not hesitate to step in when research collaborations become difficult or obstacles threaten to slow their progress. Rather than leaving them to navigate those situations alone, she helps identify solutions before small problems become larger ones.
For Eilers, building a successful research group means cultivating connections among its members as well as producing strong science.
One of the group’s traditions takes place whenever a member returns from a conference or research visit. The traveler brings back a small treat — cookies, chocolates, or another local specialty — to share during the next group meeting. Along with the snacks comes a conversation about the talks they attended, the researchers they met, and the ideas they brought home.
The tradition transforms an individual trip into a shared opportunity for learning, with new perspectives becoming part of the group’s collective conversation. These exchanges work to not only reinforce a sense of community, but also to expose students to research and ideas beyond their own projects.
Through moments like these, students develop both as researchers and as colleagues who celebrate one another’s successes and learn from one another's discoveries.
Remembering the person behind the researcher
One of Eilers’ most consistent pieces of advice has little to do with coursework or research.
“I always recommend to incoming graduate students to find a hobby outside of work that they enjoy, and ideally where they interact with people they don’t work with,” she says.
She believes maintaining interests beyond the lab helps students sustain both their curiosity and their perspective. “Graduate school can be all-consuming,” she says, reflecting on her own experiences. “It's easy to let your research become your entire identity.”
This same philosophy shapes her mentorship: successful researchers are also people with lives, relationships, and interests beyond their work. Making space for those parts of life helps students build careers that are both ambitious and sustainable.
Eilers traces her approach to the advisors who shaped her own career.
“I was very fortunate to have had — and continue to have — several mentors who have challenged me scientifically and supported me along the way," she says. “They modeled how to pursue excellent research without losing sight of the importance of personal connection and integrity.”
Her students see these values reflected within the group environment. They are encouraged to tackle ambitious questions while developing the confidence to think independently, but they know that guidance is available when they need it.
In their nominations of Eilers, students describe an advisor who is present in both the ordinary and the difficult moments — someone who notices when support is needed, advocates for her students, celebrates their successes, and builds a community where students consistently feel seen.
Through this steady commitment, Eilers demonstrates that care is not separate from academic excellence. Rather, it creates the conditions that allow excellence to flourish.
MIT projects selected for funding under US Department of Energy’s Genesis MissionInitial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.MIT researchers are set to contribute to the U.S. Department of Energy’s (DOE) Genesis Mission, with 15 collaborative projects among those selected for funding under Genesis Phase I, DOE announced Wednesday.
The Genesis Mission, a national initiative, intends to build “the world’s most powerful integrated science discovery platform” by incentivizing cross-sector collaborations that leverage AI, supercomputing, quantum systems, and advanced scientific instruments to accelerate breakthroughs in energy, scientific discovery, and national security.
“MIT researchers are proud to be leading and contributing to projects under the Genesis Mission, in vital areas of research that support national priorities,” says Ian A. Waitz, MIT’s vice president for research. “The Genesis Mission represents a fantastic opportunity to catalyze the power of universities, industry, and the U.S. national laboratories to advance science, technology, and innovation for the benefit of the nation and the world.”
The DOE announced the initial projects during its Genesis Summit in Washington on Wednesday. The research funding to MIT is pending completion of negotiations toward an award agreement for each project. In phase I, funded project teams will work to demonstrate research workflows that integrate AI with scientific investigation, and to rigorously evaluate the scientific merit of their approach.
Projects under the Genesis Mission are collaborative by design; teams must draw on the expertise of researchers from academia, industry, and/or the national laboratories. Among the selected phase I projects with MIT involvement are those that aim to develop powerful quantum sensors to help explain fundamental questions about the universe; advance knowledge of chemical-free methods to extract rare earth elements; model the behavior of plasma in fusion tokamaks and future fusion reactors; develop digital twins for fusion magnet systems; exploit the self-assembly of biomolecules to design materials with targeted properties; generatively design rotating blades for machinery systems; and more. Phase I projects that identify promising pathways toward transformative capabilities at scale may be considered by DOE for further Genesis Mission funding.
Six of the selected projects are to be led by MIT principal investigators (PIs):
MIT researchers are expected to participate in another nine selected projects led by other institutions, companies, and labs:
“The extraordinary response to this Genesis Mission application process demonstrates that America’s scientific community is ready to reimagine how discovery happens,” said DOE Under Secretary Darío Gil SM ’00 PhD ’03, in the DOE’s announcement. “Through the Genesis Mission, we are bringing together the nation’s leading researchers, institutions, and technology partners to build the next generation of scientific capability. We look forward to seeing these teams demonstrate new research workflows that accelerate discovery and reveal what is possible when AI and science advance together.”
A complete list of the first Genesis Mission projects selected for award negotiations is available from the U.S. Department of Energy.
Diffuse puffs of “missing” matter surround most galaxies An MIT-led team used bright radio bursts to illuminate a vast source of matter that was previously unaccounted for.Stars and galaxies make up much of the universe’s ordinary, observable matter. But for decades, scientists have wrestled with a cosmic conflict: There should be much more.
Physicists have good estimates of how much matter was present in the early universe. Shortly after the Big Bang, roughly 83 percent of all matter in the universe was composed of invisible dark matter, with ordinary matter making up the rest. And yet, these estimates exceed the amount of ordinary matter seen in stars and galaxies today. Where, then, did all the missing ordinary matter go?
Now MIT scientists, as part of the CHIME/FRB Collaboration, are using far-off radio signals to reveal missing matter in the vast space between galaxies. The team has developed a new method to search out missing matter by combining locations of galaxies with detections of fast radio bursts.
A fast radio burst, or FRB, is an ultrabright, millisecond flash of radio waves emitted by extremely energetic phenomena in the distant universe. As it travels through space, the signal from a fast radio burst gets stretched, or “smeared,” in time. The more missing matter that it passes through, the more smeared the signal becomes.
The MIT-led team measured the degree of smearing experienced by thousands of FRB signals detected on Earth. Then they compared each FRB smear with locations of galaxies across the universe to determine how much of an FRB’s smearing was due to galaxy matter versus other, missing matter.
The new method revealed not only whether missing matter was present, but also where. Specifically, the researchers discovered that it exists in very diffuse clouds surrounding groups of galaxies. These clouds extend out from the galaxies, to much further distances than scientists had predicted.
“We find that, overall, where there are more galaxies, there tends to be more missing matter around them,” says Haochen Wang, a graduate student in MIT’s Kavli Institute for Astrophysics and Space Research.
The results, reported today in the journal Physical Review Letters, support the idea that matter is flung outside a galaxy through black hole jets, exploding stars, and other highly energetic processes within a galaxy. What’s more, the findings suggest that such processes are more energetic than scientists had thought.
“We’re finding missing matter that is pushed out to larger scales,” says Kiyoshi Masui, associate professor of physics at MIT. “These measurements indicate that star activity, and activity from black holes, is stronger and much more violent than predicted.”
Masui and Wang are co-authors of the new study, which includes Shion Andrew, Adam Lanman, Kenzie Nimmo, and Ryan Raikman from MIT, and collaborators from multiple other institutions as part of the CHIME/FRB Collaboration.
The shape of matter
The vast majority of ordinary, observable matter in the universe is built from baryons — a type of subatomic particle that includes protons and neutrons, and that makes up most of an atom’s mass. Scientists estimate that just 17 percent of the early universe was made from this “baryonic” matter, shortly after the Big Bang.
Some of that early matter was forged into every substantial thing we see today, from planets, stars, and galaxies, to our own bodies. But as scientists have realized, this matter doesn’t quite add up. The total mass of all the stars, galaxies, and galactic clouds is about a tenth of the baryonic matter that existed in the early universe. There must be more matter, likely in the spaces between galaxies. But the universe is vast. Any leftover matter likely exists at extremely low densities, of around a single proton per cubic meter, making it extremely challenging to detect.
Recently, however, Masui and others have found that such missing matter could be sussed out using fast radio bursts. FRBs were first discovered in 2007, and since then astronomers have detected several thousand of the mysterious, ultrashort signals from distant galaxies, billions of light years away.
“What makes FRBs good to probe missing matter is that they have a special property,” Wang says. “They start out as a very quick flash, and as they pass through matter, they smear out in time. And we can measure that smearing very precisely, which is directly proportional to how much missing matter the FRB passed through.”
Researchers have previously taken advantage of this smearing property of FRBs to detect missing matter around galaxies. These efforts have confirmed that tenous clouds exist in the vast spaces between galaxies. Masui and Wang wanted to go a step further.
“We’re not just probing if the gas is with the galaxy or not, but we are seeing the shape of the missing matter that’s around the galaxies,” Wang says. “By mapping the shape of missing matter, we can understand how galaxies form and how they interact with their environment.”
Galactic fountains
For their new study, the team mapped the shape of missing matter around galaxies by cross-correlating thousands of FRB measurements with locations of millions of galaxies. They used data from two sources: the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and the Dark Energy Spectroscopic Instrument (DESI) survey.
CHIME is a large radio telescope located in British Columbia, Canada, that is designed to scan the entire northern sky for incoming radio waves. The telescope is sensitive to ultrashort, ultrabright radio signals, and since it began observing, CHIME has detected about 4,000 fast radio bursts across the sky.
DESI is an instrument that is mounted on the Mayall Telescope at Kitt Peak National Observatory, near Tucson, Arizona. The instrument makes detailed measurements of the light coming from over 30 million galaxies, to provide estimates of dark energy — the mysterious force that drives the expansion of the universe.
From CHIME’s catalog of detections, members of the CHIME/FRB collaboration analyzed 2,870 FRB signals. Each signal is a burst of radio waves, at multiple wavelengths, from highest to lowest energy. The higher-energy “blue” waves typically are less affected by any missing matter they travel through, and therefore should arrive at a detector before lower-energy “red” wavelengths, which are more delayed, or “smeared,” in time.
The team measured the smearing of each FRB’s various wavelengths, which they could then directly relate to the amount of matter that the FRB must have traveled through before reaching CHIME’s detectors. Masui and Wang then correlated these measurements with the locations of over 6 million galaxies provided by DESI data. In this way, they could look for an association between the missing matter and the galaxies, and measure where one is in relation to the other.
Their analysis revealed a pattern: Missing baryonic matter tended to be found around galaxies and galaxy clusters. But rather than gathering close to galaxies in a dense ball, missing matter was scattered across a large radius, similar to a diffuse puff.
“A galaxy is maybe a few 100,000 light years across, and we found missing matter out to about 4 million light years,” Masui says. “That’s further than the simulations predict, by quite a bit.”
“We are finding that the activity in galaxies is messier than we thought,” Wang says. “They’re more like fountains, and really push out gas to very large distances.”
The new results show that fast radio bursts can be a reliable method by which to search for missing matter. As CHIME continues to detect more FRBs, the team says its method can only improve.
“We got it to work for the first time, and will get it to work even more precisely as data gets better,” Masui says.
CHIME and CHIME/FRB are supported by the Canada Foundation for Innovation, the Natural Sciences and Engineering Research Council of Canada and, the provinces of British Columbia, Québec, and Ontario. This study was supported in part by the U.S. National Science Foundation.
Emery Brown, Daniel Hastings, and Douglas Lauffenburger named Institute ProfessorsLongtime professors and experts in the fields of anesthesiology, aerospace engineering, and biological engineering, respectively, receive MIT’s highest faculty honor.A physician and neuroscientist who studies how anesthesia affects the brain; a leader in aerospace engineering, policy, and education; and the founding head of MIT’s Department of Biological Engineering have been awarded MIT’s highest faculty honor: the title of Institute Professor.
With the appointments of Emery Brown, Daniel Hastings SM ’78, PhD ’80, and Douglas Lauffenburger, there are now 12 Institute Professors at MIT, along with 10 Institute Professors Emeriti.
The appointments, which took effect July 1, were announced today in an email to the faculty from Sally Kornbluth, MIT’s president; Anantha Chandrakasan, MIT provost; and Roger Levy, chair of the faculty and a professor of brain and cognitive sciences.
Emery Brown
Brown, who has been a member of the MIT faculty since 2005, says he is “tremendously honored” to be appointed as an Institute Professor.
“It’s a pleasure to know that your colleagues hold you in such high esteem and that the work that you’re doing is valued,” says Brown, who is the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience, an investigator at The Picower Institute for Learning and Memory, and a professor in the Department of Brain and Cognitive Sciences and the Institute for Medical Engineering and Science. “When you look down the list of people who have had this title, it’s an amazing group.”
After graduating from Harvard University with a bachelor’s degree in applied mathematics in 1978, Brown earned a PhD in statistics, also from Harvard, and an MD from Harvard Medical School. Since 1992, he has been a member of the Harvard Medical School faculty, and until recently he was a practicing anesthesiologist at Massachusetts General Hospital.
Throughout his career, Brown has made contributions in several different areas of neuroscience. In the early stages of his research career, he developed statistical methods to characterize the properties of the human circadian clock. He showed how light exposure can shift the phase of the human clock, depending on the circadian phase during which the light is administered. He also developed methods to demonstrate, from analyses of physiological data collected under special low-light conditions, that the intrinsic period of the human clock, like that of other species, is closer to 24 hours and not 25. Brown also measured the impact of shift work schedules that were designed using circadian physiology.
Later, he developed new statistical techniques and signal processing methods to analyze data collected in systems neuroscience experiments. As part of this work, he devised algorithms to decode the position of an animal in its environment by reading the activity of a small group of place cell neurons in the animal’s brain.
Joining MIT’s faculty just over 20 years ago represented an “inflection point” in his career, Brown says.
“I was an anesthesiologist doing statistical research, interested in neuroscience, and MIT allowed me to tie all those together,” he says. “I could work with colleagues who could help me understand the neuroscience of anesthesia, have another outlet for the statistical research that I was doing, and also more direct interactions with undergraduates and grad students.”
Over the past two decades, Brown has applied statistical techniques to studying what happens to the brain under anesthesia. His work has revealed how drugs such as propofol alter the brain’s intrinsic oscillations, which can be seen with electroencephalography (EEG).
During the awake state, these oscillations usually have high frequencies and low amplitudes, but as anesthetic drugs are given, they shift to low frequencies and high amplitudes. These changes disrupt normal communication between different brain regions, leading to loss of consciousness.
Brown has also shown that these EEG oscillations can be used to monitor whether a patient is too deeply unconscious, and he has developed a closed-loop anesthesia delivery system that can monitor these oscillations in real-time and guide anesthetic dosing during surgery.
In 2024, Brown was presented with the National Medal of Science. Among his other awards, he is also a recipient of a National Institute of Health Director’s Pioneer Award, the Gruber Prize in Neuroscience, and the Swartz Prize for Computational and Theoretical Neuroscience. He one of a small group of researchers to be an elected member of all three National Academies of Medicine, Sciences, and Engineering, as well as the National Academy of Inventors.
From 2012 to 2022, he served as co-director of the Harvard-MIT Program in Health Sciences and Technology. He has also played an instrumental role in several important efforts at MIT, including the 2010 Report on the Initiative for Faculty Race and Diversity, and the founding of the MIT Institute for Data, Systems, and Society (IDSS) in 2015.
Outside of his work at MIT, Brown served on President Obama’s Brain Initiative Working Group, as well as the National Academy of Sciences Committee on Women in Science and Engineering and the Council of the National Institutes of Neurological Disorders and Stroke.
Brown is also known for his commitment to teaching and mentoring students. In 2024, he was named a recipient of MIT’s “Committed to Caring” award — an honor given by MIT’s Office of Graduate Education to faculty members who have served as exceptional mentors to graduate students.
Daniel Hastings
When Hastings, the Cecil (1923) and Ida Green Professor in Education, was notified of the new distinction, it came as a total surprise.
“The people who were there will tell you that I could not believe it at first,” he says. “I never thought of myself as being in the same league as some of the Institute Professors I knew.”
Hastings grew up in England and Jamaica, and developed an early fascination with space, as a fan of the fictional “Star Trek,” and later “Star Wars” and “Stargate” (he’s seen every episode and movie of all three franchises), as well as the very real NASA Apollo program.
After receiving a bachelor’s degree in mathematics from Oxford University, he enrolled at MIT, earning his master’s degree in 1978 and PhD in 1980, both in aeronautics and astronautics. In 1985 he joined the faculty as an assistant professor and was promoted to full professor in 1993.
Throughout his tenure, Hastings has made significant and lasting impacts in astronautical engineering, particularly through his studies in space plasma environment interactions, electric propulsion, and space systems architecture.
His early research on the physical interactions between plasma and spacecraft, for which he co-wrote the definitive text (“Spacecraft Environment Interactions,” published in 1996), enabled the safe operation of solar panels on spacecraft today. Prior to Hastings’ work, high voltage solar arrays on satellites often experienced catastrophic arcing — a dangerous jumping of electrical current from one panel to another. These failures turned out to be a result of interactions with the surrounding space plasma.
Hastings developed theories to characterize these interactions. His theories informed NASA’s design of the solar panels to power the International Space Station, which are still in operation today. His work also established guidelines across the aerospace industry on the design of resilient solar panels and ways to handle issues once in orbit.
In his studies of electric propulsion, Hastings characterized the fundamental physical interactions between ion engine plumes and spacecraft systems. His work was pivotal in incorporating ion propulsion systems into many commercial satellites and deep space probes and helped to push what was an experimental technology into mainstream use in space propulsion.
In his more recent work, Hastings has explored the concept of flexible and distributed space architectures. He and his students are developing models for spacecraft that can serve purposes beyond their original mission intent. For instance, a spacecraft may incorporate a port that could serve as a waystation for future satellites to dock and refuel. Such a flexible and distributed system could help to support future missions to the moon and Mars.
In recognition of his research contributions, Hastings received the AIAA Losey Atmospheric Sciences Award in 2002, was elected to the National Academy of Engineering in 2017, and was recognized as an honorary fellow of the American Institute of Aeronautics and Astronautics (AIAA) in 2021.
Throughout his career, Hastings has taken on numerous leadership roles, at the national, international, and Institute levels. Shortly after becoming full professor, he served as associate department head of research in MIT’s Department of Aeronautics and Astronautics (AeroAstro). He then took a two-year leave from the Institute to serve as chief scientist of the U.S. Air Force. During that time, he advised the Air Force chief of staff and secretary and successfully strengthened investments in space research in the U.S. Air Force space program.
Hastings has served as an advisor on multiple expert panels and boards, including as the chair of the Air Force Scientific Advisory Board, and as a member of the NASA Advisory Council, the National Science Board, the Intelligence Science Board, and most recently, the Defense Science Board and User Advisory Group of the National Space Council. He has also chaired multiple National Research Council studies and advised the space and engineering industries in various capacities, including serving on the boards of the Aerospace Corporation, Draper, and Blue Origin. He has just finished a two-year term as president of the American Institute of Aeronautics and Astronautics.
At MIT, Hastings has stepped up to serve in pivotal leadership posts. From 2000 to 2005, he served as the director of MIT’s Technology and Policy Program, then director of the Engineering Systems Division. From 2006 to 2013, as dean for undergraduate education, he helped to develop initiatives in equity, financial aid, and curriculum development, and strengthened international education and study abroad programs during a nationally challenging economic period. He received the Gordon Y. Billard Award in 2013 for his work on international education. In 2014 he began a five-year term as director of the Singapore-MIT Alliance for Research and Technology, during which he worked to reinforce MIT’s global collaborations. And from 2019 to 2023 he served as head of AeroAstro, supporting new research and educational initiatives as he navigated the department through the global pandemic.
Hastings has also worked in multiple capacities to make the Institute a more welcoming and inclusive community. He has served as associate dean of engineering for diversity, equity, and inclusion (2021-2023), Institute Community and Equity Officer (interim, 2023-2024), and co-chair of the MIT Values Statement Committee, as well as vice chancellor for undergraduate and graduate education (interim, 2024-2025).
“MIT has been a great place for me,” Hastings reflects. “It has a mission to address some of the most pressing problems in the world. It is a high-energy place. This is a place that I am excited to work in and I want to give back to make it better.”
Douglas Lauffenburger
Lauffenburger, who is the Ford Professor of Biological Engineering, Chemical Engineering, and Biology, was the central founder of MIT’s Department of Biological Engineering, which he chaired from its inception in 1998 until 2019.
Before coming to MIT, Lauffenburger earned his undergraduate degree from the University of Illinois at Urbana-Champaign in 1975 and a PhD from the University of Minnesota at the Twin Cities in 1979, both in chemical engineering.
While in graduate school, he became fascinated by the biological sciences. Early in his career, as a faculty member at the University of Pennsylvania and at the University of Illinois, his research and teaching straddled the line between chemical engineering and cell biology. Due to his unique background, MIT recruited Lauffenburger in the late 1990s to launch its new Department of Biological Engineering.
At the time, many universities had programs in biomedical engineering — an interdisciplinary field that applies techniques from electrical, chemical, or mechanical engineering to medical problems. Lauffenburger envisioned a distinct discipline of biological engineering, in which engineers would pursue an understanding of how biological systems function at the level of molecular and cellular mechanisms, with the goal of manipulating them to create new technologies for applications across medicine, energy, the environment, nutrition, and manufacturing.
“What was clear to me was that because biological systems comprise molecular processes, which are integrated in very complex ways, a true engineering analysis and design approach ought to be useful in moving it beyond mere tinkering and trial-and-error,” he says. “We needed to develop engineering frameworks for biology based on design principles, models, and predictions.”
As department head, Lauffenburger guided the development of new curricula at both graduate and undergraduate levels, and recruited faculty members whose work spanned engineering, molecular and cellular biology, microbiology, and immunology. The new department began offering graduate degrees in the late 1990s, and an undergraduate major beginning in 2005. Since its inception, the program has served as a model for similar programs at many other institutions worldwide.
Lauffenburger described being named an Institute Professor as “an honor that is especially gratifying because it recognizes the extraordinary impact of our unique MIT biological engineering department. I’ve been blessed with the rare opportunity to help create something revolutionary, here in this remarkable institution.”
Lauffenburger also played key roles in launching new interdisciplinary programs within MIT and with other institutions, including the Center for Biomedical Engineering, the Computational and Systems Biology Initiative, the DuPont-MIT Alliance, and the Cambridge-MIT Initiative.
His research has touched on many areas of biological science, including molecular cell biology, systems biology, and computational biology. Much of his work focuses on unraveling cell signaling mechanisms, using a combination of computational modeling and quantitative experiments. This work has shed light on processes such as cell proliferation, death, adhesion, and migration.
In the field of systems biology, he has created computational models across a spectrum of mathematical approaches, which can be used to identify drug targets and patient stratification strategies for a variety of diseases, including cancer and chronic inflammation, and predict the efficacy of drugs against those targets.
In 2021, he and Linda Griffith, the School of Engineering Professor of Teaching Innovation at MIT, were jointly awarded the Bernard M. Gordon Prize for Innovation in Engineering and Technology Education, the most prestigious engineering education award in the United States.
Lauffenburger is an elected member of the National Academy of Engineering and the American Academy of Arts and Sciences. He is a fellow of the American Association for the Advancement of Science, a founding fellow of the American Institute for Medical and Biological Engineering, and has served as president of the Biomedical Engineering Society.
How an influx of salt may affect microbial ecosystemsAs sea levels rise and saltwater seeps into freshwater, stressed aquatic populations may retain overall growth even as diversity declines, MIT scientists find.As sea levels rise due to climate change, encroaching sea water will likely make freshwater environments saltier. In a new study, MIT researchers have shown how that increase in salinity might affect microbial ecosystems found in environments such as rivers and estuaries.
These microbial communities play important roles in the carbon cycle, and they also help to decompose organic matter such as algae. The MIT team found that when salt levels rise, these populations lose diversity as faster-growing strains tend to take over the community, but they maintain their overall growth rate.
“At higher salinity, you lose diversity, which is ultimately not good for an ecosystem. But what we were surprised at is that in the meantime, even though diversity decreases, the growth of the community and the production of biomass is not impacted that much,” says Jana Huisman, an MIT postdoc and the lead author of the new study.
Jeff Gore, an MIT professor of physics, is the senior author of the paper, which appears today in Nature Microbiology. Martina Dal Bello, a former MIT postdoc who is now an assistant professor of ecology and evolutionary biology at Yale University, is also an author of the study.
Rising salt levels
Microbes that live in aquatic environments are typically adapted to thrive in fresh or salt water, or somewhere in between. Microbes that live in higher salt environments have cell walls that are optimized to resist osmotic pressure, and membrane transporters that can pump sodium ions out of the cell.
Freshwater lakes and rivers have salt concentrations around 1 gram of salt per liter of water (g/L), while oceans can reach 35 g/L. As the climate warms and sea levels rise, those oceanic waters may seep into estuaries and other inland bodies of water, increasing their salinity.
“When you think about climate change, you can think about rising temperatures, which is very common, but also a lot of other environmental stresses are going to increase,” Huisman says.
Huisman is from the Netherlands, a country with an extensive coastal delta, and she was interested in exploring how changes in salinity might affect microbial ecosystems in those aquatic habitats. The new study builds on previous work from Gore’s lab showing that higher seawater temperatures tend to favor slower-growing bacteria.
For the new study, the researchers took samples from three aquatic environments with varying salinity: the Charles River near the MIT Sailing Pavilion (4 g/L), Boston Harbor (30 g/L), and a beach in Nahant, Massachusetts (35 g/L). Each community contained hundreds of species of microbes. The researchers then grew each population in three environments of varying salinity — 16, 31, or 46 g/L.
Over two weeks, the researchers measured the communities’ growth rates and found that overall, each community maintained the same growth rate at each of the three concentrations. However, in the communities exposed to higher salt environments, the overall composition became less diverse. Further studies showed that these communities tended to be dominated by faster-growing species.
“We saw that those communities that had been propagated at higher salinity had reached a markedly different composition than the ones that lower salinity,” Huisman says.
Natural ecosystems
To explore whether their lab results might correspond to what happens in natural ecosystems, the researchers analyzed publicly available genomic data from microbes found in different aquatic ecosystems, including the Chesapeake Bay, the Gulf of Mexico, and the Baltic Sea.
For this portion of the study, the researchers focused on a genetic marker called the 16S rRNA gene copy number, which can be used as a proxy for the maximum growth rate that a species can attain. The more copies of this gene that a species has, the faster its intrinsic growth rate.
The researchers found that in these natural communities, environments with higher salinity also tended to be dominated by faster-growing species.
“When we first saw that, it was very exciting — that, indeed, what we found in the lab seems to also be represented in data from natural communities, sampled across a range of different environments,” Huisman says. “You see the same signatures in such data, and that’s highly suggestive that what we found in the lab might also be true in natural environments.”
One potential drawback to this loss of diversity is a reduction in microbial populations’ ability to withstand other types of environmental stress, the researchers say.
In this study, the researchers did not investigate the functions of the individual bacterial strains that ended up becoming more prevalent. Some of them may play beneficial roles, but it’s also possible that some of them might be pathogenic strains.
“Whether you want faster-growing species to take over or not might also be related to what the identity of those species is. That is something that I’m interested in looking at in the future,” Huisman says.
The research was funded by a Human Frontier Science Program Fellowship and a Schmidt Science Polymath Award.