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Can AI help predict which heart-failure patients will worsen within a year?

Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.

Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient’s lungs, legs, feet, and other parts of the body. The condition is chronic and incurable, often leading to arrhythmias or sudden cardiac arrest. For many centuries, bloodletting and leeches were the treatment of choice, famously practiced by barber surgeons in Europe, during a time when physicians rarely operated on patients. 

In the 21st century, the management of heart failure has become decidedly less medieval: Today, patients undergo a combination of healthy lifestyle changes, prescription of medications, and sometimes use pacemakers. Yet heart failure remains one of the leading causes of morbidity and mortality, placing a substantial burden on health-care systems across the globe. 

“About half of the people diagnosed with heart failure will die within five years of diagnosis,” says Teya Bergamaschi, an MIT PhD student in the lab of Nina T. and Robert H. Rubin Professor Collin Stultz and the co-first author of a new paper introducing a deep learning model for predicting heart failure. “Understanding how a patient will fare after hospitalization is really important in allocating finite resources.”

The paper, published in Lancet eClinical Medicine by a team of researchers at MIT, Mass General Brigham, and Harvard Medical School, shares results from developing and testing PULSE-HF, which stands loosely for “Predict changes in left ventricULar Systolic function from ECGs of patients who have Heart Failure.” The project was conducted in Stultz’s lab, which is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health. Developed and retrospectively tested across three different patient cohorts from Massachusetts General Hospital, Brigham and Women’s Hospital, and MIMIC-IV (a publicly available dataset), the deep learning model accurately predicts changes in the left ventricular ejection fraction (LVEF), which is the percentage of blood being pumped out of the left ventricle of the heart.

A healthy human heart pumps out about 50 to 70 percent of blood from the left ventricle with each beat — anything less is considered a sign of a potential problem. “The model takes an [electrocardiogram] and outputs a prediction of whether or not there will be an ejection fraction within the next year that falls below 40 percent,” says Tiffany Yau, an MIT PhD student in Stultz’s lab who is also co-first author of the PULSE-HF paper. “That is the most severe subgroup of heart failure.” 

If PULSE-HF predicts that a patient’s ejection fraction is likely to worsen within a year, the clinician can prioritize the patient for follow-up. Subsequently, lower-risk patients can reduce their number of hospital visits and the amount of time spent getting 10 electrodes adhered to their body for a 12-lead ECG. The model can also be deployed in low-resource clinical settings, including doctors offices in rural areas that don’t typically have a cardiac sonographer employed to run ultrasounds on a daily basis.

“The biggest thing that distinguishes [PULSE-HF] from other heart failure ECG methods is instead of detection, it does forecasting,” says Yau. The paper notes that to date, no other methods exist for predicting future LVEF decline among patients with heart failure.

During the testing and validation process, the researchers used a metric known as "area under the receiver operating characteristic curve" (AUROC) to measure PULSE-HF’s performance. AUROC is typically used to measure a model’s ability to discriminate between classes on a scale from 0 to 1, with 0.5 being random and 1 being perfect. PULSE-HF achieved AUROCs ranging from 0.87 to 0.91 across all three patient cohorts.

Notably, the researchers also built a version of PULSE-HF for single-lead ECGs, meaning only one electrode needs to be placed on the body. While 12-lead ECGs are generally considered superior for being more comprehensive and accurate, the performance of the single-lead version of PULSE-HF was just as strong as the 12-lead version.

Despite the elegant simplicity behind the idea of PULSE-HF, like most clinical AI research, it belies a laborious execution. “It’s taken years [to complete this project],” Bergamaschi recalls. “It’s gone through many iterations.” 

One of the team’s biggest challenges was collecting, processing, and cleaning the ECG and echocardiogram datasets. While the model aims to forecast a patient’s ejection fraction, the labels for the training data weren’t always readily available. Much like a student learning from a textbook with an answer key, labeling is critical for helping machine-learning models correctly identify patterns in data.

Clean, linear text in the form of TXT files typically works best when training models. But echocardiogram files typically come in the form of PDFs, and when PDFs are converted to TXT files, the text (which gets broken up by line breaks and formatting) becomes difficult for the model to read. The unpredictable nature of real-life scenarios, like a restless patient or a loose lead, also marred the data. “There are a lot of signal artifacts that need to be cleaned,” Bergamaschi says. “It’s kind of a never-ending rabbit hole.”

While Bergamaschi and Yau acknowledge that more complicated methods could help filter the data for better signals, there is a limit to the usefulness of these approaches. “At what point do you stop?” Yau asks. “You have to think about the use case — is it easiest to have this model that works on data that is slightly messy? Because it probably will be.”

The researchers anticipate that the next step for PULSE-HF will be testing the model in a prospective study on real patients, whose future ejection fraction is unknown.

Despite the challenges inherent to bringing clinical AI tools like PULSE-HF over the finish line, including the possible risk of prolonging a PhD by another year, the students feel that the years of hard work were worthwhile. 

“I think things are rewarding partially because they’re challenging,” Bergamaschi says. “A friend said to me, ‘If you think you will find your calling after graduation, if your calling is truly calling, it will be there in the one additional year it takes you to graduate.’ … The way we’re measured as researchers in [the ML and health] space is different from other researchers in ML space. Everyone in this community understands the unique challenges that exist here.”

“There’s too much suffering in the world,” says Yau, who joined Stultz’s lab after a health event made her realize the importance of machine learning in health care. “Anything that tries to ease suffering is something that I would consider a valuable use of my time.” 

Discovering the joy of future-forward electrical engineering

One year in, MIT’s hands-on 6-5 (Electrical Engineering With Computing) degree program is already one of the most popular majors among first-year students.

“It’s a real validation of all the work behind the scenes,” says Karl Berggren, faculty head of electrical engineering within the MIT Department of Electrical Engineering and Computer Science (EECS). He’s looking at the numbers of new enrollees in Course 6-5, Electrical Engineering With Computing, the flagship electrical engineering degree offered by EECS, which was launched last fall. 

The new major has been embraced by the MIT student community. “The fact that Course 6-5 is now the third-most selected major among first-year students shows that the department is clearly meeting a growing need for a curriculum that bridges electrical engineering and computing. This growth is coming from students already interested in pursuing a degree in EECS,” says Anantha Chandrakasan, MIT’s provost. “The major was thoughtfully designed to offer a strong foundation in core electrical engineering concepts — such as circuits, signals, systems, and architecture — while also providing well-structured specialization tracks that prepare students for the future of the field.”

Those tracks include structured paths to explore not only the traditional domains of electrical engineering (such as hardware design and energy systems), but cutting-edge fields such as nanoelectronics, quantum systems engineering, and photonics. 

“They are very flexible, and essentially allow me to take whatever I want, with the tracks filling up almost automatically,” says 6-5 major Charles Reischer. “For me, it essentially reduces the amount of specific required classes in the major, which has been helpful for choosing the classes I find interesting.” 

Jelena Notaros, who helped develop the Electromagnetics and Photonics track within the new major, has seen the new wave of student interest from the other side. “It’s been incredibly rewarding … I think students are excited to have the opportunity to take a class where they can learn about a cutting-edge field and test real state-of-the-art chip hardware using industry-standard equipment.” Notaros’s class, 6.2320  (Silicon Photonics), includes features not found in a university class anywhere else, such as a sequence in which students can test actual chips at three electronic-photonic probe stations. 

Another 6-5 track, Quantum Systems Engineering, features direct student access to quantum hardware, including electron-nuclear systems and state-of-the-art simulations methods and tools. Professor Dirk Englund, who teaches multiple courses within the track, explains, “it’s been so successful in part through strong industry support, including from QuTools Inc. Students work with the same tech we use in the Boston-Area Quantum Network Testbed — the metro quantum network linking MIT, Lincoln Lab, and Harvard, and the NSF CQN.” 

Many of Englund’s students have gone on to pursue a career in quantum information science, either in grad school or in industry. “Students recognize quantum engineering is the future. They see they’re building the foundation for metro-scale quantum networks.” 

The new curriculum’s emphasis on hands-on learning is deliberate, and ubiquitous throughout 6-5. Within the Circuits track, students who enroll in class 6.208 (Semiconductor Electronic Circuits) will get an opportunity not only to design a circuit, but to actually see their design made, in a process called “tape-out.” Professor Ruonan Han, who helped design the course, explains, “a tape-out is a perfect training that poses [real-life] constraints and forces the students to solve practical engineering problems. Through circuit simulation using mainstream industry CAD tools, the students better understand how deep-scaled transistors differ from the ideal behaviors taught in textbooks. By drawing the layouts of the silicon and metal patterns, the students learn how a modern chip is made, layer by layer. The complex (and often frustrating) rules of the layout also keep reminding the students of all the technical limitations during the chip manufacturing, and make them better appreciate all the accomplishments in semiconductor manufacturing. Even the firm and non-negotiable tape-out submission deadline forces the students to not only wisely manage their development timeline, but also to experience heart-beating moments when decisions on critical engineering trade-offs should be made (in order to deliver). To these students, it was such relentless efforts that gave them lots of satisfaction and pride when they finally hold their own chips in hand.” 

The sense of completing a full problem-solving cycle is echoed in class 6.900 (Engineering for Impact), a capstone course designed by Professor Joel Voldman, a former faculty head of electrical engineering, along with Senior Lecturer Joe Steinmeyer. Over the course of a semester, students team with city governments and nonprofits to solve complex local issues. The course is designed not only to introduce students to realistic project management factors (such as budgets, timelines, and stakeholders), but also to give them a taste of the satisfaction of engineering a solution that meets a real community’s need. 

“I’ve taken 6.900, and it’s been eye-opening in the collaboration of hardware, firmware, and software to create a cohesive and working product,” says Andrea Leang, a senior majoring in 6-2 who nonetheless decided to try the new course. “In my 6-2 experience, I spent the first two years taking more CS [computer science] classes, but as I went into junior year, I wanted to explore more EE [electrical engineering].” That desire led Leang to Voltage, the student group for electrical engineers. “Honestly, it was the first big community of EE I’ve joined. Joining Voltage opened my eyes to what MIT had to offer on EE, and a community who was enthusiastic to share their knowledge.” 

Matthew Kim, one of the executives of the Voltage group, echoes Leang’s experience. “​​It has been great working [...] to build a community for EE. We heard faculty say that they wanted to be more engaged with students and communicate more, and it has definitely been felt with the restart and support of Voltage. And I’m hopeful that the community will continue to grow.” 

That growth has been rapid. The new major’s enrollment is now roughly equivalent to the combined enrollment in the older 6-1 and 6-2 programs, showing the desirability of a major that incorporates fundamentals of both computing and electrical engineering. 

Department head Professor Asu Ozdaglar is thrilled with the energizing effect of the new major. “We are delighted to see the initial success of the 6-5 major, which provides our students an exciting and forward-looking curriculum, developed through extensive work and great deal of thought by electrical engineering faculty. The new curriculum reflects the critical role computing plays in electrical engineering, whether in designing new devices and circuits, analyzing data, or in studying complex systems, which almost invariably combine hardware and software."

“What excites me most about this major is how it empowers students to bring ideas to life — from the invisible signals that connect our world to the complex systems that drive modern technology,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Professor of Electrical Engineering and Computer Science. “Students are using computation as a creative and analytical tool to expand the boundaries of engineering. They gain a deep understanding of how hardware and software come together to drive technological progress.” 

The new degree program’s designers are gratified by the swell of student interest. 

“The buzz surrounding the classes and the new 6-5 degree program is fantastic,” says Voldman. “It’s great to see the strong student interest in what we’ve put together.” 

3 Questions: Fortifying our planetary defenses

MIT astronomers are developing a new way to detect, monitor, and mitigate the threats posed by smaller asteroids to our critical space infrastructure.

When people think of asteroids, they tend to picture rare, civilization-ending impacts like those depicted in movies such as “Armageddon.” In reality, the asteroids most likely to affect modern society are much smaller. While kilometer-scale impacts occur only every tens of millions of years, decameter-scale (building-sized) objects strike Earth far more frequently: roughly every couple decades. As astronomers develop new ways to detect and track these smaller asteroids, planetary defense becomes increasingly relevant for protecting the space-based infrastructure that underpins modern life, from GPS navigation to global communications.

The good news for us earthlings is that a team of MIT researchers is on this space-case. Associate Professor Julien de Wit, Research Scientist Artem Burdanov, and their colleagues recently developed a new asteroid-detection method that could be used to track potential asteroid impactors and help protect our planet. They have now applied this new technique to the James Webb Space Telescope (JWST), demonstrating that JWST can be used to detect and characterize decameter-scale asteroids all the way out to the main belt, a crucial step in fortifying our planetary safety and security. De Wit and his colleagues recently co-led with with Andrew Rivkin PhD ’91 new observations of an asteroid called 2024 YR4, which made headlines last year when it was first discovered. They were able to determine that the asteroid will not collide with the Moon, which could have had impacts on Earth’s critical satellite systems.

De Wit, Burdanov, Assistant Professor Richard Teague, and Research Scientist Saverio Cambioni spoke to MIT News about the importance of planetary defense and how MIT astronomers are helping to lead the charge to ensure our planet’s safety.

Q: What is planetary defense and how is the field changing?

Burdanov: Planetary defense is a field of science and engineering that’s focused on preventing asteroids and comets from hitting the Earth. While traditionally the field has been focused on much larger asteroids, thanks to new observational capabilities the field is growing to include monitoring much smaller asteroids that could also have an impact.

De Wit: When people think about asteroids they tend to think of impacts along the lines of these rare, civilization-ending “dinosaur killer” asteroids — objects that are scientifically fascinating but, happily, statistically unlikely on human timescales. But as soon as you move to smaller asteroids, there are so many of them that you’re looking at impacts happening every few decades or less. That becomes much more relevant on human timescales.

Now that our society has become increasingly reliant on space-based infrastructure for communication, navigation technologies like GPS and satellite-based security systems, we can be affected by different populations of smaller asteroids. These smaller asteroids will probably lead to zero direct human casualties but would have very different consequences on our space infrastructure. At the same time, because they are smaller, they require different technologies to monitor and understand them, both for the detection and for the characterization. At MIT, we are working to redefine planetary defense in a way that is far more pertinent, personable, and practical — focusing on these much smaller asteroids that could have real consequences. In other words, planetary defense is no longer just about avoiding extinction-level events. It is about protecting the systems we depend on in the near term.

Q: Why are observations with telescopes like the James Webb Space Telescope (JWST) so important to keeping our planet safe?

Teague: We’re entering a time now where we have these large-scale sky surveys that are going to be producing an incredible amount of data. We’re trying to develop the framework here at MIT where we can sift through that data as quickly and efficiently as possible, and then use the resources that we have available, such as the optical and radio observatories that we run like the MIT Haystack and Wallace Observatories, to follow up on those potential threats as quickly as possible and determine whether they could be problematic.

We’ve been doing trial observations to try and piece together how fast we can do this. The challenging thing is that the smaller objects that we’ve been talking about, the decameter ones, they’re really hard to detect from the ground. They’re just so small, and so that’s why we really need to use space-based facilities like JWST to help keep our planet safe. JWST is just incomparable, really, for detecting these very small, faint objects. A lot of our work at the moment at MIT is trying to understand is how do we build that entire pipeline ­— from detection to risk assessment to mitigation — under one roof to make it as efficient as possible. And I think this is a really MIT-type of problem to solve. There’s not many places that have the same range of experts in astronomy and engineering and technology to really tackle this properly. It’s really exciting that MIT hosts all these sorts of experts that we’re bringing together to solve this problem and keep our planet safer.

Cambioni: There is going to be what I like to call an asteroid revolution coming up because in addition to JWST’s observational capabilities, there is a new observatory in Chile called the Vera Rubin Observatory that could increase the detection of known small objects in space by a factor of 10. The most important thing to keep in mind, though, is that this observatory will detect the objects but may lose a lot of them. This is where a part of our work is coming in, to basically follow that object and map it as soon as possible. Additionally, Vera Rubin only looks at the reflected light, and it doesn’t get a precise estimate of an asteroid’s size. This gap between detection and characterization is a fundamental problem of asteroid science, between how many objects we discover and how fast we can characterize them. At MIT, we are using our in-house capabilities to help characterize these objects. That includes the MIT Wallace Observatory and the MIT Haystack Observatory.

2026 MacVicar Faculty Fellows named

MIT professors Amos Winter and Nikolai Zeldovich are honored for exceptional undergraduate teaching.

Two outstanding MIT educators have been named MacVicar Faculty Fellows: professor of mechanical engineering Amos Winter and professor of electrical engineering and computer science Nickolai Zeldovich.

For more than 30 years, the MacVicar Faculty Fellows Program has recognized exemplary and sustained contributions to undergraduate education at MIT. The program is named in honor of Margaret MacVicar, MIT’s first dean for undergraduate education and founder of the Undergraduate Research Opportunities Program (UROP). Fellows are chosen through an annual and highly competitive nomination process. The Registrar’s Office coordinates and administers the award on behalf of the Division of Graduate and Undergraduate Education. Nominations are reviewed by an advisory committee, and the provost selects the fellows.

Amos Winter: Bringing excitement to the classroom

Amos Winter is the Germeshausen Professor in the Department of Mechanical Engineering (MechE). He joined the faculty in 2012 and is best known for teaching class 2.007 (Design and Manufacturing I).

A hallmark of Winter’s pedagogy is the way he connects technical learning and core engineering science with real-world impacts. His approach keeps students actively engaged and encourages critical thinking while developing their competence and confidence as design engineers. Current graduate student Ariel Mobius ’24 writes, “Professor Winter is a transformative educator. He successfully blends rigorous technical instruction with lessons on problem scoping and hands-on learning and backs it all up with personalized mentorship. He is a committed advocate for his students and has fundamentally shaped my path as a mechanical engineer.”

Especially notable is Winter’s energetic style and use of interactive materials and demonstrations to make fundamental topics tangible. “He wheels in a large steamer trunk filled with demos he has built or collected to illustrate the day’s topic,” writes Class of 1948 Career Development Professor and assistant professor of mechanical engineering Kaitlyn Becker. “Some demos are enduring classics and others newly designed each year.” Through his “Gearhead Moment of Zen” Winter will share an astonishing car stunt to explain the mechanics using course material. “The theatrics stay in students’ minds,” says Becker, highlighting how Winter’s dramatic examples reinforce learning.

These techniques, combined with a supportive culture, allowed Winter to transform 2.007 from a core class and first subject in engineering design into a celebration of student effort and learning. Throughout the term, students learn how to design and build objects culminating in a robot competition in which their creations tackle themed challenges on a life-size game board. In the past, fewer than half the students were able to compete and today, boosted by Winter’s mentorship and enthusiasm, nearly 97 percent finish a competition-ready robot.

Ralph E. and Eloise F. Cross Professor of Mechanical Engineering David Hardt writes, “Thanks to Amos, this subject has become transformative for many MechE undergraduates.” Becker concurs: “He is the heart and captain of the 2.007 ‘cheer squad,’ cultivating a caring and motivated teaching team.”

Current graduate student Aidan Salazar ’25 notes, “His teaching philosophy is grounded in empowerment: he encourages students to take risks when designing while giving them the confidence and support needed to do so with thoughtful engineering analysis.”

Winter is also deeply invested in students’ growth outside the classroom. He serves as faculty supervisor for MIT’s Formula SAE (Society of Automotive Engineers) and Solar Car teams and guides related UROP projects. In fall 2025 alone, he advised nearly 50 UROP students from the teams, demonstrating his commitment to experiential learning and ability to mentor students at scale.

Salazar continues: “He has offered extraordinary contributions in helping MIT undergraduates embody the Institute’s ‘mens-et-manus’ [‘mind-and-hand’] motto, and I am grateful to be one of the individuals shaped by his teaching.”

“I have always looked up to my colleagues who are MacVicar Fellows as the best educators at the Institute,” writes Winter. “What makes this acknowledgement even more special to me is by earning it from teaching 2.007, which I often cite as one of the best parts of my job. The class is where most mechanical engineering undergraduates gain their first real engineering experience by physically realizing a machine of their own conception. It has been extremely gratifying to watch a generation of students translate their knowledge of engineering and design from the class into their careers … I am honored to have played a role in their intellectual growth and done so meaningfully enough to be recognized as a MacVicar Fellow.”

Nickolai Zeldovich: Inspiring independent thinkers and future teachers

Nickolai Zeldovich is the Joan and Irwin M. (1957) Jacobs Professor of Electrical Engineering and Computer Science (EECS). Student testimonials highlight his unique ability to activate their problem-solving skills, cultivate their intellectual curiosity, and infuse learning with joy.

Katarina Cheng ’25 writes, “From my first day of lecture in the course, I was immediately drawn in by Professor Zeldovich’s joy and enthusiasm for every facet of security and its power,” and Rotem Hemo ’17, ’18 says that Zeldovich “empowers students to find solutions themselves.”

Yael Tauman Kalai, the Ellen Swallow Richards (1873) Professor and professor of EECS concurs. She notes that his lectures — with back-and-forth discussion and probing questions — encourage independent thinking and ensure that “everyone feels a little smarter at the end. It is not surprising that students love him.”

Zeldovich’s affinity for problem-solving translates to his curricular work as well. When he arrived at MIT in 2008, Course 6 offered classes in theoretical and applied cryptography, but lacked a dedicated systems security subject. Recognizing this as a significant gap, Zeldovich took it upon himself to create class 6.566/6.858 (Computer Systems Security) in 2009. Since then, the subject has become a central part of the curriculum, but sustained interest from undergraduates revealed another need, and in 2021 he partnered with colleagues to create a dedicated introductory course: 6.1600 (Foundations of Computer Security).

Edwin Sibley Webster Professor of EECS Srini Devadas writes: “What our curriculum was sorely in need of was a systems security class, and Nickolai immediately and single-handedly created [it],” and has “taught this class to rave reviews ever since.”

The impact of Zeldovich’s thoughtful, inquiry-driven approach to pedagogy extends beyond the walls of his classroom, inspiring future educators, teaching assistants (TAs), and even his faculty colleagues at MIT.

Henry Corrigan-Gibbs, the Douglas Ross (1954) Career Development Professor of Software Technology and associate professor of computer science, writes that Zeldovich has “proven himself to be a dedicated teacher of teachers … One of the things that makes teaching with Nickolai so much fun is that he shares his passion with the undergraduates and MEng students who join the course staff as TAs.”

“[He] encourages the TAs to contribute their own creative ideas to the course,” continues Corrigan-Gibbs. “It should not be a surprise then that 100% of the TAs that we have had in our class have signed up to teach with Nickolai again.”

“Due, in no small part, to how I saw Nickolai lead his classroom, I was inspired to become an educator myself,” writes MIT alumna Anna Arpaci-Dusseau ’23, SM ’24. “I saw that the role of an instructor is not only to teach, but to innovate by thinking of creative projects, and to connect by listening to students’ concerns. As I go forward in my career, I am grateful to have such a wonderful example of an educator to look up to.”

Kalai adds, “I have learned a great deal from the two times that I have ‘taken’ (part of) the class from Nickolai. His extensive knowledge and experience are evident in every lecture. There is so much variety to Nickolai’s teaching.”

Nickolai Zeldovich is the recipient of numerous awards including the EECS Spira Teaching Award (2013), the Edgerton Faculty Achievement Award (2014), the EECS Faculty Research Innovation Fellowship (2018), and the EECS Jamieson Award for Excellence in Teaching (2024).

On receiving this award, Zeldovich says, “MIT has a culture of strong undergraduate education, so being selected as a MacVicar Fellow was truly an honor. It’s a joy to teach smart students about computer systems, and the tradition of co-teaching classes in the EECS department helped me improve as a teacher. Most of all, I look forward to continuing to teach MIT’s students!”

Learn more about the MacVicar Faculty Fellows Program on the Registrar’s Office website. 

3 Questions: On the future of AI and the mathematical and physical sciences

Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power. 

Today, artificial intelligence and science find themselves at a similar inflection point. The current AI revolution has been fueled by decades of research in the mathematical and physical sciences (MPS), which provided the challenging problems, datasets, and insights that made modern AI possible. The 2024 Nobel Prizes in physics and chemistry, recognizing foundational AI methods rooted in physics and AI applications for protein design, made this connection impossible to miss.

In 2025, MIT hosted a Workshop on the Future of AI+MPS, funded by the National Science Foundation with support from the MIT School of Science and the MIT departments of Physics, Chemistry, and Mathematics. The workshop brought together leading AI and science researchers to chart how the MPS domains can best capitalize on — and contribute to — the future of AI. Now a white paper, with recommendations for funding agencies, institutions, and researchers, has been published in Machine Learning: Science and Technology. In this interview, Jesse Thaler, MIT professor of physics and chair of the workshop, describes key themes and how MIT is positioning itself to lead in AI and science.

Q: What are the report’s key themes regarding last year’s gathering of leaders across the mathematical and physical sciences?

A: Gathering so many researchers at the forefront of AI and science in one room was illuminating. Though the workshop participants came from five distinct scientific communities — astronomy, chemistry, materials science, mathematics, and physics — we found many similarities in how we are each engaging with AI. A real consensus emerged from our animated discussions: Coordinated investment in computing and data infrastructures, cross-disciplinary research techniques, and rigorous training can meaningfully advance both AI and science.

One of the central insights was that this has to be a two-way street. It’s not just about using AI to do better science; science can also make AI better. Scientists excel at distilling insights from complex systems, including neural networks, by uncovering underlying principles and emergent behaviors. We call this the “science of AI,” and it comes in three flavors: science driving AI, where scientific reasoning informs foundational AI approaches; science inspiring AI, where scientific challenges push the development of new algorithms; and science explaining AI, where scientific tools help illuminate how machine intelligence actually works.

In my own field of particle physics, for instance, researchers are developing real-time AI algorithms to handle the data deluge from collider experiments. This work has direct implications for discovering new physics, but the algorithms themselves turn out to be valuable well beyond our field. The workshop made clear that the science of AI should be a community priority — it has the potential to transform how we understand, develop, and control AI systems.

Of course, bridging science and AI requires people who can work across both worlds. Attendees consistently emphasized the need for “centaur scientists” — researchers with genuine interdisciplinary expertise. Supporting these polymaths at every career stage, from integrated undergraduate courses to interdisciplinary PhD programs to joint faculty hires, emerged as essential.

Q: How do MIT’s AI and science efforts align with the workshop recommendations?

A: The workshop framed its recommendations around three pillars: research, talent, and community. As director of the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) — a collaborative AI and physics effort among MIT and Harvard, Northeastern, and Tufts universities — I’ve seen firsthand how effective this framework can be. Scaling this up to MIT, we can see where progress is being made and where opportunities lie.

On the research front, MIT is already enabling AI-and-science work in both directions. Even a quick scroll through MIT News shows how individual researchers across the School of Science are pursuing AI-driven projects, building a pipeline of knowledge and surfacing new opportunities. At the same time, collaborative efforts like IAIFI and the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute concentrate interdisciplinary energy for greater impact. The MIT Generative AI Impact Consortium is also supporting application-driven AI work at the university scale.

To foster early-career AI-and-science talent, several initiatives are training the next generation of centaur scientists. The MIT Schwarzman College of Computing's Common Ground for Computing Education program helps students become “bilingual” in computing and their home discipline. Interdisciplinary PhD pathways are also gaining traction; IAIFI worked with the MIT Institute for Data, Systems, and Society to create one in physics, statistics, and data science, and about 10 percent of physics PhD students now opt for it — a number that's likely to grow. Dedicated postdoctoral roles like the IAIFI Fellowship and Tayebati Fellowship give early-career researchers the freedom to pursue interdisciplinary work. Funding centaur scientists and giving them space to build connections across domains, universities, and career stages has been transformative.

Finally, community-building ties it all together. From focused workshops to large symposia, organizing interdisciplinary events signals that AI and science isn’t siloed work — it’s an emerging field. MIT has the talent and resources to make a significant impact, and hosting these gatherings at multiple scales helps establish that leadership.

Q: What lessons can MIT draw about further advancing its AI-and-science efforts?

A: The workshop crystallized something important: The institutions that lead in AI and science will be the ones that think systematically, not piecemeal. Resources are finite, so priorities matter. Workshop attendees were clear about what becomes possible when an institution coordinates hires, research, and training around a cohesive strategy.

MIT is well positioned to build on what’s already underway with more structural initiatives — joint faculty lines across computing and scientific domains, expanded interdisciplinary degree pathways, and deliberate “science of AI” funding. We’re already seeing moves in this direction; this year, the MIT Schwarzman College of Computing and the Department of Physics are conducting their first-ever joint faculty search, which is exciting to see.

The virtuous cycle of AI and science has the potential to be truly transformative — offering deeper insight into AI, accelerating scientific discovery, and producing robust tools for both. By developing an intentional strategy, MIT will be well positioned to lead in, and benefit from, the coming waves of AI.

New MIT class uses anthropology to improve chatbots

MIT computer science students design AI chatbots to help young users become more social, and socially confident.

Young adults growing up in the attention economy — preparing for adult life, with social media and chatbots competing for their attention — can easily fall into unhealthy relationships with digital platforms. But what if chatbots weren’t mere distractions from real life? Could they be designed humanely, as moral partners whose digital goal is to be a social guide rather than an addictive escape?

At MIT, a friendship between two professors — one an anthropologist, the other a computer scientist — led to creation of an undergraduate class that set out to find the answer to those questions. Combining the two seemingly disparate disciplines, the class encourages students to design artificial intelligence chatbots in humane ways that help users improve themselves.

The class, 6.S061/21A.S02 (Humane User Experience Design, a.k.a. Humane UXD), is an upper-level computer science class cross-listed with anthropology. This unique cross-listing allows computer science majors to fulfill a humanities requirement while also pursuing their career objectives. The two professors use methods from linguistic anthropology to teach students how to integrate the interactional and interpersonal needs of humans into programming.

Professor Arvind Satyanarayan, a computer scientist whose research develops tools for interactive data visualization and user interfaces, and Professor Graham Jones, an anthropologist whose research focuses on communication, created Humane UXD last summer with a grant from the MIT Morningside Academy for Design (MAD). The MIT MAD Design Curriculum Program provides funding for faculty to develop new classes or enhance existing classes using innovative pedagogical approaches that transcend departmental boundaries. Alongside the grant provided by MAD, Jones and Satyanarayan received funding to develop Humane UXD under the auspices of the Common Ground for Computing Education, an initiative of the MIT Schwarzman College of Computing that brings together departments to create courses integrating computing with other disciplines. 

The Design Curriculum Program is currently accepting applications for the 2026-27 academic year; the deadline is Friday, March 20. 

Jones and Satyanarayan met several years ago when they co-advised a doctoral student’s research on data visualization for visually impaired people. They’ve since become close friends who can pretty much finish one another’s sentences.

“There’s a way in which you don’t really fully externalize what you know or how you think until you’re teaching,” Jones says. “So, it’s been really fun for me to see Arvind unfurl his expertise as a teacher in a way that lets me see how the pieces fit together — and discover underlying commonalities between our disciplines and our ways of thinking.”

Satyanarayan continues that thought: “One of the things I really enjoyed is the reciprocal version of what Graham said, which is that my field — human-computer interaction — inherited a lot of methods from anthropology, such as interviews and user studies and observation studies. And over the decades, those methods have gotten more and more watered down. As a result, a lot of things have been lost.

“For instance, it was very exciting for me to see how an anthropologist teaches students to interview people. It’s completely different than how I would do it. With my way, we lose the rapport and connection you need to build with your interview participant. Instead, we just extract data from them.”

For Jones’ part, teaching with a computer scientist holds another kind of allure: design. He says that human speech and interaction are organized into underlying genres with stable sets of rules that differentiate an interview at a cocktail party from a conversation at a funeral.

“ChatGPT and other large language models are trained on naturally occurring human communication, so they have all those genres inside them in a latent state, waiting to be activated,” he says.

“As a social scientist, I teach methods for analyzing human conversation, and give students very powerful tools to do that. But it ends up usually being an exercise in pure research, whereas this is a design class, where students are building real-world systems.”

The curriculum appears to be on target for preparing students for jobs after graduation. One student sought permission to miss class for a week because he had a trial internship at a chatbot startup; when he returned, he said his work at the startup was just like what he was learning in class. He got the job.

The sampling of group projects below, built with Google’s Gemini, demonstrates some of what’s possible when, as Jones says, “there’s a really deep intertwining of the technology piece with the humanities piece.” The students’ design work shows that entirely new ways of programming can be conceptualized when the humane is made a priority.

The bots demonstrate clearly that an interdisciplinary class can be designed in such a way that everyone benefits: Students learn more and differently; they can fulfill a non-major course requirement by taking a class that is directly beneficial to their careers; and long-term faculty partnerships can be forged or strengthened.

Team Pond

One project promises to be particularly useful for graduating seniors. Pond is designed to help young college graduates adapt to the challenges of independent adult life. Team Pond configured the chatbot not to simply parrot the user, or to sycophantically praise wrong answers. Instead, Pond provides advice to help with “adulting” (behaving as a responsible adult).

“Pond is built to be your companion from college life into post-college life, to help you in your transition from being a small fish in a small pond to being a small fish in a very big pond,” says sophomore Mary Feliz.

“College is very much a high-proximity and high-context environment, in the sense that everybody around you is going through the same thing, and it’s easy to build relationships or find opportunities, because there are structured pathways that you have access to,” explains graduate student Emaan Khan. “Post-grad life is low-context. You’re not always surrounded by your peers or your professors. It’s no-proximity also, in the sense that you don’t have opportunities at your doorstep. Pond is a tool to help empower you to access certain opportunities, or learn how to navigate.”

Pond’s developers are graduate student Claire Camacho, Harvard University graduate student Charlotte Cheah, Feliz, and Khan. They trained Pond to offer expertise in three areas: social life, professional life, and adult skills.

The students demonstrated Pond’s social communication by asking how to start conversations in a hobby group. Next, they exhibited Pond’s adulting communication by typing in a question.

“My landlord has asked me personal questions that make me uncomfortable. What should I do?”

Appearing in Pond’s textbox was this response: “Politely set boundaries. You are not obligated to answer personal questions. You can gently redirect the conversation back to property-related topics. Know your rights: familiarize yourself with tenant rights in your city or state regarding privacy and landlord conduct.”

When asked how to do that, Pond offered specific instructions: “You can try saying phrases like: ‘I prefer to keep our conversations focused on the property,’ and ‘let’s stick to topics related to the apartment, please.’ Always redirect the conversation back to the apartment or a legitimate maintenance issue. Keep your tone polite but firm. Document any conversations if needed.”

Pond also offered a role-playing scenario to help the user learn what polite-but-firm language might be in that situation.

“The ethos of the practice mode is that you are actively building a skill, so that after using Pond for some time, you feel confident that you can swim on your own,” Khan says. The chatbot uses a point system that allows users to graduate from a topic, and a treasure chest to store prizes, elements added to boost the bot’s appeal.

Team News Nest

Another of the projects, News Nest, provides a sophisticated means of helping young people engage with credible news sources in a way that makes it fun. The name is derived from the program’s 10 appealing and colorful birds, each of which focuses on a particular area of news. If you want the headlines, you ask Polly the Parrot, the main news carrier; if you’re interested in science, Gaia the Goose guides you. The flock also includes Flynn the Falcon, sports reporter; Credo the Crow, for crime and legal news; Edwin the Eagle, a business and economics news guide; Pizzazz the Peacock for pop and entertainment stories; and Pixel the Pigeon, a technology news specialist.

News Nest’s development team is made up of MIT seniors Tiana Jiang and Krystal Montgomery, and junior Natalie Tan. They intentionally built News Nest to prevent “doomscrolling,” provide media transparency (sources and political leanings are always shown), and they created a clever, healthy buffer from emotional manipulation and engagement traps by employing birds rather than human characters.

Team M^3 (Multi-Agent Murder Mystery)

A third team, M^3, decided to experiment with making AI humane by keeping it fun. MIT senior Rodis Aguilar, junior David De La Torre, and second-year Deeraj Pothapragada developed M^3, a social deduction multi-agent murder mystery that incorporates four chatbots as different personalities: Gemini, OpenAI’s ChatGPT, xAI’s Grok, and Anthropic’s Claude. The user is the fifth player. 

Like a regular murder mystery, there are locations, weapons, and lies. The user has to guess who committed the murder. It’s very similar to a board or online game played with real players, only these are enhanced AI opponents you can’t see, who may or may not tell the truth in response to questions. Users can’t get too involved with one chatbot, because they’re playing all four. Also, as in a real life murder mystery game, the user is sometimes guilty.

New photonic device efficiently beams light into free space

Light-emitting structures that curl off the chip surface could enable advanced displays, high-speed optical communications, and larger-scale quantum computers.

Photonic chips use light to process data instead of electricity, enabling faster communication speeds and greater bandwidth. Most of that light typically stays on the chip, trapped in optical wires, and is difficult to transmit to the outside world in an efficient manner.

If a lot of light could be rapidly and precisely beamed off the chip, free from the confines of the wiring, it could open the door to higher-resolution displays, smaller Lidar systems, more precise 3D printers, or larger-scale quantum computers.

Now, researchers from MIT and elsewhere have developed a new class of photonic devices that enable the precise broadcasting of light from the chip into free space in a scalable way.

Their chip uses an array of microscopic structures that curl upward, resembling tiny, glowing ski jumps. The researchers can carefully control how light is emitted from thousands of these tiny structures at once.

They used this new platform to project detailed, full-color images that are roughly half the size of a grain of table salt. Used in this way, the technology could aid in the development of lightweight augmented reality glasses or compact displays.

They also demonstrated how photonic “ski jumps” could be used to precisely control quantum bits, or qubits, in a quantum computing system.

“On a chip, light travels in wires, but in our normal, free-space world, light travels wherever it wants. Interfacing between these two worlds has long been a challenge. But now, with this new platform, we can create thousands of individually controllable laser beams that can interact with the world outside the chip in a single shot,” says Henry Wen, a visiting research scientist in the Research Laboratory of Electronics (RLE) at MIT, research scientist at MITRE, and co-lead author of a paper on the new platform.

He is joined on the paper by co-lead authors Matt Saha, of MITRE; Andrew S. Greenspon, a visiting scientist in RLE and MITRE; Matthew Zimmermann, of MITRE; Matt Eichenfeld, a professor at the University of Arizona; senior author Dirk Englund, a professor in the MIT Department of Electrical Engineering and Computer Science and principal investigator in the Quantum Photonics and Artificial Intelligence Group and the RLE; as well as others at MIT, MITRE, Sandia National Laboratories, and the University of Arizona. The research appears today in Nature.

A scalable platform

This work grew out of the Quantum Moonshot Program, a collaboration between MIT, the University of Colorado at Boulder, the MITRE Corporation, and Sandia National Laboratories to develop a novel quantum computing platform using the diamond-based qubits being developed in the Englund lab.

These diamond-based qubits are controlled using laser beams, and the researchers needed a way to interact with millions of qubits at once.

“We can’t control a million laser beams, but we may need to control a million qubits. So, we needed something that can shoot laser beams into free space and scan them over a large area, kind of like firing a T-shirt gun into the crowd at a sports stadium,” Wen says.

Existing methods used to broadcast and steer light off a photonic chip typically work with only a few beams at once and can’t scale up enough to interact with millions of qubits.

To create a scalable platform, the researchers developed a new fabrication technique. Their method produces photonic chips with tiny structures that curve upward off the chip’s surface to shine laser beams into free space.

They built these tiny “ski jumps” for light by creating two-layer structures from two different materials. Each material expands differently when it cools down from the high fabrication temperatures.

The researchers designed the structures with special patterns in each layer so that, when the temperature changes, the difference in strain between the materials causes the entire structure to curve upward as it cools.

This is the same effect as in an old-fashioned thermostat, which utilizes a coil of two metallic materials that curl and uncurl based on the temperature in the room, triggering the HVAC system. “Both of these materials, silicon nitride and aluminum nitride, were separate technologies. Finding a way to put them together was really the fabrication innovation that enables the ski jumps. This wouldn’t have been possible without the pioneering contributions of Matt Eichenfield and Andrew Leenheer at Sandia National Labs,” Wen says.

On the chip, connected waveguides funnel light to the ski jump structures. The researchers use a series of modulators to rapidly and precisely control how that light is turned on and off, enabling them to project light off the chip and move it around in free space.

Painting with light

They can broadcast light in different colors and, by tweaking the frequencies of light, adjust the density of the pattern that is emitted. In this way, they can essentially paint pictures in free space using light.

“This system is so stable we don’t even need to correct for errors. The pattern stays perfectly still on its own. We just calculate what color lasers need to be on at a given time and then turn it on,” he says.

Because the individual points of light, or pixels, are so tiny, the researchers can use this platform to generate extremely high-resolution displays. For instance, with their technique, 30,000 pixels can be fit into the same area that can hold only two pixels used in smartphone displays, Wen says.

“Our platform is the ideal optical engine because our pixels are at the physical limit of how small a pixel can be,” he adds.

Beyond high-resolution displays and larger quantum computers with diamond-based qubits, the method could be used to produce Lidars that are small enough to fit on tiny robots.

It could also be utilized in 3D printing processes that fabricate objects using lasers to cure layers of resin. Because their chip generates controllable beams of light so rapidly, it could greatly increase the speed of these printing processes, allowing users to create more complex objects.

In the future, the researchers want to scale their system up and conduct additional experiments on the yield and uniformity of the light, design a larger system to capture light from an array of photonic chips with “ski jumps,” and conduct robustness tests to see how long the devices last.

“We envision this opening the door to a new class of lab-on-chip capabilities and lithographically defined micro-opto-robotic agents,” Wen says.

This research was funded, in part, by the MITRE Quantum Moonshot Program, the U.S. Department of Energy, and the Center for Integrated Nanotechnologies.

A better method for planning complex visual tasks

A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.

MIT researchers have developed a generative artificial intelligence-driven approach for planning long-term visual tasks, like robot navigation, that is about twice as effective as some existing techniques.

Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.

In the end, the system automatically generates a set of files that can be fed into classical planning software, which computes a plan to achieve the goal. This two-step system generated plans with an average success rate of about 70 percent, outperforming the best baseline methods that could only reach about 30 percent.

Importantly, the system can solve new problems it hasn’t encountered before, making it well-suited for real environments where conditions can change at a moment’s notice.

“Our framework combines the advantages of vision-language models, like their ability to understand images, with the strong planning capabilities of a formal solver,” says Yilun Hao, an aeronautics and astronautics (AeroAstro) graduate student at MIT and lead author of an open-access paper on this technique. “It can take a single image and move it through simulation and then to a reliable, long-horizon plan that could be useful in many real-life applications.”

She is joined on the paper by Yongchao Chen, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS); Chuchu Fan, an associate professor in AeroAstro and a principal investigator in LIDS; and Yang Zhang, a research scientist at the MIT-IBM Watson AI Lab. The paper will be presented at the International Conference on Learning Representations.

Tackling visual tasks

For the past few years, Fan and her colleagues have studied the use of generative AI models to perform complex reasoning and planning, often employing large language models (LLMs) to process text inputs.

Many real-world planning problems, like robotic assembly and autonomous driving, have visual inputs that an LLM can’t handle well on its own. The researchers sought to expand into the visual domain by utilizing vision-language models (VLMs), powerful AI systems that can process images and text.

But VLMs struggle to understand spatial relationships between objects in a scene and often fail to reason correctly over many steps. This makes it difficult to use VLMs for long-range planning.

On the other hand, scientists have developed robust, formal planners that can generate effective long-horizon plans for complex situations. However, these software systems can’t process visual inputs and require expert knowledge to encode a problem into language the solver can understand.

Fan and her team built an automatic planning system that takes the best of both methods. The system, called VLM-guided formal planning (VLMFP), utilizes two specialized VLMs that work together to turn visual planning problems into ready-to-use files for formal planning software.

The researchers first carefully trained a small model they call SimVLM to specialize in describing the scenario in an image using natural language and simulating a sequence of actions in that scenario. Then a much larger model, which they call GenVLM, uses the description from SimVLM to generate a set of initial files in a formal planning language known as the Planning Domain Definition Language (PDDL).

The files are ready to be fed into a classical PDDL solver, which computes a step-by-step plan to solve the task. GenVLM compares the results of the solver with those of the simulator and iteratively refines the PDDL files.

“The generator and simulator work together to be able to reach the exact same result, which is an action simulation that achieves the goal,” Hao says.

Because GenVLM is a large generative AI model, it has seen many examples of PDDL during training and learned how this formal language can solve a wide range of problems. This existing knowledge enables the model to generate accurate PDDL files.

A flexible approach

VLMFP generates two separate PDDL files. The first is a domain file that defines the environment, valid actions, and domain rules. It also produces a problem file that defines the initial states and the goal of a particular problem at hand.

“One advantage of PDDL is the domain file is the same for all instances in that environment. This makes our framework good at generalizing to unseen instances under the same domain,” Hao explains.

To enable the system to generalize effectively, the researchers needed to carefully design just enough training data for SimVLM so the model learned to understand the problem and goal without memorizing patterns in the scenario. When tested, SimVLM successfully described the scenario, simulated actions, and detected if the goal was reached in about 85 percent of experiments.

Overall, the VLMFP framework achieved a success rate of about 60 percent on six 2D planning tasks and greater than 80 percent on two 3D tasks, including multirobot collaboration and robotic assembly. It also generated valid plans for more than 50 percent of scenarios it hadn’t seen before, far outpacing the baseline methods.

“Our framework can generalize when the rules change in different situations. This gives our system the flexibility to solve many types of visual-based planning problems,” Fan adds.

In the future, the researchers want to enable VLMFP to handle more complex scenarios and explore methods to identify and mitigate hallucinations by the VLMs.

“In the long term, generative AI models could act as agents and make use of the right tools to solve much more complicated problems. But what does it mean to have the right tools, and how do we incorporate those tools? There is still a long way to go, but by bringing visual-based planning into the picture, this work is an important piece of the puzzle,” Fan says.

This work was funded, in part, by the MIT-IBM Watson AI Lab.

2026 MIT Sloan Sports Analytics Conference shows why data make a difference

Over 2,500 — including coaches and players from Team USA, the NBA, WNBA, and more — attended MIT’s industry-leading event, now in its 20th year.

With time dwindling in the Olympic women’s ice hockey gold medal game on Feb. 19, players for Team USA and Team Canada lined up for a key faceoff in Canada’s end. Canada had a 1-0 lead. USA had 2:23 left, and an ace up their sleeve: analytics.

USA Coach John Wroblewski pulled the goalkeeper, to get a player advantage, and had forward Alex Carpenter take the faceoff. Statistics show that Carpenter is not only very good at winning faceoffs; she also wins a lot of them cleanly. That allows her team to quickly regain possession, without too many teammates nearby. Knowing that, Wroblewski directed the USA players to spread out, largely away from the faceoff circle, in position to circulate the puck as soon as they got it back.

Carpenter won the faceoff, and Team USA quickly started a passing move. Laila Edwards soon launched a shot that longtime star Hilary Knight deflected in for the crucial, game-tying goal with 2:04 left. Team USA then won in overtime. And data-driven decision-making had also won big; indeed, it helped change the Olympics.

“What it does for a coach, the other thing these analytics do, is … it allows you to move forward with this confidence level,” Wroblewski said on Saturday at the 20th annual MIT Sloan Sports Analytics Conference (SSAC), during a hockey analytics panel where he detailed his decision-making for that faceoff, and in the gold medal game generally.

Using the data, he added, lets coaches “limit the emotion” that might cloud their in-game decisions.

“By the time you get to that decision, you’re then allowed the freedom to step away from the decision, to allow the players to go earn their medal,” Wroblewski added.

You don’t usually find coaches divulging their tactical secrets just three weeks after a big game has been played. But then, this is the MIT Sloan conference, a trailblazing forum that has helped analytics ideas spread throughout sports. Coaches, players, and analysts know any data-driven discussion will find an interested audience.

“Analytics was massive for us going into the gold medal game,” Wroblewski said.

20 years on: From classrooms to convention halls

The 20th edition of SSAC was a strong one, with many substantive panel discussions and interviews; the annual research paper, hackathon, and case study contests; mentorship events and informal networking opportunities; and more. Over 2,500 people attended the two-day event, held at Boston’s Menino Conference and Exhibition Center (MCEC). The conference was founded in 2007 by Daryl Morey, now president of basketball operations for the NBA Philadelphia 76ers, and Jessica Gelman, now CEO of the Kraft Analytics Group.

The first three editions of the conference were held on the MIT campus. In 2010, it first moved to the MCEC (one of two regular convention-center sites it uses), and starting in 2011, the conference became a two-day event.

Today people attend for the panels, the career opportunities, and, in some cases, to make news. NBA Commissioner Adam Silver was on hand this year, engaging in an on-stage conversation with former WNBA great Sue Bird, publicly addressing some of the key issues facing his league, and drawing wide media coverage.

First, though, Silver reflected about attending the second edition of the conference on the MIT campus in 2008, when he was deputy commissioner.

“It was literally a classroom of 20 people we were talking to,” Silver recalled. “I think it was the beginning of the moment when people were taking sports as a discipline more seriously. … I give Jessica and Daryl a lot of credit [for that].”

Addressing tanking and gambling

A core part of Silver’s comments focused on two big issues in pro basketball: tanking and gambling. About eight NBA teams appear to be tanking this season, that is, losing games in order to increase their chances of getting a high draft pick.

“We are going to make substantial changes for next year,” Silver said, although he also added: “I am an incrementalist. I think we’ve got to be a little bit careful about how huge a change we make at once. I’m not ruling anything out. But I am paying attention to that.”

To be sure, tanking has long been a part of professional basketball, as Bird noted during the conversation.

“We did it in Seattle, to be honest,” Bird said. “Breanna Stewart was coming out of college. We were in a ‘rebuild.’”

Still, in this NBA season, tanking has become an epidemic, in “a little bit of a perfect storm,” as Silver put it on Friday. And almost every proposed solution seems to have drawbacks. Perhaps the simplest cure for tanking, actually, would be robust analytical studies showing that it is not a very effective team-building strategy. If that is what the numbers reveal, of course.

Meanwhile, multiple arrests of NBA players and coaches at the beginning of the season show further that sports gambling continues to present challenges to professional sports leagues.

“I personally think there should be more regulation now, not less,” Silver said on Friday, suggesting that federal rules would simplify things in the U.S., where 39 states allow sports gambling to some extent. He also said the NBA can continue to work on monitoring data to protect against gambling scandals.

“I think there are some large-platform companies are that are looking at a business opportunity to come in and in a much more sophisticated way work as a detection service with the league,” Silver said.

Through it all, Silver said, the NBA will continue to be a data-driven operation. Have you watched a game with a long instant-replay review, and gotten a little impatient? Still, have you kept watching that game? So does almost everyone.

“For years people would tell us, ‘Don’t use instant replay, because you’ll turn fans off,’” Silver said. However, he added, “The data suggests, in terms of ratings and what servers tell us, you almost never lose a fan when you’re going to replay. Because they want to see the replay and they want to see what happened.”

The minnows got big

Sports analytics took root in baseball, with its discrete pitcher-hitter actions. Legendary MLB general manager Branch Rickey employed a statistician for the great Brooklyn Dodgers of the 1950s; the famous manager Earl Weaver thought analytically with the Baltimore Orioles in the 1970s. Baseball analyst Bill James made sports analytics a viable pursuit with his annual “Baseball Abstract” bestsellers in the 1980s, and Michael Lewis’ “Moneyball” popularized it.

But data can be applied to all sports — and sometimes is most valuable when only some teams are interested in it. Take soccer. In the English Premier League, about three clubs have been heavily oriented around analytics over the last decade: Liverpool FC, Brighton FC, and Brentford FC. That has helped Liverpool win multiple titles, while Brighton and Brentford, smaller clubs, have startled many with their success.

Saturday at SSAC, Brentford’s majority owner Matthew Benham made one of his most visible public appearances, in an onstage interview with podcaster Roger Bennett. Benham first made money wagering on soccer, then invested in Brentford, his childhood club.

“The information we used in the early days was really, really rudimentary,” Benham said. In his account, his success building an analytics-based club has only partly been about the numbers.

“A lot of the success has just been in running things efficiently.” Benham said. He prefers to have management discussions that are an “exchange of views, rather than debate,” since the latter implies an interaction with a clear winner and loser. Instead, compiling independent-minded views from his executives is more important.

Brentford also uses “a combination of old-style scouting and data” for its player acquisition decisions, Benham said. Not every decision works. Brentford could have signed current Arsenal FC star Eberechi Eze for a mere $4 million pounds in 2019, and passed; Crystal Palace FC acquired Eze, then realized a windfall when Arsenal purchased his services.

Still, pressed by Bennett to specify a little more about his analytical thinking, Benham implied that strikers are valuable not only for their finishing skills, but for consistently getting open for shots on goal. Fans tend to focus too much on a player’s misses, rather than how many chances are created by their off-ball work.

“Getting in position is way, way more informative than finishing,” Benham said.

A similar insight seems to have guided Liverpool’s thinking. As it happens, a Friday panel at SSAC featured Ian Graham, who ran Liverpool’s analytics operations from 2012 to 2023, and weighed in on a number of subjects. Among other things, Graham noted, teams are too cautious when tied late in a match; soccer grants three points for a win, one for a draw, and zero for a loss, so from a tied position, the reward for winning is twice as great as the penalty for losing.

“Teams don’t go for it enough,” Graham said. “Teams think a draw is an okay result.”

The limits of knowledge

Sports, of course, are ultimately played by imperfect, injury-prone, and sometimes exhausted athletes. One consistent lesson from the MIT Sloan conference involves the limits of data and plans.

“We think the data is giving us an answer, when actually it’s giving us some information, and we still have to make a choice,” said Ariana Andonian, vice president of player personnel for the Philadelphia 76ers, during a basketball panel on Saturday.

Asked about the promise of artificial intelligence for sports analytics, Sonia Raman, head coach of the WNBA’s Seattle Storm, noted that its insights might always be limited by circumstances.

“It’s not like you can just get an AI report in the middle of the game that says, ‘Get some shooting in,’” said Raman, who, prior to coaching in the WNBA and NBA served for 12 years as head coach of the MIT women’s basketball team.

“You can have a great plan, but if it’s poorly executed, it’s way worse than a poor plan that’s well executed,” added Steven Adams, a center for the NBA’s Houston Rockets (who is currently not playing due to injury), during the same panel.

And yet, in some games and matches, the analytics do work, the plans do come to fruition, and the numbers do make a difference. When that happens, as John Wroblewski can now attest, the results are golden. 

3 Questions: Building predictive models to characterize tumor progression

Assistant Professor Matthew Jones is working to decode molecular processes on the genetic, epigenetic, and microenvironment levels to anticipate how and when tumors evolve to resist treatment.

Just as Darwin’s finches evolved in response to natural selection in order to endure, the cells that make up a cancerous tumor similarly counter selective pressures in order to survive, evolve, and spread. Tumors are, in fact, complex sets of cells with their own unique structure and ability to change. 

Today, artificial Intelligence and machine learning tools offer an unparalleled opportunity to illuminate the generalizable rules governing tumor progression on the genetic, epigenetic, metabolic, and microenvironmental levels. 

Matthew G. Jones, an assistant professor in the MIT Department of Biology, the Koch Institute for Integrative Cancer Research, and the Institute for Medical Engineering and Science, hopes to use computational approaches to build predictive models — to play a game of chess with cancer, making sense of a tumor’s ability to evolve and resist treatment with the ultimate goal of improving patient outcomes. In this interview, he describes his current work.

Q: What aspect of tumor progression are you working to explore and characterize? 

A: A very common story with cancer is that patients will respond to a therapy at first, and then eventually that treatment will stop working. The reason this largely happens is that tumors have an incredible, and very challenging, ability to evolve: the ability to change their genetic makeup, protein signaling composition, and cellular dynamics. The tumor as a system also evolves at a structural level. Oftentimes, the reason why a patient succumbs to a tumor is because either the tumor has evolved to a state we can no longer control, or it evolves in an unpredictable manner. 

In many ways, cancers can be thought of as, on the one hand, incredibly dysregulated and disorganized, and on the other hand, as having their own internal logic, which is constantly changing. The central thesis of my lab is that tumors follow stereotypical patterns in space and time, and we’re hoping to use computation and experimental technology to decode the molecular processes underlying these transformations.  

We’re focused on one specific way tumors are evolving through a form of DNA amplification called extrachromosomal DNA. Excised from the chromosome, these ecDNAs are circularized and exist as their own separate pool of DNA particles in the nucleus. 

Initially discovered in the 1960s, ecDNA were thought to be a rare event in cancer. However, as researchers began applying next-generation sequencing to large patient cohorts in the 2010s, it seemed like not only were these ecDNA amplifications conferring the ability of tumors to adapt to stresses, and therapies, faster, but that they were far more prevalent than initially thought.

We now know these ecDNA amplifications are apparent in about 25 percent of cancers, in the most aggressive cancers: brain, lung, and ovarian cancers. We have found that, for a variety of reasons, ecDNA amplifications are able to change the rule book by which tumors evolve in ways that allow them to accelerate to a more aggressive disease in very surprising ways. 

Q: How are you using machine learning and artificial intelligence to study ecDNA amplifications and tumor evolution? 

A: There’s a mandate to translate what I’m doing in the lab to improve patients’ lives. I want to start with patient data to discover how various evolutionary pressures are driving disease and the mutations we observe. 

One of the tools we use to study tumor evolution is single-cell lineage tracing technologies. Broadly, they allow us to study the lineages of individual cells. When we sample a particular cell, not only do we know what that cell looks like, but we can (ideally) pinpoint exactly when aggressive mutations appeared in the tumor’s history. That evolutionary history gives us a way of studying these dynamic processes that we otherwise wouldn’t be able to observe in real time, and helps us make sense of how we might be able to intercept that evolution. 

I hope we’re going to get better at stratifying patients who will respond to certain drugs, to anticipate and overcome drug resistance, and to identify new therapeutic targets.

Q: What excited you about joining the MIT community?

A: One of the things that I was really attracted to was the integration of excellence in both engineering and biological sciences. At the Koch Institute, every floor is structured to promote this interface between engineers and basic scientists, and beyond campus, we can connect with all the biomedical research enterprises in the greater Boston area. 

Another thing that drew me to MIT was the fact that it places such a strong emphasis on education, training, and investing in student success. I’m a personal believer that what distinguishes academic research from industry research is that academic research is fundamentally a service job, in that we are training the next generation of scientists. 

It was always a mission of mine to bring excellence to both computational and experimental technology disciplines. The types of trainees I’m hoping to recruit are those who are eager to collaborate and solve big problems that require both disciplines. The KI [Koch Institute] is uniquely set up for this type of hybrid lab: my dry lab is right next to my wet lab, and it’s a source of collaboration and connection, and that reflects the KI’s general vision. 

How Joseph Paradiso’s sensing innovations bridge the arts, medicine, and ecology

From early motion-sensing platforms to environmental monitoring, the professor and head of the Program in Media Arts and Sciences has turned decades of cross-disciplinary research into real-world impact.

Joseph Paradiso thinks that the most engaging research questions usually span disciplines. 

Paradiso was trained as a physicist and completed his PhD in experimental high-energy physics at MIT in 1981. His father was a photographer and filmmaker working at MIT, MIT Lincoln Laboratory, and the MITRE Corporation, so he grew up in a house where artists, scientists, and engineers regularly gathered and interesting music was always playing. 

That mix of influences led him to the MIT Media Lab, where he is the Alexander W. Dreyfoos Professor, academic head of the Program in Media Arts and Sciences, and director of the Responsive Environments research group.

At the Media Lab, Paradiso conducts research that engages sensing of different kinds and applies it across diverse and often extreme applications. He works on developing technologies that can efficiently capture and process multiple sensing modalities, and leverages this capability in application domains like the internet of things, medicine, environmental sensing, space exploration, and artistic expression. These efforts use that information to help people better understand the world, express themselves, and connect with one another.

Early in his career, Paradiso helped pioneer the field of wireless wearable sensing. He built many systems with multiple embedded sensors that could send information from the human body in real-time. One of his early flagship projects in this area was a pair of shoes fielded in 1997 for real-time augmented dance performance that embedded 16 sensors in each shoe, allowing wearers’ movements to directly generate music through algorithmic mapping. And Paradiso’s research at the Media Lab has consistently focused on sensing and using that information in new ways. 

“When I would list all the sensors … people would laugh. But now, my watch is measuring most of these things,” Paradiso notes. “The world has moved.” 

That progression from early prototypes to everyday technology helped lay the groundwork for devices people now use regularly to track activity, health, and performance.

As sensing systems improved, Paradiso expanded his work from individuals to groups. He developed platforms that allowed dance ensembles to create music together through their collective motion. Achieving this required Paradiso and his team to develop new ways for compact wearable devices to communicate wirelessly at high speed, as well as new approaches to real-time data processing and extending the range of available microelectromechanical systems (MEMS) sensors.

Those same sensing platforms were later adapted for sports medicine in 2006. Working with doctors who support elite athletes, his array of compact, wearable sensors captured large amounts of high-speed motion data from multiple points on the body, aimed at helping clinicians assess injury risk, performance, and recovery on the go, without the complex equipment typically associated with biomechanical monitoring and clinical settings.

More recently, Paradiso’s research has extended beyond humans. Through collaborations with National Geographic Explorers, his team has deployed sensors in remote environments to study animal behavior, including low-power compact wearable devices to detect the environmental conditions around the animal as well as track them (currently on lions and hyenas in Botswana and goats in Chile), and acoustic sensors with onboard AI to detect and monitor populations of endangered honeybees in Patagonia. This work provides new ways to understand how ecosystems function and how the planet is changing.

Paradiso was named an IEEE Fellow in January, recognizing his achievement in wireless wearable sensing and mobile energy harvesting. This is the highest grade of membership in IEEE, the world’s leading professional association dedicated to advancing technology for the benefit of humanity.

Across art, health, and the natural world, Paradiso’s work reflects how foundational research at MIT can seed technologies that ripple outward over time, shaping new applications and opening new fields. As advances in wearable technologies drive the rush toward the ever-more-connected human, a persistent existential question lurks. 

“Where do I stop, versus others begin?” Paradiso asks. 

For him, the aim is not novelty for its own sake, but amplification: using technology to help people become more perceptive, better connected, and more aware of their place in a larger system.

MIT School of Engineering faculty receive awards in fall 2025

Faculty members and researchers were honored in recognition of their scholarship, service, and overall excellence.

Each year, faculty and researchers across the MIT School of Engineering are recognized with prestigious awards for their contributions to research, technology, society, and education. To celebrate these achievements, the school periodically highlights select honors received by members of its departments, institutes, labs, and centers. The following individuals were recognized in fall 2025:

Hal Abelson, the Class of 1922 Professor in the Department of Electrical Engineering and Computer Science, received the 2025 Lifetime Achievement Award for Excellence from Open Education Global. The award honors his foundational impact on open education, Creative Commons, and open knowledge movements.

Faez Ahmed, the Henry L. Doherty Career Development Professor in Ocean Utilization in the Department of Mechanical Engineering, received an Amazon Research Award for his project “AutoDA‑Sim: A Multi‑Agent Framework for Safe, Aesthetic, and Aerodynamic Vehicle Design.” Amazon Research Awards provide unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines.

Pulkit Agrawal, an associate professor in the Department of Electrical Engineering and Computer Science, received the 2025 IROS Toshio Fukuda Young Professional Award for contributions to robot learning, policy learning, agile locomotion, and dexterous manipulation. The award recognizes outstanding contributions of an individual of the IROS community who has pioneered activities in robotics and intelligent systems.

Ahmad Bahai, a professor of the practice in the Department of Electrical Engineering and Computer Science, was elected to the 2025 class of Fellows of the National Academy of Inventors for contribution to innovation in new semiconductor devices with extensive applications in clinical grade personal sensors for a variety of biomarkers. The honor recognizes inventors whose patented work has made a meaningful global impact.

Yufeng (Kevin) Chen, an associate professor in the Department of Electrical Engineering and Computer Science, received the 2025 IROS Toshio Fukuda Young Professional Award for contributions to insect‑scale multimodal robots and soft‑actuated aerial systems. The award recognizes outstanding contributions of an individual of the IROS community who has pioneered activities in robotics and intelligent systems.

Angela Koehler, the Charles W. and Jennifer C. Johnson Professor in the Department of Biological Engineering, received the 2025 Sato Memorial International Award from the Pharmaceutical Society of Japan, recognizing advancements in pharmaceutical sciences and U.S.–Japan scientific collaboration.

Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science, was elected to the National Academy of Medicine for pioneering digital health technology that enables noninvasive, off-body remote health monitoring via AI and wireless signals, and for developing digital biomarkers for Parkinson’s progression and detection. Election to the academy is considered one of the highest honors in the fields of health and medicine, and recognizes individuals who have demonstrated outstanding professional achievement and commitment to service.

Darcy McRose, the Thomas D. and Virginia W. Cabot Career Development Professor in the Department of Civil and Environmental Engineering, was selected as a 2025 Packard Fellow for Science and Engineering. The Packard Foundation established the Packard Fellowships for Science and Engineering to allow the nation’s most promising early-career scientists and engineers flexible funding to take risks and explore new frontiers in their fields of study.

Muriel Médard, the NEC Professor of Software Science and Engineering in the Department of Electrical Engineering and Computer Science, received the 2026 IEEE Richard W. Hamming Medal for contributions to coding for reliable communications and networking. Recognized for breakthroughs in network coding and information theory, Médard’s innovations improve the reliability of data transmission in applications such as streaming video, wireless networks, and satellite communications. The award is given for exceptional contributions to information sciences, systems and technology.

Tess Smidt, an associate professor in the Department of Electrical Engineering and Computer Science, was selected as a 2025 AI2050 Fellow by Schmidt Sciences for her project, “Hierarchical Representations of Complex Physical Systems with Euclidean Neural Networks.” The program supports research that aims to help AI benefit humanity by mid‑century.

MIT undergraduates help US high schoolers tackle calculus

The MIT4America Calculus Project is a growing source of tutoring support on a topic that’s a “gateway” to many STEM careers.

This year in a rural school district in southeastern Montana, one high school student is taking calculus. For many people, calculus is daunting enough, even when teachers are used to offering it and peers are around to help. Studying it solo can be even harder. Yet this lone student has an unusual source of support: weekly tutoring directly from an MIT undergraduate, by Zoom, a long-distance but helpful way to stay on track.

It's part of a new program called the MIT4America Calculus Project, launched from the Institute last summer, in which MIT undergraduates and alumni work with school districts across the U.S., from Montana to Texas to New York, to tutor high school students. The logic is compelling: Students are highly proficient at calculus at MIT, where it is almost a requirement for admissions and success. The new civic-minded outreach program lets those MIT people share their knowledge and skills, getting high schoolers ready for further studies and even jobs, especially in STEM fields. 

“Calculus is a gateway for many students into STEM higher education and careers,” says MIT Professor Eric Klopfer, a co-director of the MIT4America Calculus Project. “We can help more students, in more places, fulfill requirements and get into great universities across the country, whether MIT or others, and then into STEM careers. We want to make sure they have the skills to do that.”

At this point, the project is working closely with 14 school districts across the U.S., deploying 30 current MIT undergraduates and seven alumni as tutors. The weekly sessions are carefully coordinated with school administrators and teachers, and the MIT tutors have all received training. The program started with an in-person summer calculus camp in 2025; by next summer, the goal is to be collaborating with about 20 schools districts.

“We want it to have a lasting impact,” says Claudia Urrea, an education scholar and co-director of the MIT4America Calculus Project “It’s not just about students passing an exam, but having tutors who look like what the students want to be in the future, who are mentors, have conversations, and make sure the high school students are learning.” 

Klopfer and Urrea bring substantial experience to the project. Klopfer is a professor and director of the Scheller Teacher Education Program and the Education Arcade at MIT; Urrea is executive director for the PreK-12 Initiative at MIT Open Learning.

The MIT4America Calculus Project is supported through a gift from the Siegel Family Endowment and was developed as a project in consultation with David Siegel SM ’86, PhD ’91, a computer scientist and entrepreneur who is chairman of the firm Two Sigma.

“David Siegel came to us with two powerful questions: How can we spread the educational impact of MIT beyond our walls? And how can we open doors to STEM careers for U.S. high school students who don’t have access to calculus?” says MIT President Sally Kornbluth.

She adds: “The MIT4America Calculus Project answers those questions in a perfectly MIT way: Reflecting the Institute’s longstanding commitment to national service, the MIT4America Calculus Project supplies an innovative answer to a hard practical problem, and it taps the uncommon skill of the people of MIT to create opportunity for others. We’re enormously grateful to David for his inspiration and guidance, and to the Siegel Family Endowment for the financial support that brought this idea to life.”

The U.S. has more than 13,000 school districts, and about half of them offer calculus classes. The MIT effort aims to work with districts that already have existing programs but are striving to add educational support for them, often while facing funding constraints or other limitations.

In contrast to the one-student calculus situation in Montana, the project is also working with a 5,000-student district in Texas, south of Dallas, where about 60 high school students take calculus; currently five Institute undergraduates are tutoring 15 students from the district’s schools.

“Other organizations are involved in efforts like this, but I think MIT brings some unique things to it,” Klopfer says. “I think involving our undergraduates in this is an awesome contribution. Our students really do come from all over the place, and are sometimes connecting back to their home states and communities, and that makes a difference on both sides.”

He adds: “I see benefits for our students, too. They develop good ways of communicating, working with other people and building skills. They can gain a lot of great experience.”

In addition to the in-person summer calculus camp, which is expected to continue, and the weekly video tutoring, the MIT4America Calculus Project is working on developing online tools that help guide high school students as well. Still, Urrea emphasizes, the project is built around “the importance of people. A community of support is very important, to have connections that build over time.  The human aspect of the program is irreplaceable.”

The MIT tutors must pass rigorous training sessions that cover pedagogy and other aspects of working with high school students, and know they are making a substantial commitment of time and effort.

It has been worth it, as teachers say their high school students have been responding very well to the MIT tutors.

“For students to be able to see themselves in their tutors is a really cool thing,” says Shilpa Agrawal ’15, director of computer science and an AP calculus AB teacher at Comp Sci High in the Bronx, New York, where 15 students are participating in the project.

“It’s led to a lot of success for my students,” adds Agrawal, who majored in computer science at MIT. She is part of the national network of MIT-connected teachers who have been helping the program grow organically, having reached out to Jenny Gardony, manager of the MIT4America Calculus Project.

Gardony, who is also the math project manager in MIT’s Scheller Teacher Education program, has been receiving enthusiastic emails from teachers in other participating districts since the project started.

“I have to start by saying thank you,” one teacher wrote to Gardony, adding that one student “was so excited in class today. The session she had with you made her so confident. She’s always nervous, but today she was smiling and helping others, and that was 100 percent because of you.”

Gardony adds: “The fact that a busy teacher takes the time to send that email, I’m touched they would do that.” 

Understanding how “marine snow” acts as a carbon sink

A new study finds hitchhiking bacteria dissolve essential ballast in ubiquitous “snow” particles, which could counteract the ocean’s ability to sequester carbon.

In some parts of the deep ocean, it can look like it’s snowing. This “marine snow” is the dust and detritus that organisms slough off as they die and decompose. Marine snow can fall several kilometers to the deepest parts of the ocean, where the particles are buried in the seafloor for millennia.

Now, researchers at MIT and their collaborators have found that as marine snow falls, tiny hitchhikers may limit how deep the particles can sink before dissolving away. The team shows that when bacteria hitch a ride on marine snow particles, the microbes can eat away at calcium carbonate, which is an essential ballast that helps particles sink.

The findings, which appear this week in the Proceedings of the National Academy of Sciences, could explain how calcium carbonate dissolves in shallow layers of the ocean, where scientists had assumed it should remain intact. The results could also change scientists’ understanding of how quickly the ocean can sequester carbon from the atmosphere.

Marine snow is a main vehicle by which the ocean stores carbon. At the ocean’s surface, phytoplankton absorb carbon dioxide from the atmosphere and convert the gas into other forms of carbon, including calcium carbonate — the same stuff that’s found in shells and corals. When they die, bits of phytoplankton drift down through the ocean as marine snow, carrying the carbon with them. If the particles make it to the deep ocean, the carbon they carry can be buried and locked away for hundreds to thousands of years.

But the new study suggests bacteria may be working against the ocean’s ability to sequester carbon. By eroding the particles’ calcium carbonate, bacteria can significantly slow the sinking of marine snow. The more they linger, the more likely the particles are to be respired quickly, releasing carbon dioxide into the shallow ocean, and possibly back into the atmosphere.

“What we’ve shown is that carbon may not sink as deep or as fast as one may expect,” says study co-author Andrew Babbin, an associate professor in the Department of Earth, Atmospheric and Planetary Sciences and a mission director at the Climate Project at MIT. “As humanity tries to design our way out of the problem of having so much CO₂ in the atmosphere, we have to take into account these natural microbial mechanisms and feedbacks.”

The study’s primary author is Benedict Borer, a former MIT postdoc who is now an assistant professor of marine and coastal sciences at the Rutgers School of Environmental and Biological Sciences; co-authors include Adam Subhas and Matthew Hayden at the Woods Hole Oceanographic Institution and Ryan Woosley, a principal research scientist at MIT’s Center for Sustainability Science and Strategy.

Losing weight

Marine snow acts as the ocean’s main “biological pump,” the process by which the ocean pulls carbon from the surface down into the deep ocean. Scientists estimate that marine snow is responsible for drawing down billions of tons of carbon each year. Marine snow’s ability to sink comes mainly from minerals such as calcium carbonate embedded within the particles. The mineral is a dense ballast that weighs down the particle. The more calcium carbonate a particle has, the faster it sinks.

Scientists had assumed based on thermodynamics that calcium carbonate should not dissolve within the ocean’s upper layers, given the general temperature and pH conditions in the surface ocean. Any calcium carbonate that is bound up in marine snow should then safely sink to depths greater than 1,000 meters without dissolving along the way.

But oceanographers have long observed signs of dissolved calcium carbonate in the upper layers of the ocean, suggesting that something other than the ocean’s macroscale conditions was dissolving the mineral and slowing down the ocean’s biological pump.

And indeed, the MIT team has found that what is dissolving calcium carbonate in shallow waters is a microscale process that occurs within the immediate environment of an individual particle.

“Most oceanographers think about the macroscale, and in this instance what’s happening in microscopic particles is what is actually controlling bulk seawater chemistry,” Borer says. “Consequences abound for the ocean’s carbon dioxide sequestration capacity.”

A sinking sweetspot

In their new study, the researchers set up an experiment to simulate a sinking particle of marine snow and its interactions at the microscale. The team synthesized particles similar to marine snow that they made from varying concentrations of calcium carbonate and bacteria — organisms that are often found feasting on the particles in the ocean.

“The ocean is a fairly dilute medium with respect to organic matter,” Babbin says. “So organisms like bacteria have to search for food. And particles of marine snow are like cheeseburgers for bacteria.”

The team designed a small microfluidic chip to contain the particles, and flowed seawater through the chip at various rates to simulate different sinking speeds in the ocean. Their experiments revealed that whenever particles hosted any bacteria, they also rapidly lost some calcium carbonate, which dissolved into the surrounding seawater. As bacteria feed on the particles’ organic material, the microbes excrete acidic waste products that act to dissolve the particles’ inorganic, ballasting calcium carbonate.

The researchers also found that the amount of calcium carbonate that dissolves depends on how fast the particles sink. They flowed seawater around the particles at slow, intermediate, and fast speeds and found that both slow and fast sinking limit the amount of calcium carbonate that’s dissolved. With slow sinking, particles don’t receive as much oxygen from their surroundings, which essentially suffocates any hitchhiking bacteria. When particles sink quickly, bacteria may be sufficiently oxygenated, but any waste products that they produce can be easily flushed away before they can dissolve the particles’ calcium carbonate.

At intermediate speeds, there is a sweet spot: Bacteria are sufficiently oxygenated and can also build up enough waste, enabling the microbes to efficiently dissolve calcium carbonate.

Overall, the work shows that bacteria can have a significant effect on marine snow’s ability to sink and sequester carbon in the deep ocean. Bacteria can be found everywhere, and particularly in the shallower ocean regions. Even if macroscale conditions in these upper layers should not dissolve calcium carbonate, the study finds bacteria working at the microscale most likely do.

The findings could explain oceanographers’ observations of dissolved calcium carbonate in shallow ocean regions. They also illustrate that bacteria and other microbes may be working against the ocean’s natural ability to sequester carbon, by dissolving marine snow’s ballast and slowing its descent into the deep ocean. As humans consider climate solutions that involve enhancing the ocean’s biological pump, the researchers emphasize that bacteria’s role must be taken into account.

“Insights from this work are vital to predict how ecosystems will respond to marine carbon dioxide removal attempts, and overall how the oceans will change in response to future climate scenarios,” says Benedict Borer, who carried out the study’s experiments as a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences.

This work was supported, in part, by the Simons Foundation, the National Science Foundation, and the Climate Project at MIT.

Neurons receive precisely tailored teaching signals as we learn

New work suggests the brain can deliver neuron-specific feedback during learning — resembling the error signals that drive machine learning.

When we learn a new skill, the brain has to decide — cell by cell — what to change. New research from MIT suggests it can do that with surprising precision, sending targeted feedback to individual neurons so each one can adjust its activity in the right direction.

The finding echoes a key idea from modern artificial intelligence. Many AI systems learn by comparing their output to a target, computing an “error” signal, and using it to fine-tune connections within the network. A long-standing question has been whether the brain also uses that kind of individualized feedback. In an open-access study published in the Feb. 25 issue of the journal Nature, MIT researchers report evidence that it does.

A research team led by Mark Harnett, a McGovern Institute for Brain Research investigator and associate professor in the Department of Brain and Cognitive Sciences at MIT, discovered these instructive signals in mice by training animals to control the activity of specific neurons using a brain-computer interface (BCI). Their approach, the researchers say, can be used to further study the relationships between artificial neural networks and real brains, in ways that are expected to both improve understanding of biological learning and enable better brain-inspired artificial intelligence.

The changing brain

Our brains are constantly changing as we interact with the world, modifying their circuitry as we learn and adapt. “We know a lot from 50 years of studies that there are many ways to change the strength of connections between neurons,” Harnett says. “What the field really lacks is a way of understanding how those changes are orchestrated to actually produce efficient learning.”

Some actions — and the neural connections that enable them — are reinforced with the release of neuromodulators like dopamine or norepinephrine in the brain. But those signals are broadcast to large groups of neurons, without discriminating between cells’ individual contributions to a failure or a success. “Reinforcement learning via neuromodulators works, but it’s inefficient, because all the neurons and all the synapses basically get only one signal,” Harnett says.

Machine learning uses an alternative, and extremely powerful, way to learn from mistakes. Using a method called back propagation, artificial neural networks compute an error signal and use it to adjust their individual connections. They do this over and over, learning from experience how to fine-tune their networks for success. “It works really well and it’s computationally very effective,” Harnett says.

It seemed likely that brains might use similar error signals for learning. But neuroscientists were skeptical that brains would have the precision to send tailored signals to individual neurons, due to the constraints imposed by using living cells and circuits instead of software and equations. A major problem for testing this idea was how to find the signals that provide personalized instructions to neurons, which are called vectorized instructive signals. The challenge, explains Valerio Francioni, first author of the Nature paper and a former postdoc in Harnett’s lab, is that scientists don’t know how individual neurons contribute to specific behaviors.

“If I was recording your brain activity while you were learning to play piano,” Francioni explains, “I would learn that there is a correlation between the changes happening in your brain and you learning piano. But if you asked me to make you a better piano player by manipulating your brain activity, I would not be able to do that, because we don’t know how the activity of individual neurons map to that ultimate performance.”

Without knowing which neurons need to become more active and which ones should be reined in, it is impossible to look for signals directing those changes.

Understanding neuron function

To get around this problem, Harnett’s team developed a brain-computer interface task to directly link neural activity and reward outcome — akin to linking the keys of the piano directly to the activity of single neurons. To succeed at the task, certain neurons needed to increase their activity, whereas others were required to decrease their activity.

They set up a BCI to directly link activity in those neurons — just eight to 10 of the millions of neurons in a mouse’s brain — to a visual readout, providing sensory feedback to the mice about their performance. Success was accompanied by delivery of a sugary reward.

“Now if you ask me, ‘How does the mouse get more rewards? Which neuron do you have to activate and which neuron do you have to inhibit?’ I know exactly what the answer to that question is,” says Francioni, whose work was supported by a Y. Eva Tan Fellowship from the Yang Tan Collective at MIT.

The scientists didn’t know the exact function of the particular neurons they linked to the BCI, but the cells were active enough that mice received occasional rewards whenever the signals happened to be right. Within a week, mice learned to switch on the right neurons while leaving the other set of neurons inactive, earning themselves more rewards.

Francioni monitored the target neurons daily during this learning process using a powerful microscope to visualize fluorescent indicators of neural activity. He zeroed in on the neurons’ branching dendrites, where the appropriate feedback signals have long been suspected to arrive. At the same time, he tracked activity in the parent cell bodies of those neurons. The team used these data to examine the relationship between signals received at a neuron’s dendrites and its activity, as well as how these changed when mice were rewarded for activating the right neurons or when they failed at their task.

Vectorized neural signals

They concluded that the two groups of neurons whose activity controlled the BCI in opposite ways, also received opposing error signals at their dendrites as the mice learned. Some were told to ramp up their activity during the task, while others were instructed to dial it down. What’s more, when the team manipulated the dendrites to inhibit these instructive signals, mice failed to learn the task. “This is the first biological evidence that vectorized [neuron-specific] signal-based instructive learning is taking place in the cortex,” Harnett says.

The discovery of vectorized signals in the brain — and the team’s ability to find them — should promote more back-and-forth between neuroscientists and machine learning researchers, says postdoc Vincent Tang. “It provides further incentive for the machine learning community to keep developing models and proposing new hypotheses along this direction,” he says. “Then we can come back and test them.”

The researchers say they are just as excited about applying their approach to future experiments as they are about their current discovery.

“Machine learning offers a robust, mathematically tractable way to really study learning. The fact that we can now translate at least some of this directly into the brain is very powerful,” Francioni says.

Harnett says the approach opens new opportunities to investigate possible parallels between the brain and machine learning. “Now we can go after figuring out, how does cortex learn? How do other brain regions learn? How similar or how different is it to this particular algorithm? Can we figure out how to build better, more brain-inspired models from what we learn from the biology?” he says. “This feels like a really big new beginning.” 

Improving AI models’ ability to explain their predictions

A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output.

Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use a set of concepts, which can be understood by humans, to make a prediction. In new research, MIT computer scientists developed a method that coaxes the model to achieve better accuracy and clearer, more concise explanations.

The concepts the model uses are usually defined in advance by human experts. For instance, a clinician could suggest the use of concepts like “clustered brown dots” and “variegated pigmentation” to predict that a medical image shows melanoma.

But previously defined concepts could be irrelevant or lack sufficient detail for a specific task, reducing the model’s accuracy. The new method extracts concepts the model has already learned while it was trained to perform that particular task, and forces the model to use those, producing better explanations than standard concept bottleneck models.

The approach utilizes a pair of specialized machine-learning models that automatically extract knowledge from a target model and translate it into plain-language concepts. In the end, their technique can convert any pretrained computer vision model into one that can use concepts to explain its reasoning.

“In a sense, we want to be able to read the minds of these computer vision models. A concept bottleneck model is one way for users to tell what the model is thinking and why it made a certain prediction. Because our method uses better concepts, it can lead to higher accuracy and ultimately improve the accountability of black-box AI models,” says lead author Antonio De Santis, a graduate student at Polytechnic University of Milan who completed this research while a visiting graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.

He is joined on a paper about the work by Schrasing Tong SM ’20, PhD ’26; Marco Brambilla, professor of computer science and engineering at Polytechnic University of Milan; and senior author Lalana Kagal, a principal research scientist in CSAIL. The research will be presented at the International Conference on Learning Representations.

Building a better bottleneck

Concept bottleneck models (CBMs) are a popular approach for improving AI explainability. These techniques add an intermediate step by forcing a computer vision model to predict the concepts present in an image, then use those concepts to make a final prediction.

This intermediate step, or “bottleneck,” helps users understand the model’s reasoning.

For example, a model that identifies bird species could select concepts like “yellow legs” and “blue wings” before predicting a barn swallow.

But because these concepts are often generated in advance by humans or large language models (LLMs), they might not fit the specific task. In addition, even if given a set of pre-defined concepts, the model sometimes utilizes undesirable learned information anyway, which is a problem known as information leakage.

“These models are trained to maximize performance, so the model might secretly use concepts we are unaware of,” De Santis explains.

The MIT researchers had a different idea: Since the model has been trained on a vast amount of data, it may have learned the concepts needed to generate accurate predictions for the particular task at hand. They sought to build a CBM by extracting this existing knowledge and converting it into text a human can understand.

In the first step of their method, a specialized deep-learning model called a sparse autoencoder selectively takes the most relevant features the model learned and reconstructs them into a handful of concepts. Then, a multimodal LLM describes each concept in plain language.

This multimodal LLM also annotates images in the dataset by identifying which concepts are present and absent in each image. The researchers use this annotated dataset to train a concept bottleneck module to recognize the concepts.

They incorporate this module into the target model, forcing it to make predictions using only the set of learned concepts the researchers extracted.

Controlling the concepts

They overcame many challenges as they developed this method, from ensuring the LLM annotated concepts correctly to determining whether the sparse autoencoder had identified human-understandable concepts.

To prevent the model from using unknown or unwanted concepts, they restrict it to use only five concepts for each prediction. This also forces the model to choose the most relevant concepts and makes the explanations more understandable.

When they compared their approach to state-of-the-art CBMs on tasks like predicting bird species and identifying skin lesions in medical images, their method achieved the highest accuracy while providing more precise explanations.

Their approach also generated concepts that were more applicable to the images in the dataset. 

“We’ve shown that extracting concepts from the original model can outperform other CBMs, but there is still a tradeoff between interpretability and accuracy that needs to be addressed. Black-box models that are not interpretable still outperform ours,” De Santis says.

In the future, the researchers want to study potential solutions to the information leakage problem, perhaps by adding additional concept bottleneck modules so unwanted concepts can’t leak through. They also plan to scale up their method by using a larger multimodal LLM to annotate a bigger training dataset, which could boost performance.

“I’m excited by this work because it pushes interpretable AI in a very promising direction and creates a natural bridge to symbolic AI and knowledge graphs,” says Andreas Hotho, professor and head of the Data Science Chair at the University of Würzburg, who was not involved with this work. “By deriving concept bottlenecks from the model’s own internal mechanisms rather than only from human-defined concepts, it offers a path toward explanations that are more faithful to the model and opens many opportunities for follow-up work with structured knowledge.”

This research was supported by the Progetto Rocca Doctoral Fellowship, the Italian Ministry of University and Research under the National Recovery and Resilience Plan, Thales Alenia Space, and the European Union under the NextGenerationEU project.

Personal tech, social media, and the “decline of humanity”

In Compton Lecture at MIT, social psychologist Jonathan Haidt warns of dramatic global decay in cognition, attention spans, and civic life, and urges curbs to tech use.

Social psychologist Jonathan Haidt presented a forceful analysis of the damage smartphones and social media are doing to our cognition, our civic fabric, and our children’s wellbeing, while calling for renewed action to ward off their effects, in the latest of MIT’s Compton Lectures on Wednesday.

“Around the world, people are getting diminished,” Haidt said. “Less intelligent, less happy, less competent. And it’s happening very fast … My argument is that if we continue with current trends as AI is coming in, it’s going to accelerate. The decline of humanity is going to accelerate.”

Haidt is the Thomas Cooley Professor of Ethical Leadership at New York University’s Stern School of Business and the author of the recent bestseller “The Anxious Generation,” which suggests that the widespread adoption of social media in the 2010s has been especially damaging to young women, making them prone to anxiety and depression.

But as Haidt has continued to examine the effects of social media on society, he has started focusing on additional issues. Our inability to put our phones away, our compulsion to check social media, and the way we spend hours a day watching short-form videos, may be causing problems that go far beyond any rise in anxiety and depression.

“It turns out, it’s not the biggest thing,” Haidt said. “There’s something bigger. It is the destruction of the human capacity to pay attention. Because this is affecting most people, including most adults. And if you imagine humanity with 10 to 50 percent of its attentional ability sucked out of it, there’s not much left. We’re not very capable of doing things if we can’t focus or stay on a task for more than 30 seconds.”

Whatever solution may emerge to these problems, Haidt declared, is going to have to come from “human agency. People see a problem, they figure out a way around it. That’s what I’m hoping to promote here [to] this very important audience. So please consider what I’m saying, these trends, and then work to change them.”

Haidt’s lecture, titled, “Life After Babel: Democracy and Human Development in the Fractured, Lonely World That Technology Gave Us,” was delivered before a capacity audience of over 400 people in MIT’s Huntington Hall (Room 10-250).

The lecture spanned a variety of related topics, with Haidt presenting chart after chart showing the onset of declines in cognition, educational achievement, and happiness, which all have seemed to occur soon after the widespread adoption of smartphones in the 2010s. The individual adoption of smartphones, he notes, has been compounded by the way schools brought internet-connected computing devices into classrooms around the same time.

“The biggest, the most costly mistake we’ve ever made in the history of American education [was] to put computers and high tech on people’s desks,” Haidt said.

Distractible students with shorter attention spans are reading fewer books, he noted; some cinema students cannot sit through films. The top quartile of students is continuing to do well, he noted, but for most students, proficiency levels have dipped notably since the 2010s.

“Fifty years of progress in education, 50 years of progress, up in smoke, gone,” Haidt said. “We’re back to where we were 50 years ago. That’s pretty big, that’s pretty serious.”

As Haidt mentioned multiple times in his remarks, he is not an opponent of all forms of technology, or even personal communication technology, but rather is seeking to mitigate its harmful effects.

“I love tech, I love modernity, we’re all dependent on it, I love my iPhone,” Haidt said. Just as he finished that sentence, an audience member’s cellphone started ringing loudly — drawing a huge laugh from the audience.

“I did not plant that, that was a truly spontaneous demonstration of what I’m talking about,” Haidt said.

Haidt was introduced by MIT President Sally A. Kornbluth, who called him “a leading voice for reforming society’s relationship with technology.” She praised Haidt’s work, noting that he wants to “encourage us to imagine a more positive role for technology in humanity’s future.”

The Karl Taylor Compton Lecture Series was introduced in 1957. It is named for MIT’s ninth president, who led the Institute from 1930 to 1948 and also served as chair of the MIT Corporation from 1948 to 1954.

Compton, as Kornbluth observed, helped MIT evolve from being more strictly an engineering school into “a great global university” with “a new focus on fundamental scientific research.” During World War II, she added, Compton “helped invent the longstanding partnership between the federal government and America’s research universities.”

Haidt received his undergraduate degree from Yale University and his PhD from the University of Pennsylvania. He taught on the faculty at the University of Virginia for 16 years before joining New York University. He has written several widely discussed books about contemporary civic life. Haidt observed that the problems stemming from device distraction and compulsion appear to have hit so-called Gen Z — those born from roughly the mid 1990s to the early 2010s — especially hard, though he emphasized that people in that cohort are essentially victims of circumstance.

“I am not blaming Gen Z,” Haidt said. “I am saying we raised our kids in a way — we allowed the technology companies to take over childhood. We allowed a few giant companies to own our children’s attention, to show them millions of short videos, to destroy their ability to pay attention, to stop them from reading books, and this is the result.”

For a portion of his remarks, Haidt also examined the consequences of social media for politics, showing data that chart the global diminishment of democracy since the 2010s, while the world has become soaked in misinformation and conflictual online interactions.

“That, I think, is what digital technology has done to us,” Haidt said. “It was supposed to connect us, but instead it has broken things, divided us, and made it very, very hard to ever have common facts, common truths, common stories again.”

Towards the end of his remarks, Haidt also speculated that the effects of using AI will be corrosive as well, intellectually and psychologically.

“AI is not exactly going to make us better at interacting with human beings,” Haidt said.

With all this in mind, what is to be done, to limit the intellectual and social damage from tech devices and social media? For one thing, Haidt suggested, we should be less impressed by high-tech innovations and social media.

“We need to disenthrall ourselves from technology,” Haidt said, paraphrasing a line written by President Abraham Lincoln. He added: “I suggest that we have a generally negative view … of social media and of AI.” This kind of “more emotionally negative or ambivalent view” will make it easier for us to reverse the way technology seems to control us.

As a practical matter, Haidt suggested, that means taking steps to limit our exposure to technology. His own public-advocacy group, The Anxious Generation Movement, suggests a set of four reforms: No smartphones for kids before they are high-school age; no social media before age 16; making school phone-free, from bell to bell; and giving kids more independence, free play, and responsibility in the world.

Certainly there is movement toward some of these concepts. Some school districts in the U.S. are banning or limiting phone usage; Australia has also instituted a ban on social media for anyone under 16, while a handful of other countries have announced similar plans.

“There’s a gigantic techlash happening right now,” Haidt suggested. For all the sudden changes technology has introduced within the last 15 years, it is still possible, for now, for people to find a way out of our tech-induced predicament.

“The good news is, there is human agency,” Haidt said.

Seeds of something different

Kate Brown’s book, “Tiny Gardens Everywhere,” examines the hidden history of urban farming, its extensive use, and the politics of growing food.

In Berlin in the early 1870s, tourists began visiting a neighborhood called Barackia. It did not have museums, palaces, or any other typical attractions. Barackia was a working-class neighborhood where people grew their own food, lived in small dwellings, and established communal arrangements outside the normal reach of government. For a while, anyway: In 1872, authorities moved in and cleared out Barackia.

Still, the concept of small urban farming caught on, and by 1900, about 50,000 Berlin households were growing food, often in so-called arbor colonies. The practice has never really been abandoned: Today, by law, Germany provides residents the right to garden, still a very popular activity in urban areas.

“In a little space, you can grow a lot of produce,” says MIT Professor Kate Brown, author of a new history of urban gardening. “Once you set things up, it need not take too much of your time. You can have another job and still grow food. You go to Berlin, and many German cities, and you’re surrounded by these allotment gardens.”

But as the residents of Barackia found out, there is a politics that comes with growing your own food on common land. Other interests may want to claim or at least control the land themselves. Or they may want to tap into the labor being applied to gardening. One way or another, when many people start gardening for themselves, core questions about the organization of society seem to sprout up, too.

Brown examines urban gardening and its politics in her book, “Tiny Gardens Everywhere: The Past Present, and Future of the Self-Provisioning City,” published by W.W. Norton. Brown is the Thomas M. Siebel Distinguished Professor in History of Science within MIT’s Program in Science, Technology, and Society. In a book with global scope, ranging from Estonia to Amsterdam and Washington, Brown contends that urban gardening has many positive spillover effects, from health and environmental benefits to community-building — apart from periods of pushback when others are trying to eliminate it.

“Community after community, people work together to create food provisioning practices,” Brown says. “And after people come together for food and gardening, then they start to solve other problems they have.”

Whose land?

“Tiny Gardens Everywhere” was several years in making, featuring extensive archival research, with firsthand material interspersed too. Brown’s story begins in England, which had a very long tradition of people farming on common land, often in ingenious, productive ways. “Every bit of space was used,” Brown says.

Then in the late 18th century, the advent of “enclosures” for wealthy landowners privatized much land and changed social life for many. Poorer residents, even when given allotments, found them not big enough for self-sustaining farming.

“Private property is largely an English invention of the late 18th century,” Brown says. “Before that, and in many parts of the world to this day, people live with a communal sense of the ownership of the land.”

In Brown’s interpretation, the enclosure movement did not just claim more land for Britain’s upper class. In an industrializing society, it forced peasants into the factory labor force, whether in cities or in rural mills.

“Really what they were doing when they were enclosing land was trying to control labor, as much as controlling land,” Brown says. “Because of their reliance on the commons, peasants were self-sufficient. Who wants to go work in a factory when you could be out having fun in the forest? Expelling people was a way to force them to become homeless, the landless proletariat, with nothing to sell but their labor, for 10 or 18 hours a day.”

As Brown chronicles in detail, conflicts between communal agriculture and propertied classes have often arisen since then, in varying forms. And sometimes, in now-surprising places, because urban gardening has been more extensive than we realize.

A core section of “Tiny Gardens Everywhere” focuses on Washington, in the middle of the 20th century. During the Great Migration, which started a few decades earlier, African Americans moved north en masse, resettling in cities. They brought extensive knowledge with them about agricultural practices. In the part of Washington east of the Anacostia River, Black neighborhoods relied heavily on local gardening.

“They set up workers’ cooperatives and food cooperatives,” Brown observes. Despite often living in difficult circumstances, she adds, “I think it’s very interesting that people found really smart ways to adapt. If the neighborhood had no garbage collection, they’ll compost. No sewers, they’ll compost.”

Over time, though, authorities started claiming more land, designating homes to be torn down, and restricting the ability of residents to garden. And as Brown chronicles in the book, local officials have used restrictions on urban gardening as a form of social control, with one outcome being a homogenized social and physical landscape characterized by grass lawns for the affluent.

How much food?

Even if urban gardening has been fairly common in the past, it is natural to ask: How much food can it really provide? As Brown sees it, there is not one simple answer to that question. At one point, victory gardens provided about 40 percent of all produce grown in the U.S. during World War II, for one thing. More recently, In 1996, 91 percent of the potatoes Russians ate came from urban allotment gardens on 1.5 percent of the country’s arable land.

As Brown also points out in the book, we may not be growing as much produce on giant farms as we think. Only 2 percent of agricultural land in the U.S. is used to produce fruit and vegetables, for instance. The U.S., as a variety of analysts and writers have observed, has corn-and soy-heavy agricultural systems at its largest scales, principally yielding corn-based products. That means, Brown says, “They’re really inefficiently [working] to produce ethanol, corn syrup, chips, and cookies.”

In sum, she adds, “Yes, I do think it’s possible to take an urban space and grow a good part of the fruits and vegetables that people need there.”

It is possible, Brown believes, for things to change on this front. For instance, Florida, Illinois, and Maine, three fairly different states in terms of politics, all have laws providing the right to garden. Oklahoma has a similar bill in the works.

“I think this approach to looking at our right to grow food, to self-provision, to step outside of markets for our most essential needs, is something that represents a unifying set of desires in our hyperpolarized political landscape,” Brown says.

Other scholars have praised “Tiny Gardens Everywhere.” Sunil Amrith, a professor of history at Yale University, has said that Brown uses “enviable skill, craft, and insight” to show “that the past of small-scale urban provisioning contains the seeds of a more resilient future for us all.”

For her part, Brown hopes the book will not only appeal to readers, but spur them to become more active about the issue, as gardeners, local policy advocates, or both.

“One of the drumbeats of this book is that people do — and maybe we all should — win the right to garden,” Brown says. 

Studying the genetic basis of disease to explore fundamental biological questions

Eliezer Calo’s studies of craniofacial malformations have yielded insight into protein synthesis and embryonic development.

When Associate Professor Eliezer Calo PhD ’11 was applying for faculty positions, he was drawn to MIT not only because it’s his alma mater, but also because the Department of Biology places high value on exploring fundamental questions in biology.

In his own lab, Calo studies how craniofacial malformations arise. One motivation is to seek new treatments for those conditions, but another is to learn more about fundamental biological processes such as protein synthesis and embryonic development.

“We use genes that are mutated in disease to uncover fundamental biology,” Calo says. “Mutations that happen in disease are an experiment of nature, telling us that those are the important genes, and then we follow them up not only to understand the disease, but to fundamentally understand what the genes are doing.”

Calo’s work has led to new insights into how ribosomes form and how they control protein synthesis, as well as how the nucleolus, the birthplace of ribosomes in eukaryotic cells, has evolved over hundreds of millions of years.

In addition to earning his PhD at MIT, Calo is also an alumnus of MIT’s Summer Research Program (MSRP), which helps to prepare undergraduate students to pursue graduate education. Since starting his lab at MIT, Calo has made a point to serve as a research mentor for the program every summer.

“I feel that it’s important to pay back to the program that helped me realize what I wanted to do,” he says.

A nontraditional path

Growing up in a mountainous region of Puerto Rico, Calo was the first person from his family to finish high school. While attending the University of Puerto Rico at Rio Piedras, the largest university in Puerto Rico, he explored a few different majors before settling on chemistry.

One of Calo’s chemistry professors invited him to work in her lab, where he did a research project studying the pharmacokinetics of cell receptors found on the surface of astrocytes, a type of brain cell.

“It was a good mix of biology and chemistry,” he says. “I think that that was the catalyst to my pursuit of a career in the sciences.”

He learned about MSRP from Mandana Sassanfar, a senior lecturer in biology at MIT and director of outreach for several MIT departments, at an event hosted by the University of Puerto Rico for students interested in careers in science. He was accepted into the program, and during the summer after his junior year, he worked in the lab of Stephen Bell, an MIT professor of biology. That experience, he says, was transformative.

“Without that experience, I would have probably chosen another career,” Calo says. In Puerto Rico, “science was fun, but it was a struggle. We had to make everything from scratch, and then you spend more time making reagents than doing the experiments. When I came to MIT, I was always doing experiments.”

During that time, he realized he liked working in biology labs more than chemistry labs, so when he applied to graduate school, he decided to move into biology. He applied to five schools, including MIT. “Once MIT sent me the acceptance, I just had to say yes. There was no saying no.”

At MIT, Calo thought he might study biochemistry, but he ended up focusing on cancer biology instead, working with Jacqueline Lees, an MIT biology professor, to study the role of the tumor suppressor protein Rb.

After finishing his PhD, Calo felt burnt out and wasn’t sure if he wanted to continue along the academic track. His thesis committee advisors encouraged him to do a postdoc just to try it out, and he ended up going to Stanford University, where he fell in love with California and switched to a new research focus. Working with Joanna Wysocka, a professor of developmental biology at Stanford, he began investigating how development is affected by the regulation of proteins that make up cellular ribosomes — a topic his lab still studies today.

Returning to MIT

When searching for faculty jobs, Calo focused mainly on schools in California, but also sent an application to MIT. As he was deciding between offers from MIT and the University of California at Berkeley, a phone call from Angelika Amon, the late MIT professor of biology, convinced him to take the cross-country leap back to MIT.

“She had me on the phone for more than one hour telling me why I should come to MIT,” he recalls. “And that was so heartwarming that I could not say no.”

Since starting his lab in 2017, Calo has been studying how defects in the production of ribosomes give rise to diseases, in particular craniofacial malformations such as cleft palate.

Ribosomes, the organelles where protein synthesis occurs, consist of two subunits made of about 80 proteins. A longstanding question in biology has been why mutations that affect ribosome formation appear to primarily affect the development of the face, but not the rest of the body.

In a 2018 study, Calo discovered that this is because the mutations that affect ribosomes can have secondary effects that influence craniofacial development. In embryonic cells that form the face, a mutation in a gene called TCOF1 activates p53 at a higher level than in other embryonic cells. High levels of p53 cause some of those cells to undergo programmed cell death, leading to Treacher-Collins Syndrome, a disorder that produces underdeveloped bones in the jaw and cheek.

His lab has shown that p53 overactivation is also responsible for craniofacial disorders caused by mutations in RNA splicing factors.

Calo’s work on ribosome formation also led him to explore another cell organelle known as the nucleolus, whose role is to help build ribosomes. In 2023, he found that a gene called TCOF1, which can lead to craniofacial malformations when mutated, is critical for forming the three compartments that make up the nucleolus.

That finding, he says, could help to explain a major evolutionary shift that occurred around 300 million years ago, when the nucleolus transitioned from two to three compartments. This “tripartite” nucleolus is found in all reptiles, birds, and mammals.

“That was quite surprising,” Calo says. “Studying disease-related genes allowed us to understand a very fundamental biological process of how the nucleolus evolved, which has been a question in the field that nobody could figure out the answer for.”

X-raying rocks reveals their carbon-storing capacity

New research by MIT geophysicists could assist efforts to remove carbon from the atmosphere and store it underground.

To avoid the worst effects of climate change, many billions of metric tons of industrially generated carbon dioxide will have to be captured and stored away by the end of this century. One place to store such an enormous amount of greenhouse gas is in the Earth itself. If carbon dioxide were pumped into the cracks and crevices of certain underground rocks, the fluid would react with the rocks and solidify carbon into minerals. In this way, carbon dioxide could potentially be locked in the rocks in stable form for millions of years without escaping back into the atmosphere.

Some pilot projects are already underway to demonstrate such “carbon mineralization.” These efforts have shown promising results in terms of successfully mineralizing a large fraction of injected CO₂. However, it’s less clear how the rocks will evolve in response. As carbonate minerals build up, could they clog up cracks and crevices, and ultimately limit the amount of CO₂ that can be stored there?

In a new study appearing today in the journal AGU Advances, MIT geophysicists explored this question by injecting fluid into rocks and using X-ray imaging to reveal how the rocks’ pores and cracks changed as the fluid mineralized over time.

Their experiments showed that as fluid was pumped into a rock, the rock’s permeability (the ability of fluid to flow through the rock) dropped sharply. Meanwhile, the rock’s porosity (its total amount of empty space, in the form of pores, cracks, and crevices) remained relatively the same.

The researchers found that the minerals were precipitating out of the fluid in the narrower tunnels connecting larger pores, preventing the fluid from flowing into larger pore spaces. Even so, the fluid did keep flowing through the rock, albeit at a lower rate, and minerals continued to form in some cracks and crevices.

“This study gives you information about what the rock does during this complex mineralization process, which could give you ideas of how to engineer it in your favor,” says study co-author Matėj Peč, an associate professor of geophysics at MIT. 

“If you were injecting CO₂ into the Earth and saw a massive drop in permeability, some operators might think they clogged up the well,” adds co-author Jonathan Simpson, a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “But as this study shows, in some cases, it might not matter that much. As long as you maintain some flow rate, you could still form minerals and sequester carbon.”

The study’s co-authors include EAPS Research Scientist Hoagy O’Ghaffari as well as Sharath Mahavadi and Jean Elkhoury of the Schlumberger-Doll Research Center.

Drilling down

Basalt is a type of erupted volcanic rock that is found in places such as Hawaii and Iceland. When fresh, it’s highly porous, with many pores, cracks, and fractures running through the rock. The material also is highly concentrated in iron, calcium, and magnesium. When these elements come in contact with fluid that is rich in carbon dioxide, they can dissolve and mix with CO₂, and eventually form a new carbon-based mineral such as calcite or dolomite.

A project based in Iceland and piloted by the company CarbFix is currently injecting CO₂-rich water into the region’s underground basalt to see how much of the gas can be converted and stored as minerals in the rock. The company’s runs have shown that more than 95 percent of the CO₂ injected into the ground turns into minerals within two years. The project is proving that the chemistry works: CO₂ can be stored as stone.

But the MIT team wondered how this mineralization process would change the basalt itself and its capacity to store carbon over time.

“Most studies investigating carbon mineralization have focused on optimizing the geochemistry, but we wanted to know how mineralization would affect real reservoir rocks,” Peč says.

Rocky X-rays

The team set out to study how the permeability and porosity of basalt changes as carbonate-rich fluid is pumped into and mineralized throughout the rock.

“Porosity refers to the total amount of open space in the rock, which could be in the form of vesicles, or fractures that connect vesicles, or even areas between sand grains,” Simpson explains. “Because there is so much variability in porosity patterns, there is no one-to-one relationship between porosity and permeability. You could have a lot of pores that are not necessarily connected. So, even if 20 percent of the rock is porous, if they’re not connected, then permeability would be zero.”

“The details of that are important to understand for all these problems of injecting fluids into the subsurface,” Peč emphasizes.

For their experiments, the team used samples of basalt that Peč and others collected during a trip to Iceland in 2023. They placed small samples of basalt in a custom-built holder that they connected to two tubes, through which they flowed two different fluids, each containing a solution that, when mixed, quickly forms carbonate minerals. The team chose this combination of fluids in order to speed up the mineralization process.

In the actual process of injecting CO₂ into the ground, CO₂ is mixed with water. When it is pumped through rock, the fluid first goes through a “dissolution” phase, in which it draws elements such as iron, calcium, and magnesium out from the basalt and into the CO₂-rich fluid. This dissolution process can take some time, before the mineralization process, in which CO₂ mixes with the drawn-out elements, can proceed.

The researchers used two different fluids that quickly mineralize when combined, in order to skip over the dissolution phase and efficiently study the effects of the mineralization process. The team was able to see the mineralization process occurring within the rock, at an unprecedented level of detail, by performing experiments inside an X-ray CT scanner. The team set up their experiment in a CT scanner (similar to the ones used for medical imaging in hospitals) and took frequent, high-resolution, three-dimensional snapshots of the basalt periodically over several days to weeks as they flowed the fluids through.

Their imaging revealed how the pores, cracks, and crevices in the rock evolved, and filled in with minerals as the fluid flowed through over time. Over multiple experiments, they found that the rock’s permeability quickly dropped within a day, by an order of magnitude. The rock’s porosity, however, decreased at a much slower rate. At the end of the longest-duration experiments, only about 5 percent of the original pore space was filled with new minerals.

“Our findings tell us that the minerals are initially forming in really small microcracks that connect the bigger pore spaces, and clogging up those spaces,” Simpson says. “You don’t need much to clog up the tiny microfractures. But when you do clog them up, that really drops the permeability.”

Even after the initial drop in permeability, however, the team could continue to flow fluid through, and minerals continued to form in tight spaces within the rock. This suggests that even when it seems like an underground reservoir is full, it might still be able to store more carbon.

The researchers also monitored the rock with ultrasonic sensors during each experiment and found that the sensor could track even small changes in the rock’s porosity. The less porous, or more filled in the rock was with minerals, the faster sound waves traveled through the material. These results suggest that seismic waves could be a reliable way to monitor the porosity of underground rocks and ultimately their capacity to store carbon.

“Overall, we think that carbon mineralization seems like a promising avenue to permanently store large volumes of CO₂,” Peč concludes. “There are plenty of reservoirs and they should be injectable over extended periods of time if our results can be extrapolated.”

This work was supported by MIT’s Advanced Carbon Mineralization Initiative funded by Beth Siegelman SM ’84 and Russ Siegelman ’84, with additional funding from the Chan-Zuckerberg Foundation.

A winning formula for student project teams at MIT

The teamwork, leadership, and communication skills developed in the Gordon Engineering Leadership (GEL) Program drive success of Edgerton Center project teams.

When Francis Wang ’21, MEng ’22 first joined the MIT Edgerton Center’s Solar Electric Vehicle Team (SEVT), his approach to engineering projects was “to focus my energy and attention on a tidy problem with neat boundaries that I could completely control.”

“But on Solar Car, I realized it takes a very different mindset to manage a substantial project with many moving pieces. It takes engineering leadership,” he recalls.

Wang was determined to strengthen his leadership skills. When he became Solar Car captain, he applied and was accepted into the Gordon Engineering Leadership (GEL) Program.

GEL’s courses and hands-on labs equip students with capabilities they need to lead and contribute to complex, real-world engineering challenges. The one- or two-year program for juniors and seniors complements MIT’s technical education, teaching teamwork, leadership, and communication skills in an engineering context. GEL students also benefit from personalized coaching, mentoring, industry networking, and career support throughout their professional lives.

“Before GEL, I saw the leadership parts of my role as a necessary evil to get to the actual interesting parts, which was the engineering,” says Wang. “The GEL Program gave me an understanding of how engineering leadership is crucial, because in the real world any project worth working on is larger than the scope of an individual engineer.”

In GEL he improved capabilities such as decision-making, taking initiative, and negotiating. He became a more effective SEVT team captain, able to navigate the challenges of taking an engineering project from concept to completion.

“It was often the case that the challenges I faced on Solar Car were not solely technical, involving aspects of communication, coordination, and negotiation. From GEL, I had the framework and the language to approach them,” says Wang.

Each year, 30-40 Edgerton students are accepted into the GEL Program. They come from a variety of teams and clubs including Arcturus, Assistive Technology Club, ChemE Club, Combat Robotics Club, Design Build Fly (DBF), Design for America, Electric Vehicle Team, Engineers Without Borders, First Nations Launch, MIT Electronics Research Society (MITERS), Motorsports, Robotics Team, Rocket Team, and Solar Electric Vehicle Team (SEVT).

“MIT’s best engineering students have GEL training and authentic project management experience with our competition teams,” says Professor J. Kim Vandiver, director of the Edgerton Center.

Edgerton project teams are entirely student-run organizations responsible for all levels of project and team management including fundraising, recruiting, designing, testing, risk mitigation, and project validation. The most successful teams have skilled leaders.

“Many of the excellent Edgerton project team students admitted to GEL are team or sub-team leaders who credit their GEL experience, particularly the experiential learning component, with improving their leadership skills,” says Leo McGonagle, executive director of GEL.

“It’s a win-win-win. GEL gets hard-working, motivated Edgerton Program students who are intent on self-development and improvement. Edgerton project teams often perform better with leaders who are GEL-trained. And the students gain leadership, teamwork, and communication abilities that they can use beyond their project team — in their capstones, course projects, internships, and jobs after MIT,” says McGonagle.

The overlapping connection between GEL and Edgerton truly becomes obvious when students begin to take ownership of project milestones.

“When you become the leader of a technical project, no one gives you a roadmap to team success,” says senior Hailey Polson, former captain of First Nations Launch team. “Technical expertise is not enough to leverage the talent and skills of an entire team or the ability to coordinate a multifaceted project; that’s where the tools, skills, and leadership theory I learned in GEL helped me bridge the gap between knowing how to accomplish our goals and actually leading my team successfully.”

Faris Elnager ’25 served as testing lead on the Motorsports team, which designs, manufactures, and competes with a formula-style electric race car every year.

“Making tough decisions was something that I learned in GEL. On Motorsports, I had to make high-stakes decisions about testing time that affected how we performed at a competition,” he says.

He found that GEL’s weekly Engineering Leadership Labs were a way to test for himself specific leadership capabilities that he could use to improve his Motorsports team.

“One of the most useful skills from GEL was evaluating your stakeholders and learning how to balance their needs. I remember thinking, we’re doing this right now in the [GEL] lab, and then we’re going back to the [Edgerton] shop to do this for real!” says Elnager. “It’s like a positive feedback loop. GEL labs make you better on project teams, and project teams make you better in GEL.”

Now a startup co-founder, Elnager says that the communication skills that he learned through Motorsports and GEL have been critical to his company’s early success. “You can build the best tech in the world. If you can’t pitch it to people, you’re never going to raise any money. Being able to explain a technical project to anyone, whether they're an investor or someone in your industry, is something that’s incredibly valuable.”

Adrienne Lai ’25 served as both mechanical lead and then captain of the Solar Electric Vehicle Team. She recalls how her GEL training would kick in on race day.

“It’s quite tricky to be captain of a build team, because there’s no adult to tell you what to do. You have to figure it all out for yourself. When you’re competing, it can be very chaotic. You are trying to maximize a score by driving more miles, but that comes with a trade-off of spending energy or ending the day in a more rural area, or with less sun, so there are a lot of trade-offs to consider. Sometimes someone just has to make a decision. I was very comfortable doing that because I had learned how to take initiative, which is one of the GEL capabilities,” she says.

Now a course assistant in GEL, Lai helps design scenarios that enable GEL students to become better and more resilient leaders. She particularly enjoys playing the role of an uncooperative supplier.

“We close our store randomly. We don’t have what they need. We won’t tell them what we have,” she laughs. “Students get very frustrated. They think that we’re just being mean. But from a real-world perspective, that is all very true. It simulates unpredictability, which is important not just in a job, but in life.”

The value of the engineering leadership skills learned in GEL and honed on Edgerton project teams carries forward into industry, graduate studies, and entrepreneurial ventures.

“GEL preparation, coupled with authentic project management on a competition team, prepares MIT students for great careers in industry,” says Vandiver.

Henry Smith ’25 says he still relies on skills such as negotiation, communication, and understanding stakeholder needs that he used when he was a Motorsports mechanical lead.

“I was doing high-level management, planning, and organization on the team. Being in the GEL Program really increased my value for the team and helped me be prepared to enter the job field. When I graduated, I wasn’t worried about being ready or not. It was a definite yes,” says Smith.

As project teams continue to address ambitious engineering challenges, the synergy between Edgerton and the Gordon Engineering Leadership (GEL) Program ensures that as students graduate, they’re prepared to not only become strong technical contributors, but confident leaders prepared to tackle complex engineering problems in the real world.

New insights into a hidden process that protects cells from harmful mutations

Research reveals how cells may activate a compensation system that can reduce the effects of harmful genetic mutations. This could inform gene therapy development.



For one learner, online MIT courses are “like getting a Ferrari for the price of an electric scooter”

Senior engineer Badri Ratnam has enhanced his education and career with 40 courses offered by MITx at MIT Open Learning.

As a professional mechanical engineer, Badri Ratnam was inspired when MIT started offering massive open online courses (MOOCs) in engineering and science in 2012. He wondered if he was up to the challenge of solving problem sets and successfully completing exams from MIT.

Ratnam first began his journey with the course 8.MReVx/8.MReV (Mechanics ReView), and he hasn’t looked back since. As he grew in his career in mechanical design and computer-aided engineering, he also completed nearly 40 MITx courses in physics, mechanical engineering, and materials science. 

Part of MIT Open Learning, MITx offers free online courses across a wide variety of subjects to learners around the world. Learners may also opt for the certificate track for a low fee. 

Ratnam has worked for companies such as Freudenberg e-Power Systems, Siemens, GE, and Westport Fuel Systems. His continued learning through MITx courses, as well as courses offered by other universities, has expanded his expertise to include areas such as physics, mechanics of materials, transport phenomena, failure and root cause analysis, validation and verification testing, vibration signal processing, certification and compliance statistical quality control, manufacturing, reliability, supplier selection, and more.

“There are many different learning styles,” says Ratnam. “Some people might need to be in a classroom, and others might be able to learn entirely on their own from a textbook. Personally, I benefit from some amount of structure, including having timelines and deadlines, as well as assignments and discussion forums. With MITx, there is also the excitement of the rigor that can be a boost of adrenaline — trying to see whether you can tackle some of the toughest material, presented by a top institution.”

Supplementing engineering education with extensive course offerings

Ratnam earned a bachelor’s degree in engineering from the University of Delhi. He says during his undergraduate program he tended to study the night before exams, and was “more focused on passing the subject than deep learning.”

He followed his undergrad studies with a master of science degree in mechanical engineering from the University of South Florida and an MS in computational and applied mathematics from Simon Fraser University in British Columbia. Even with all of his degrees, he felt that he needed to revisit the engineering subjects he had initially learned as an undergraduate student, pursuing online courses to review the fundamentals and gain greater understanding and mastery.

The MITx courses Ratnam has taken have covered many different areas within engineering, physics, mathematics, supply chains, and manufacturing. He has recently completed Vibrations and Waves, taught by Yen-Jie Lee, Alex Shvonski, and Michelle Tomasik.

“It’s an 18-week class with over 40 lessons, 13 assignments, and three exams, all designed very deliberately. I don’t think I could have ever learned this very difficult subject without this structure,” says Ratnam. “It’s also important to note that I paid less than $100 for this class. MITx does not follow the dictum that ‘you get what you pay for.’ It’s like getting a Ferrari for the price of an electric scooter.”

Ratnam has also recently finished Information Entropy: Energy and Exergy, taught by former MIT Open Learning dean for digital learning Krishna Rajagopal, Peter Dourmaskin, and Aidan MacDonagh, as well as Shvonski and Tomasik.

Although Ratnam says he can’t pick a favorite course — and is hard-pressed to even pick a few favorites of the many MITx courses he has taken — he says he has especially liked these recent courses and Elements of Structures, taught by Alexie M. Kolpak and Simona Socrate. In addition to the many MITx courses he has taken, he has also completed a few MIT Professional Education programs in smart manufacturing and design. 

“As I’ve taken more and more courses, I’ve learned to never fear learning new things and exploring new areas,” says Ratnam. “I used to think of more unfamiliar subjects and feel a little terrified, not knowing where to start, but I don’t feel that any more. I know that with some time and effort, I can pick up new skills and knowledge.”

Ratnam has found the discussion forums for MITx courses to be especially useful to the learning process.

“This is where the rigorous, engaging, yet automated, courses come to life,” says Ratnam. “Learners from all over the world help each other in the problem sets and discuss their conceptual doubts. And the forums are diligently monitored by MIT staff to ensure there are no open questions, and all errors are corrected.”

Increasing value in the workplace

Ratnam says that his MITx studies have deepened his understanding of a variety of engineering topics, which have given him new insights to apply as an engineer.

“My learnings from MITx courses have really helped me gain the confidence of having a deep understanding on the theoretical side,” says Ratman. “I’ve developed a wide base of knowledge and have become the go-to person whom people come to with questions.”

Ratnam has found MITx to be an excellent professional development resource. He notes that while many professionals have access to and complete courses offered at or through their workplaces, these usually aim to enable people to complete a very specific goal — such as performing a set task at work — within a short period of time. He says that with online courses, it’s a much different timeline and result.

“MITx classes have provided me with a much broader overview of engineering phenomena,” says Ratnam. “The benefit of the classes might not always come immediately. It can be a long gestation period for the information to all gel together. It’s much more of a profound and long-term benefit.”

Explore lifelong learning opportunities from the Institute, including online courses, resources, and professional programs, on MIT Learn.

New catalog more than doubles the number of gravitational-wave detections made by LIGO, Virgo, and KAGRA observatories

The latest crop of space-time wobbles includes a variety of heavy, fast-spinning, and lopsided colliding black holes.

When the densest objects in the universe collide and merge, the violence sets off ripples, in the form of gravitational waves, that reverberate across space and time, over hundreds of millions and even billions of years. By the time they pass through Earth, such cosmic ripples are barely discernible.

And yet, scientists are able to detect them, thanks to a global network of gravitational-wave observatories: the U.S.-based National Science Foundation Laser Interferometer Gravitational-Wave Observatory (NSF LIGO), the Virgo interferometer in Italy, and the Kamioka Gravitational Wave Detector (KAGRA) in Japan. Together, the observatories “listen” for faint wobbles in the gravitational field that could have come from far-off astrophysical smash-ups.

Now the LIGO-Virgo-KAGRA (LVK) Collaboration is publishing its latest compilation of gravitational-wave detections, presented in a forthcoming special issue of Astrophysical Journal Letters. From the findings, it appears that the universe is echoing all over with a kaleidoscope of cosmic collisions.

The LVK’s Gravitational-Wave Transient Catalog-4.0 (GWTC-4) comprises detections of gravitational waves from a portion of the observatories’ fourth and most recent observing run, which occurred between May 2023 and January 2024. During this nine-month period, the observatories detected 128 new gravitational-wave “candidates,” meaning that the signals are likely from extreme, far-off astrophysical sources. (The LVK detected about 300 mergers so far in the fourth run, but not all of these appear yet in the LVK catalog.)

This newest crop more than doubles the size of the gravitational-wave catalog, which previously contained 90 candidates compiled from all three previous observing runs.

“The beautiful science that we are able to do with this catalog is enabled by significant improvements in the sensitivity of the gravitational-wave detectors as well as more powerful analysis techniques,” says LVK member Nergis Mavalvala, who is dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics.

“In the past decade, gravitational wave astronomy has progressed from the first detection  to the observation of hundreds of black hole mergers,” says Stephen Fairhurst, a professor at Cardiff University and LIGO Scientific Collaboration spokesperson. “These observations enable us to better understand how black holes form from the collapse of massive stars, probe the cosmological evolution of the universe and provide increasingly rigorous confirmations of the theory of general relativity.”

“Pushing the edges”

Black holes are created when all the matter in a dying star collapses into a single point. Black holes are therefore among the densest objects in the universe. Black holes often form in pairs, bound together through the gravitational attraction. As they spiral toward each other, they emit enormous amounts of energy in the form of gravitational waves, before merging into a more massive black hole.

A binary black hole was the source of the very first gravitational-wave detection, made by NSF’s LIGO observatories in 2015, and colliding black holes are the source of many of the gravitational waves detected since then. Such “bread-and-butter” binaries typically consist of two black holes of similar size (usually several tens of times more massive than the sun) that merge into one larger black hole.

Gravitational waves can also be produced by the collision of a black hole with a neutron star, which is an extremely dense remnant core of a massive star. While the collision of two black holes only produces gravitational waves, a smash-up involving a neutron star can also generate light, which provides more information about the event that scientists can probe. In its first three observing runs, the LVK observatories detected signals from a handful of collisions involving a black hole and neutron star, as well as two collisions between two neutron stars.

The newest detections published today reveal a greater variety of binaries that produce gravitational waves. In addition to the black hole binaries, the updated catalog includes the heaviest black hole binary; a binary with black holes of asymmetric, lopsided masses; and a binary where both black holes have exceptionally high spins. The catalog also holds two black hole-neutron star binaries.

“The message from this catalog is: We are expanding into new parts of what we call ‘parameter space’ and a whole new variety of black holes,” says co-author Daniel Williams, a research fellow at the University of Glasgow and a member of the LVK. “We are really pushing the edges, and are seeing things that are more massive, spinning faster, and are more astrophysically interesting and unusual.”

Unusual signals

The LIGO, Virgo, and KAGRA observatories detect gravitational waves using L-shaped, kilometer-scale instruments, called interferometers. Scientists send laser light down the length of each tunnel and precisely measure the time it takes each beam to return to its source. Any slight difference in their timing can mean that a gravitational wave passed through and minutely wobbled the laser’s light.

For the first segment of the LVK’s fourth observing run, gravitational-wave detections were made using only LIGO’s identical interferometers — one located in Hanford, Washington, and the other in Livingston, Louisiana. Recent upgrades to LIGO’s detectors enabled them to search for signals from binary neutron stars as far out as 360 megaparsecs, or about 1 billion light-years away, and for signals from binaries including black holes tens of times farther away.

“You can’t ever predict when a gravitational wave is going to come into your detector,” says co-author and LVK member Amanda Baylor, a graduate student at the University of Wisconsin at Milwaukee who was involved in the signal search process. “We could have five detections in one day, or one detection every 20 days. The universe is just so random.”

Among the more unusual signals that LIGO detected in the first phase of the O4 observing run was GW231123_135430, which is the heaviest black hole binary detected to date. Scientists estimate that the signal arose from the collision of two heavier-than-normal black holes, each roughly 130 times as massive as the sun. (Most of the detected merging black holes are around 30 solar masses.) The much heavier black holes of GW231123_135430 suggest that each may be a product of a prior collision of lighter “progenitor” black holes.

Another standout is GW231028_153006, which is a black hole binary with the highest inspiral spin, meaning that both black holes appear to be spinning very fast, at about 40 percent the speed of light. Again, scientists suspect that these black holes were also products of previous mergers that spun them up as they were created from two smaller, inspiraling black holes.

The O4 run also detected GW231118_005626 — an unusually lopsided pair, with one black hole twice as massive as the other. 

“One of the striking things about our collection of black holes is their broad range of properties,” says co-author LVK member Jack Heinzel, an MIT graduate student who contributed to the catalog’s analysis. “Some of them are over 100 times the mass of our sun, others are as small as only a few times the mass of the sun. Some black holes are rapidly spinning, others have no measurable spin. We still don’t completely understand how black holes form in the universe, but our observations offer a crucial insight into these questions.”

Cosmic connections

From the newest gravitational-wave detections, scientists have begun to make connections about the properties of black holes as a population.

“For instance, this dataset has increased our belief that black holes that collided earlier in the history of the universe could more easily have had larger spins than the ones that collided later,” says LVK member Salvatore Vitale, associate professor of physics at MIT and member of the MIT LIGO Lab.

This idea raises interesting questions about what sort of conditions could have spun up black holes in the early universe.

The new detections have also allowed scientists to test Albert Einstein’s general theory of relativity, which describes gravity as a geometric property of space and time.

“Black holes are one of the most iconic and mind-bending predictions of general relativity,” says co-author and LVK member Aaron Zimmerman, associate professor of physics at the University of Texas at Austin, adding that when black holes collide, they “shake up space and time more intensely than almost any other process we can imagine observing. When testing our physical theories, it’s good to look at the most extreme situations we can, since this is where our theories are most likely to break down, and where we have the best chance of discovery.”

Scientists put Einstein’s theory to the test using GW230814_230901, which is one of the “loudest” gravitational-wave signals observed to date. The surprisingly clear signal gave scientists a chance to probe it in detail, to see if any aspects of the signal might deviate from what Einstein’s theory predicts. This signal pushed the limits of their tests of general relativity, passing most with flying colors but illustrating how environmental noise can challenge others in such an extreme scenario.

“So far, the theory is passing all our tests,” Zimmerman says. “But we’re also learning that we have to make even more accurate predictions to keep up with all the data the universe is giving us.”

The updated catalog is also helping scientists to nail down a key mystery in cosmology: How fast is the universe expanding today? Scientists have tried to answer this by measuring a rate known as the Hubble constant. Various methods, using different astrophysical sources, have given conflicting answers.

Gravitational waves offer an alternative way to measure the Hubble constant, since scientists are able to work out, in relatively straightforward fashion, how far these waves traveled from their source.

“Merging black holes have a really unique property: We can tell how far away they are from Earth just from analyzing their signals,” says co-author and LVK member Rachel Gray, a lecturer at the University of Glasgow who was involved in the cosmological interpretations of the catalog’s data. “So, every merging black hole gives us a measurement of the Hubble constant, and by combining all of the gravitational wave sources together, we can vastly improve how accurate this measurement is.”

By analyzing all the gravitational-wave detections in the LVK’s entire catalog, scientists have come up with a new, independent estimate of the Hubble constant, that suggests the universe is expanding at a rate of 76 kilometers, per second, per megaparsec (a square volume of about half a billion light-years wide).

“It’s still early days for this method, and we expect to significantly improve our precision as we detect more gravitational wave sources,” Gray says.

“Each new gravitational-wave detection allows us to unlock another piece of the universe’s puzzle in ways we couldn’t just a decade ago,” says Lucy Thomas, who led part of the catalog’s analysis, and is a postdoc in the Caltech LIGO Lab. “It’s incredibly exciting to think about what astrophysical mysteries and surprises we can uncover with future observing runs."

Nitrous oxide, a product of fertilizer use, may harm some soil bacteria

While some N2O is produced naturally at the plant root, agricultural practices can increase its levels, to the detriment of some microbes that support plant growth.

Plant growth is supported by millions of tiny soil microbes competing and cooperating with each other as they perform important roles at the plant root, including improving access to nutrients and protecting against pathogens. As a byproduct of their metabolism, soil microbes can also produce nitrous oxide, or N₂O, a potent greenhouse gas that has mostly been studied for its impact on the climate. While some N₂O occurs naturally, its production can spike due to fertilizer application and other factors.

While it has long been believed that nitrous oxide doesn’t meaningfully interact with living organisms, a new paper by two MIT researchers shows that it may in fact shape microbial communities, making some bacterial strains more likely to grow than others.

Based on the prevalence of the biological processes disrupted by nitrous oxide, the researchers estimate about 30 percent of all bacteria with sequenced genomes are susceptible to nitrous oxide toxicity, suggesting the substance could play an important and underappreciated role in the intricate microbial ecosystems that influence plant growth.

The researchers have published their findings today in mBio, a journal of the American Society for Microbiology. If their lab findings carry over to agricultural settings, it could influence the way farmers go about everyday tasks that expose crops to spikes in nitrous oxide, such as watering and fertilization.

“This work suggests N₂O production in agricultural settings is worth paying attention to for plant health,” says senior author Darcy McRose, MIT’s Thomas D. and Virginia W. Cabot Career Development Professor, who wrote the paper with lead author and PhD student Philip Wasson. “It hasn’t been on people’s radar, but it is particularly harmful for certain microbes. This could be another knock against N₂O in addition to its climate impact. With more research, you might be able to understand how the timing of N₂O production influences these microbial relationships, and that timing could be managed to improve crop health.”

A toxic gas

Nitrous oxide was shown to be toxic decades ago when researchers realized it can deactivate vitamin B12 in the human body. Since then, it has mostly drawn attention as a long-lived greenhouse gas that can eat away at the ozone. But when it comes to agricultural settings, most people have assumed it doesn’t interact with organisms growing in the soil around the plant root, a region called the rhizosphere.

“In general, there’s an assumption that N₂O is not harmful at all despite this history of published studies showing that it can be toxic in specific contexts,” says McRose, who joined the faculty of the Department of Civil and Environmental Engineering in 2022. “People have not extended that understanding to microbial communities in the rhizosphere.”

While some studies have shown nitrous oxide sensitivity in a handful of microorganisms, less is known about how it impacts the distribution of microbial communities at the plant root. McRose and Wasson sought to fill that research gap.

They started by looking at a ubiquitous process that cells use to grow called methionine biosynthesis. Methionine biosynthesis can be carried out by enzymes that are dependent on B12 — and by other enzymes that are not. Many bacteria have both types.

Using a well-studied microbe named Pseudomonas aeruginosa, the researchers genetically removed the enzyme that isn’t dependent on B12 and found the microbe became sensitive to nitrous oxide, with its growth harmed even by nitrous oxide it produced itself.

Next the researchers looked at a synthetic microbial community from the plant Arabidopsis thaliana, finding many root-based microbes were also sensitive to nitrous oxide. Combining sensitive microbes with nitrous oxide-producing bacteria hampered their growth.

“This suggests that N₂O-producing bacteria can affect the survival of their immediate neighbors,” Wasson explains. Together, the experiments confirmed the researchers’ suspicion that the production of nitrous oxide can hamper the growth of soil bacteria dependent on vitamin B12 to make methionine.

“These results suggest nitrous oxide producers shape microbial communities,” McRose says. “In the lab the result is very clear, and the work goes beyond just looking at a single organism. The co-culture experiments aren’t the same as a study in the field, but it’s a strong demonstration.”

From the lab to the farm

In farms, soil commonly experiences spikes of nitrous oxide for days or weeks from the addition of nitrogen fertilizer, rainfall, thawing, and other events. The researchers caution that their lab experiments are only the first step toward understanding how nitrous oxide affects microbial populations in agricultural settings.

Wasson calls the paper a proof of concept and plans to study agricultural soil next.

“In agricultural environments, N₂O has been historically high,” Wasson says. “We want to see if we can detect a signature for this N₂O exposure through genome sequencing studies, where the only microbes sticking around are not sensitive to N₂O. This is the obvious next step.”

McRose says the findings could lead to a new way for researchers and farmers to think about nitrous oxide.

“What’s important and exciting about this case is it predicts that microbes with one version of an enzyme are going to be sensitive to N₂O and those with a different version of the enzyme are not going to be sensitive,” McRose says. “This suggests that in the environment, exposure to N₂O is going to select for certain types of organisms based on their genomic content, which is a highly testable hypothesis.”

The work was supported, in part, by the MIT Research Support Committee and a MIT Health and Life Sciences Collaborative Graduate Fellowship (HEALS).

How some skills become second nature

Patterns of gaze and attention can reveal how some people unconsciously figure out how to master a task, new research shows.

Expertise isn’t easy to pass down. Take riding a bike: A seasoned cyclist might talk a beginner through the basics of how to sit and when to push off. But other skills, like how hard to pedal to keep balanced, are more intuitive and harder to articulate. This implicit know-how is known as tacit knowledge, and very often, it can only be learned with experience and time.

But a team of MIT engineers wondered: Could an expert’s unconscious know-how be accessed, and even taught, to quickly bring a novice up to an expert’s level?

The answer appears to be “yes,” at least for a particular type of visual-learning task.

In a study published today in the Journal of Neural Engineering, the engineers identified tacit knowledge in volunteers who were tasked with classifying images of various shapes and patterns. As the volunteers were shown images to organize, the team recorded their eye movements and brain activity to measure their visual focus and cognitive attention, respectively.

The measurements showed that, over time, the volunteers shifted their focus and attention to a part of each image that made it easier to classify. However, when asked directly, the volunteers were not aware that they had made such a shift. The researchers concluded that this unconscious shift in attention and focus was a form of tacit knowledge that the volunteers possessed, even if they could not articulate it. What’s more, when the volunteers were made aware of this tacit knowledge, their accuracy in classifying images improved significantly.

The study is the first to directly show that visual attention can reveal unconscious, tacit knowledge during image classification tasks. It also finds for the first time that bringing this concealed knowledge to the surface can enhance experts’ performance.

While the results are specific to the study’s experiment, the researchers say they suggest that some forms of hidden know-how can be made explicit and applied to boost one’s learning experience. They suspect that tacit knowledge could be accessed for disciplines that require keen observation skills, including certain physical trades and crafts, sports, and image analysis, such as medical X-ray diagnoses.

“We as humans have a lot of knowledge, some that is explicit that we can translate into books, encyclopedias, manuals, equations. The tacit knowledge is what we cannot verbalize, that’s hidden in our unconscious,” says study author Alex Armengol-Urpi, a research scientist in MIT’s Department of Mechanical Engineering. “If we can make that knowledge explicit, we can then allow for it to be transferred easier, which can help in education and learning in general.”

The study’s co-authors include Andrés F. Salazar-Gomez, research scientist at the MIT Media Lab; Pawan Sinha, professor of vision and computational neuroscience in MIT’s Department of Brain and Cognitive Sciences; and Sanjay Sarma, the Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering.

Hidden gaze

The concept of tacit knowledge is credited to the scientist and philosopher Michael Polyani, who in the mid 20th century was the first to investigate the notion that “we know more than we can tell.” His insights revealed that humans can hold a form of knowledge that is internalized, almost second nature, and often difficult to express or translate to others.

Since Polyani’s work, many studies have highlighted how tacit knowledge may play a part in perfecting certain skills, spanning everything from diagnosing medical images to discerning the sex of cats from images of their faces.

For Armengol-Urpi, these studies raised a question: Could a person’s tacit knowledge be revealed through unconscious signals, such as patterns in their eye movements? His PhD work focused on visual attention, and he had developed methods to study how humans focus their attention, by using cameras to follow the direction of their gaze, and electroencephalography (EEG) monitors to record their brain activity. In his research, he learned of a previous study that used similar methods to investigate how radiologists diagnose nodules in X-ray images. That study showed that the doctors unconsciously focused on areas of an image that helped them to correctly detect the nodules.

“That paper didn’t focus on tacit knowledge, but it suggested that there are some hidden clues in our gaze that could be explored further,” Armengol-Urpi says.

The shape of knowledge

For their new study, the team looked at whether they could identify signs of tacit knowledge from measurements of visual focus and attention. In their experiment, they asked 30 volunteers to look sequentially at over 120 images. They could look at each image for several seconds and then were asked to classify the image as belonging to either group A, or group B, before they were shown the next image.

Each image contained two simple shapes on either side of the image — a square, a triangle, a circle, and any combination of the three, along with different colors and patterns for each shape. The researchers designed the images such that they should be classified into one of two groups, based on an intricate combination of shape, color, and pattern. Importantly, only one side of each image was relevant for the classification.

The volunteers, however, were given no guidelines on how to classify the images. Therefore, for about the first half of the experiment, they were considered “novices,” and more or less guessed at their classifications. Over time, and many more images, their accuracy improved to a level that the researchers considered “expert.” Throughout the experiment, the team used cameras to follow each participant’s eye movements, as a measure of visual focus.

They also outfitted volunteers with EEG sensors to record their brain waves, which they used as a measure of cognitive attention. They designed each image to show two shapes, each of which flickered at different, imperceptible frequencies. They found they could identify where a volunteer’s attention landed, based on which shape’s flicker their brain waves synced up with.

For each volunteer, the team created maps of where their gaze and attention were focused, both during their novice and expert phases. Overall, these maps showed that in the beginning, the volunteers focused on all parts of an image as they tried to make sense of how to classify it. Toward the end, as they got a grasp of the exercise and improved their accuracy, their attention shifted to just one side of each image. This side happened to be the side that the researchers designed to be most relevant, while the other side was just random noise.

The maps showed that the volunteers picked up some knowledge of how to accurately classify the images. But when they were given a survey and asked to articulate how they learned the task, they always maintained that they focused on each entire image. It seemed their actual shift in focus was an unconscious, tacit skill.

“They were unconsciously focusing their attention on the part of the image that was actually informative,” Armengol-Urpi says. “So the tacit knowledge they had was hidden inside them.”

Going a step further, the team then showed each participant the maps of their gaze and attention, and how the maps changed from their novice to expert phases. When they were then shown additional images, the volunteers seemed to use this once-tacit knowledge, and further improved their classification accuracy.

“We are currently extending this approach to other domains where tacit knowledge plays a central role,” says Armengol-Urpi, who is exploring tacit knowledge in skilled crafts and sports such as glassblowing and table tennis, as well as in diagnosing medical imaging. “We believe the underlying principle — capturing and reinforcing implicit expertise through physiological signals — can generalize to a wide range of perceptual and skill-based domains.”

This research was supported, in part, by Takeda Pharmaceutical Company.

A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster

The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.

Many engineering challenges come down to the same headache — too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be costly, and there may be hundreds of variables that could matter.

Consider car safety design. Engineers must integrate thousands of parts, and many design choices can affect how a vehicle performs in a collision. Classic optimization tools could start to struggle when searching for the best combination.

MIT researchers developed a new approach that rethinks how a classic method, known as Bayesian optimization, can be used to solve problems with hundreds of variables. In tests on realistic engineering-style benchmarks, like power-system optimization, the approach found top solutions 10 to 100 times faster than widely used methods.

Their technique leverages a foundation model trained on tabular data that automatically identifies the variables that matter most for improving performance, repeating the process to hone in on better and better solutions. Foundation models are huge artificial intelligence systems trained on vast, general datasets. This allows them to adapt to different applications.

The researchers’ tabular foundation model does not need to be constantly retrained as it works toward a solution, increasing the efficiency of the optimization process. The technique also delivers greater speedups for more complicated problems, so it could be especially useful in demanding applications like materials development or drug discovery.

“Modern AI and machine-learning models can fundamentally change the way engineers and scientists create complex systems. We came up with one algorithm that can not only solve high-dimensional problems, but is also reusable so it can be applied to many problems without the need to start everything from scratch,” says Rosen Yu, a graduate student in computational science and engineering and lead author of a paper on this technique.

Yu is joined on the paper by Cyril Picard, a former MIT postdoc and research scientist, and Faez Ahmed, associate professor of mechanical engineering and a core member of the MIT Center for Computational Science and Engineering. The research will be presented at the International Conference on Learning Representations.

Improving a proven method

When scientists seek to solve a multifaceted problem but have expensive methods to evaluate success, like crash testing a car to know how good each design is, they often use a tried-and-true method called Bayesian optimization. This iterative method finds the best configuration for a complicated system by building a surrogate model that helps estimate what to explore next while considering the uncertainty of its predictions.

But the surrogate model must be retrained after each iteration, which can quickly become computationally intractable when the space of potential solutions is very large. In addition, scientists need to build a new model from scratch any time they want to tackle a different scenario.

To address both shortcomings, the MIT researchers utilized a generative AI system known as a tabular foundation model as the surrogate model inside a Bayesian optimization algorithm.

“A tabular foundation model is like a ChatGPT for spreadsheets. The input and output of these models are tabular data, which in the engineering domain is much more common to see and use than language,” Yu says.

Just like large language models such as ChatGPT,  Claude, and Gemini, the model has been pre-trained on an enormous amount of tabular data. This makes it well-equipped to tackle a range of prediction problems. In addition, the model can be deployed as-is, without the need for any retraining.

To make their system more accurate and efficient for optimization, the researchers employed a trick that enables the model to identify features of the design space that will have the biggest impact on the solution.

“A car might have 300 design criteria, but not all of them are the main driver of the best design if you are trying to increase some safety parameters. Our algorithm can smartly select the most critical features to focus on,” Yu says.

It does this by using a tabular foundation model to estimate which variables (or combinations of variables) most influence the outcome.

It then focuses the search on those high-impact variables instead of wasting time exploring everything equally. For instance, if the size of the front crumple zone significantly increased and the car’s safety rating improved, that feature likely played a role in the enhancement.

Bigger problems, better solutions

One of their biggest challenges was finding the best tabular foundation model for this task, Yu says. Then they had to connect it with a Bayesian optimization algorithm in such a way that it could identify the most prominent design features.

“Finding the most prominent dimension is a well-known problem in math and computer science, but coming up with a way that leveraged the properties of a tabular foundation model was a real challenge,” Yu says.

With the algorithmic framework in place, the researchers tested their method by comparing it to five state-of-the-art optimization algorithms.

On 60 benchmark problems, including realistic situations like power grid design and car crash testing, their method consistently found the best solution between 10 and 100 times faster than the other algorithms.

“When an optimization problem gets more and more dimensions, our algorithm really shines,” Yu added.

But their method did not outperform the baselines on all problems, such as robotic path planning. This likely indicates that scenario was not well-defined in the model’s training data, Yu says.

In the future, the researchers want to study methods that could boost the performance of tabular foundation models. They also want to apply their technique to problems with thousands or even millions of dimensions, like the design of a naval ship.

“At a higher level, this work points to a broader shift: using foundation models not just for perception or language, but as algorithmic engines inside scientific and engineering tools, allowing classical methods like Bayesian optimization to scale to regimes that were previously impractical,” says Ahmed.

“The approach presented in this work, using a pretrained foundation model together with high‑dimensional Bayesian optimization, is a creative and promising way to reduce the heavy data requirements of simulation‑based design. Overall, this work is a practical and powerful step toward making advanced design optimization more accessible and easier to apply in real-world settings,” says Wei Chen, the Wilson-Cook Professor in Engineering Design and chair of the Department of Mechanical Engineering at Northwestern University, who was not involved in this research.

Injectable “satellite livers” could offer an alternative to liver transplantation

The engineered tissue grafts could take on the liver’s function and help thousands of people with liver failure.

More than 10,000 Americans who suffer from chronic liver disease are on a waitlist for a liver transplant, but there are not enough donated organs for all of those patients. Additionally, many people with liver failure aren’t eligible for a transplant if they are not healthy enough to tolerate the surgery.

To help those patients, MIT engineers have developed “mini livers” that could be injected into the body and take over the functions of the failing liver.

In a new study in mice, the researchers showed that these injected liver cells could remain viable in the body for at least two months, and they were able to generate many of the enzymes and other proteins that the liver produces.

“We think of these as satellite livers. If we could deliver these cells into the body, while leaving the sick organ in place, that would provide booster function,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science (IMES).

Bhatia is the senior author of the new study, which appears today in the journal Cell Biomaterials. MIT postdoc Vardhman Kumar is the paper’s lead author.

Restoring liver function

The human liver plays a role in about 500 essential functions, including regulation of blood clotting, removing bacteria from the bloodstream, and metabolizing drugs. Most of these functions are performed by cells called hepatocytes.

Over the past decade, Bhatia’s lab has been working on ways to restore hepatocyte function without a surgical liver transplant. One possible approach is to embed hepatocytes into a biomaterial such as a hydrogel, but these gels also have to be surgically implanted.

Another option is to inject hepatocytes into the body, which eliminates the need for surgery. In this study, Bhatia’s lab sought to improve on this strategy by providing an engineered niche that could enhance the cells’ survival and facilitate noninvasive monitoring of graft health.

To achieve that, the researchers came up with the idea of injecting cells along with hydrogel microspheres that would help them stay together and form connections with nearby blood vessels. These spheres have special properties that allow them to act like a liquid when they are closely packed together, so they can be injected through a syringe and then regain their solid structure once inside the body.

In recent years, researchers have explored using hydrogel microspheres to promote wound healing, as they help cells to migrate into the spaces between the spheres and build new tissue. In the new study, the MIT team adapted them to help hepatocytes form a stable tissue graft after injection.

“What we did is use this technology to create an engineered niche for cell transplantation,” Kumar says. “If the cells are injected in the absence of these spheres, they would not integrate efficiently with the host, but these microspheres provide the hepatocytes with a niche where they can stay localized and become connected to the host circulation much faster.”

The injected mixture also includes fibroblast cells — supportive cells that help the hepatocytes survive and promote the growth of blood vessels into the tissue.

Working with Nicole Henning, an ultrasound research specialist at the Koch Institute, the researchers developed a way to inject the cell mixture using a syringe guided by ultrasound. After injection, the researchers can also use ultrasound to monitor the long-term stability of the implant.

In this study, the mini livers were injected into the fat tissue in the belly. In the future, similar grafts could be delivered to other sites in the body, such as into the spleen or near the kidneys. As long as they have enough space and access to blood vessels, the injected hepatocytes can function similarly to hepatocytes in the liver.

“For a vast majority of liver disorders, the graft does not need to sit close to the liver,” Kumar says.

An alternative to transplantation

In tests in mice, the researchers injected the mixture of liver cells and microspheres into an area of fatty tissue known as the perigonadal adipose tissue. Once the cells are localized in the body, they form a stable, compact structure. Over time, blood vessels begin to grow into the graft area, helping the injected hepatocytes to stay healthy.

“The new blood vessels formed right next to the hepatocytes, which is why they were able to survive,” Kumar says. “They were able to get the nutrients delivered right to them, they were able to function the way they're supposed to, and they produced the proteins that we expect them to.”

After injection, the cells remained viable and able to secrete specialized proteins into the host circulation for eight weeks, the length of the study. That suggests that the therapy could potentially work as a long-term treatment for liver disease, the researchers say.

“The way we see this technology is it can provide an alternative to surgery, but it can also serve as a bridge to transplantation where these grafts can provide support until a donor organ becomes available,” Kumar says. “And if we think they might need another therapy or more grafts, the barriers to do that are much less with this injectable technology than undergoing another surgery.”

With the current version of this technology, patients would likely need to take immunosuppressive drugs, but the researchers are exploring the possibility of developing “stealthy” hepatocytes that could evade the immune system, or using the hydrogel microspheres to deliver immunosuppressants locally.

The research was funded by the Koch Institute Support (core) grant from the National Cancer Institute, the National Institutes of Health, the Wellcome Leap HOPE Program, a National Science Foundation Graduate Research Fellowship, and the Howard Hughes Medical Institute.

LAB14 joins the MIT.nano Consortium

The advanced manufacturing group becomes a member and will contribute equipment to MIT.nano.

LAB14 GmbH, a corporate network based in Germany that unites eight high-tech companies focused on nanofabrication, microfabrication, and surface analysis, has joined the MIT.nano Consortium.

“The addition of LAB14 to the MIT.nano Consortium reinforces the importance of collaboration to advance the next set of great ideas,” says Vladimir Bulović, the founding faculty director of MIT.nano and the Fariborz Maseeh (1990) Professor of Emerging Technologies at MIT. “At MIT.nano, we are thrilled when our shared-access facility leads to cross-disciplinary discoveries. LAB14 carries this same motivation by assembling the constellation of remarkable interconnected industry partners.”

Comprising eight companies — Heidelberg Instruments, Nanoscribe, GenISys, Notion Systems, 40-30, Amcoss, SPECSGROUP, and Nanosurf — LAB14 is focused on developing products and services that are fundamental to micro- and nanofabrication technologies, supporting industrial and research-driven applications with complex manufacturing and analysis requirements.

The companies of LAB14 operate under a shared organizational structure that enables closer coordination in technology development. This setup allows for faster research progress and more efficient manufacturing workflows.

“Joining the MIT.nano Consortium marks a significant milestone for LAB14 and our companies,” says Martin Wynaendts van Resandt, CEO of LAB14. “This participation allows our network to collaborate directly with world-leading researchers, accelerating innovation in micro- and nanotechnology."

As part of this engagement, LAB14 will provide two pieces of equipment to be installed at MIT.nano within the coming year. The VPG 300 DI maskless stepper, a high-performance, direct-write system from Heidelberg Instruments, will be positioned inside MIT.nano’s cleanroom. This tool will allow MIT.nano users to pattern structures smaller than 500 nanometers directly onto wafers with accuracy and uniformity comparable to typical high resolution i-line lithography. Equipped with advanced multi-layer alignment and mix‑and‑match functions, the VPG creates a seamless link between laser direct writing and e‑beam lithography.

The EnviroMETROS X-ray photoelectron spectroscopy (XPS/HAXPES) tool by SPECSGROUP will join the suite of Characterization.nano instruments. This unique system is specialized in nondestructive depth profile measurements using multiple X-ray energies to determine the thickness of thin-film samples and their chemical compositions with highest precision. It supports various analyses across a wide pressure range, allowing MIT.nano users to examine thin‑film materials under more realistic environmental conditions and to observe how they change during operation.

The MIT.nano Consortium is a platform for academia-industry collaboration, fostering research and innovation in nanoscale science and engineering. Consortium members gain unparalleled access to MIT.nano and its dynamic user community, providing opportunities to share expertise and guide advances in nanoscale technology.

MIT.nano continues to welcome new companies as sustaining members. For details, and to see a list of current members, visit the MIT.nano Consortium page.

Les Perelman, expert in writing assessment and champion of writing education, dies at 77

The longtime MIT faculty member and former dean established an influential writing program at the Institute and was known for his fierce criticism of automated essay grading.

Leslie “Les” Perelman, an influential figure in college writing assessment; a champion of writing instruction across all subject matters for over three decades at MIT; and a former MIT associate dean for undergraduate education, died on Nov. 12, 2025, at home in Lexington, Massachusetts. He was 77.

A Los Angeles native, Perelman attended the University of California at Berkeley, joining in its lively activist years, and in 1980 received his PhD in English from the University of Massachusetts at Amherst. After stints at the University of Southern California and Tulane University, he returned to Massachusetts — to MIT — in 1987, and stayed for the next 35 years.

Perelman became best known for his dogged critique of autograding systems and writing assessments that didn’t assess actual college writing. The Boston Globe dubbed him “The man who killed the SAT essay.” He told NPR that colleges “spend the first year deprogramming [students] from the five-paragraph essay.” 

His widow, MIT Professor Emerita Elizabeth Garrels, says that while attending a conference, Perelman — who was practically blind without his glasses — arranged to stand at one end of a room in order to “grade” essays held up for him on the other side. “He would call out the grade that each essay would likely receive on standardized scoring,” Garrels says. “And he was consistently right.” Perelman was doing what automatic scorers were: He was, he said in the NPR interview, “mirroring how automated or formulaic grading systems often reward form over substance.” 

Perelman also “ruffled a lot of feathers” in industry, says Garrels, with his 2020 paper documenting his BABEL (“Basic Automatic B.S. Essay Language”) Generator, which output nonsense that commercial autograders nevertheless gave top marks. He saved some of his most systematic criticism for autograders’ defenders in academia, at one point calling out peers at the University of Akron for the methodology in their widely-touted paper claiming autograders performed just as well as human graders. 

At least one service, though, E.T.S., partly welcomed Perelman’s critique by making its autograder available to him for testing. (Others, like Pearson and Vantage Learning, declined.) He discovered he could ace the tests, even when his essay included non-factual gibberish and typographical errors:

Teaching assistants are paid an excessive amount of money. The average teaching assistant makes six times as much money as college presidents. In addition, they often receive a plethora of extra benefits such as private jets, vacations in the south seas, a staring roles in motion pictures. Moreover, in the Dickens novel Great Expectation, Pip makes his fortune by being a teaching assistant. It doesn’t matter what the subject is, since there are three parts to everything you can think of.

MIT career

Within MIT, Perelman’s legacy was his push to embed writing instruction into the whole of MIT’s curriculum, not as standalone expository writing subjects, let alone as merely a writing exam that incoming students could use to pass out of writing subjects altogether. Supported by a $325,000 National Science Foundation grant, he convinced MIT to hire writing instructors who were also subject matter experts, often with STEM PhDs. They were tasked with collaborating with departments to plant writing instruction into both existing curricula and new subjects. That effort eventually became the Writing Across the Curriculum program (today named Writing, Rhetoric, and Professional Communication) with a staff of more than 30 instructors.

Building out the infrastructure wasn’t quick, however. Perelman’s successor, Suzanne Lane ’85, says it took him almost 15 years. It started with proving to others just how uneven writing instruction at MIT actually was. “A whole cohort of students who took a lot of writing classes or got communication instruction in various places would make great progress,” Lane says. “But it was definitely possible to get through all of MIT without doing much writing at all.” 

To bolster his case, Perelman turned to alumni surveys. “The surveys asked how well MIT prepared you for your career,” says Lane. “The technical skills scored really high, but — what is horribly termed, sometimes, as ‘soft skills’ — communication skills, collaboration, etc., these scored really high on importance to career, but really low on how well MIT had prepared them.”

In other words, MIT alumni knew their stuff but were bad at communicating it, at a cost to their careers.

This led Perelman and others to push for a new undergraduate communication requirement. That NSF grant supported a 1997 pilot, designing experiments for courses that would be communication-intensive. It was a huge success. Every department participated. It involved 24 subjects and roughly 300 students. MIT faculty, following “lively” discussion at an April 1999 faculty meeting, approved the proposal of the creation of a report on the communication requirement’s implementation, followed a year later by its formal passage, effective fall 2001.

From that initial pilot of 24, there are now nearly 300 subjects that count toward the requirement, from ​​class 1.013 (Senior Civil and Environmental Engineering Design) to 24.918 (Workshop in Linguistic Research).

Connections beyond MIT

Early in his career, Perelman worked with Vincent DiMarco, a literature scholar at the University of Massachusetts at Amherst, to publish “The Middle English Letter of Alexander to Aristotle” (Brill, 1978). With Wang Computers as publisher, he was a technical writer and project leader on the “DOS Release 3.30 User’s Reference Guide.” He edited a book and chapter on writing studies and assessment with New Jersey Institute of Technology professor Norbert Elliot. And in a project he was particularly proud of, he worked with the New South Wales Teachers Federation in 2018 to convince Australia to reject the adoption of an automated essay grading regime. 

“Les was brilliant, with a Talmudic way of asking questions and entering academic debates,” says Nancy Sommers, whose work on undergraduate writing assessment at Harvard University paralleled Perelman’s. “I loved the way his eyes sparkled when he was ready to rip an adversary or a colleague who wasn’t up to his quick mind and vast, encyclopedic knowledge.” 

Openness to rhetorical combat didn’t keep Perelman from being a wonderful friend, Sommers says, saying he once waited for her at the airline gate with a sandwich and a smile after a canceled flight. “That was Les, so gracious, generous, anticipating the needs of friends, always there to offer sustenance and friendship.”

Donations in Perelman’s name can be made to UNICEF’s work supporting children in Ukraine, the Lexington Refugee Assistance Program, Doctors Without Borders, and the Ash Grove Movie Finishing Fund.

Coping with catastrophe

Japan incorporates more disaster planning into its buildings and public spaces than any other nation. Miho Mazereeuw’s new book explains how they do it.

Each April in Japan, people participate in a tradition called “hanami,” or cherry-blossom viewing, where they picnic under the blooming trees. The tradition has a second purpose: The presence of people at these gatherings, often by water, helps solidify riverbanks and protect them from spring floods. The celebration has a dual purpose, by addressing, however incrementally, the threat of natural disaster.

The practice of creating things that also protect against disasters can be seen all over Japan, where many new or renovated school buildings have design features unfamiliar to students elsewhere. In Tokyo, one elementary school has a roof swimming pool that stores water and is used to help the building’s toilets flush, plus an additional rainwater catchment tank and exterior stairs leading to a large balcony that wraps around one side of the building.

Why? Well, Japan is prone to natural disasters, such as tsunamis, earthquakes, and flooding. The country’s schools often double as evacuation sites for local residents, and design practices increasingly reflect this. In normal times, the roof pool is where students learn to swim and helps keep the school cool, and the large balcony is used by spectators watching the adjacent school athletics field. In emergencies, water storage is crucial and exterior stairs help people ascend quickly to the gymnasium, built on the second floor — to keep evacuees safer during flooding.

Meanwhile, in one Tokyo district, rooftop solar power is now common. Some schools feature skylights and courtyards to bring in natural light. Again, these architectural features serve dual purposes. Solar power, for one, lowers annual operating costs, and it provides electricity even in case of grid troubles.

These are examples of what MIT scholar Miho Mazereeuw has termed “anticipatory design,” in which structures and spaces are built with dual uses, for daily living and for when crisis strikes.

“The idea is to have these proactive measures in place rather than being reactionary and jumping into action only after something has happened,” says Mazereeuw, an associate professor in MIT’s Department of Architecture and a leading expert on resilient design.

Now Mazereeuw has a new book on the subject, “Design Before Disaster: Japan’s Culture of Preparedness,” published by the University of Virginia Press. Based on many years of research, with extensive illustrations, Mazereeuw examines scores of successful design examples from Japan, both in terms of architectural features and the civic process that created them.

“I’m hoping there can be a culture shift,” Mazereeuw says. “Wherever you can invent design outcomes to help society be more resilient beforehand, it is not at exorbitant cost. You can design for exceptional everyday spaces but embed other infrastructure and flexibility in there, so when there is a flood event or earthquake, those buildings have more capability.”

Bosai and barbecue

Mazereeuw, who is also the head of MIT’s Urban Risk Lab, has been studying disaster preparedness for over 30 years. As part of the Climate Project at MIT, she is also one of the mission directors and has worked with communities around the world on resiliency planning.

Japan has a particularly well-established culture of preparedness, often referred to through the Japanese word “bosai.” Mazereeuw has been studying the country’s practices carefully since the 1990s. In researching the book, she has visited hundreds of sites in the country and talked to many officials, designers, and citizens along the way.

Indeed, Mazereeuw emphasizes, “A major theme in the book is connecting the top-down and bottom-up.” Some good design ideas come from planners and architects. Other have come from community groups and local residents. All these sources are important.

“The Japanese government does invest a lot in disaster research and recovery,” Mazereeuw says. “But I would hate for people in other countries to think this isn’t possible elsewhere. It’s the opposite. There are a lot of examples in here that don’t cost extra, because of careful design through community participation.”

As one example, Mazereeuw devotes a chapter of the book to public parks, which are often primary evacuation spaces for residents in case of emergency. Some have outdoor cooking facilities, which in normal times are used for, say, a weekend barbecue or local community events but are also there in case of emergency. Some parks also have water storage, or restroom facilities designed to expand if needed, and many serve as flood reservoirs, protecting the surrounding neighborhood.

“The barbecue facilities are a great example of dual use, connecting the everyday with disaster preparedness,” Mazereeuw says. “You can bring food into this beautiful park, so you’re used to using this space for cooking already. The idea is that your cognitive map of where you should go is connected to fun things you have done in the past.”

Some of the parks Mazereeuw surveys in the book are tiny pocket parks, which are also filled with useful resilience tools.

“Anticipatory design does not have to be monumental,” Mazereeuw writes in the book.

Negotiating through design

To be sure, some disaster mitigation measures are difficult to enact. In the Naiwan district of Kesennuma, as Mazereeuw outlines in the book, much of the local port area was destroyed in the 2011 tsunami, and the government wanted to build a seawall as part of the reconstruction plan. Some local residents and fishermen were unenthusiastic; a seawall could limit ocean access. Finally, after extended negotiations, designers created a seawall integrated into a new commercial district with cafes and stores, as well as new areas of public water access.

“This project used the power of design to negotiate between prefectural and local regulations, structural integrity and aesthetics, ocean access and safety,” Mazereeuw says.

Ultimately, working to build a coalition in support of resilience measures can help create more interesting and useful designs.

Other scholars have praised “Design Before Disaster.” Daniel P. Aldrich, a professor at Northeastern University, has called the book a “well-researched, clearly written investigation” into Japanese disaster-management practices, adding that any officials or citizens around the world “who seek to keep residents and communities safe from shocks of all kinds will learn something important from this book. It sets a high bar for future scholarship in the field.”

For her part, Mazereeuw emphasizes, “We can learn from the Japanese example, but it’s not a copy-paste thing. The book is so people can understand the essence of it and then create their own disaster preparedness culture and approach. This should be an all-hands process. Emergency management is not about relying on managers. It’s figuring out how we all play a part.”

Featured video: Coding for underwater robotics

Lincoln Laboratory intern Ivy Mahncke developed and tested algorithms to help human divers and robots navigate underwater.

During a summer internship at MIT Lincoln Laboratory, Ivy Mahncke, an undergraduate student of robotics engineering at Olin College of Engineering, took a hands-on approach to testing algorithms for underwater navigation. She first discovered her love for working with underwater robotics as an intern at the Woods Hole Oceanographic Institution in 2024. Drawn by the chance to tackle new problems and cutting-edge algorithm development, Mahncke began an internship with Lincoln Laboratory's Advanced Undersea Systems and Technology Group in 2025. 

Mahncke spent the summer developing and troubleshooting an algorithm that would help a human diver and robotic vehicle collaboratively navigate underwater. The lack of traditional localization aids — such as the Global Positioning System, or GPS — in an underwater environment posed challenges for navigation that Mahncke and her mentors sought to overcome. Her work in the laboratory culminated in field tests of the algorithm on an operational underwater vehicle. Accompanying group staff to field test sites in the Atlantic Ocean, Charles River, and Lake Superior, Mahncke had the opportunity see her software in action in the real world.

"One of the lead engineers on the project had split off to go do other work. And she said, 'Here's my laptop. Here are the things that you need to do. I trust you to go do them.' And so I got to be out on the water as not just an extra pair of hands, but as one of the lead field testers," Mahncke says. "I really felt that my supervisors saw me as the future generation of engineers, either at Lincoln Lab or just in the broader industry."

Says Madeline Miller, Mahncke's internship supervisor: "Ivy's internship coincided with a rigorous series of field tests at the end of an ambitious program. We figuratively threw her right in the water, and she not only floated, but played an integral part in our program's ability to hit several reach goals."

Lincoln Laboratory's summer research program runs from mid-May to August. Applications are now open. 

Video by Tim Briggs/MIT Lincoln Laboratory | 2 minutes, 59 seconds

Designing a more resilient future for plants, from the cell up

Foray Bioscience, founded by Ashley Beckwith SM ’18, PhD ’22, is engineering single plant cells to create new materials and meet growing demand.

In a narrow strip of land along the Andes mountain range in central Chile, an Indigenous community has long celebrated the bark of a rare tree for its medicinal properties. Modern science only recently caught up to the tradition, finding the so-called soapbark tree contains potent compounds for boosting the human immune system.

The molecules have since been harnessed to make the world’s first malaria vaccine and to boost the effectiveness of vaccines for everything from shingles to Covid-19 and cancer. Unfortunately, unsustainable harvesting has threatened the existence of the tree species, leading the Chilean government to severely restrict lumbering.

The soapbark tree’s story is not unique. Plants are the foundation of industries such as pharmaceuticals, beauty, agriculture, and forestry, yet around 45 percent of plant species are in danger of going extinct. At the same time, human demand for plant products continues to rise. Ashley Beckwith SM ’18, PhD ’22 believes meeting that demand requires rethinking how plants are grown. Her company, Foray Bioscience, aims to make plant production faster, more adaptable, and less damaging to fragile natural supply chains.

The company is working to make it possible to grow any plant or plant product from single cells using biomanufacturing powered by artificial intelligence. Foray has already developed molecules, materials, and fabricated seeds with various partners, including academic researchers, nurseries, conservationists, and companies.

In one new partnership, Foray is working with the nursery West Coast Chestnut to deploy a more disease-resistant version of the chestnut trees that once filled forests across the eastern U.S. but have since been wiped out. The project is just one example of how AI and plant science can be leveraged to protect the plant populations that bring so much value to humans and the planet.

“Plant systems underpin every aspect of our daily lives, from the air we breathe to the food we eat, the clothes we wear, the homes we live in, and more,” Beckwith says. “But these plant systems are fragile and in decline. We need new strategies to ensure lasting access to the plant products and ecosystems we depend on.”

From human cells to plants

Beckwith focused on biology and materials manufacturing as a master’s student in MIT’s Department of Mechanical Engineering. Her research involved building platforms to enable precision treatments for human diseases. After graduating, she worked on a regenerative, self-sufficient farm that mimicked natural ecosystems, and began thinking about applying her work to address the fragility of plant systems.

Beckwith returned to MIT for her PhD to explore the idea of regenerative plant systems, studying in the lab of Research Scientist Luis Fernando Velásquez-García.

“To address organ shortages for transplants, scientists aspire to grow kidneys that don’t have to be harvested from a human using tissue engineering,” Beckwith says. “What if we could do something similar for our plant systems?”

Beckwith went on to publish papers showing she could grow wood-like plant material in a lab. By adjusting certain chemicals, the researchers could precisely control properties like stiffness and density.

“I was thinking about how we build products, like wood, from the cell up instead of extracting from the top down,” Beckwith recalls. “It led to some foundational demonstrations that underpin the work we do at Foray today, but it also opened up questions: Where are these new approaches most urgently needed? What would it take to apply these tools where they’re needed, fast?”

Beckwith began exploring the idea of starting a company in 2021, participating in accelerator programs run by the E14 Fund and The Engine — both MIT-affiliated initiatives designed to support breakthrough science ventures. She officially founded Foray in February of 2022 after completing her PhD.

“Our early research showed that we could grow wood-like material directly from plant cells,” she says. “We are now able to grow not just wood without the tree, but also produce harvest-free molecules, materials, and even seeds by steering single cells to develop precisely into the products we need without ever having to grow the whole plant.”

Beckwith describes her lab-grown wood innovation as analogous to Uber if there were no internet — a powerful idea without the digital backbone to scale. To create the data foundation and ecosystem to scale plant innovation, Foray is now building the Pando AI platform to enable rapid discovery and deployment of these novel plant solutions.

“Pando functions like a Google Maps for plant growth,” Beckwith says. “It helps scientists navigate a really complex field of variables and arrive at a research destination efficiently — because to steer a cell to produce a particular product, there might be 50 different variables to tweak. It would take a lifetime to explore each of those, and that’s one reason why plant research is so slow today.”

The “operating system for plant science”

Foray’s team includes experts in plant biology, artificial intelligence, machine learning, computational biology, and process engineering.

“This is a very intersectional problem,” Beckwith says. “One of the most exciting things for me is building this highly capable team that is able to deliver solutions that could never be created in a silo.”

After a year of pilot collaborations with select researchers, Foray is preparing for a broader public launch of its Pando platform early this year.

Over the next several years, Beckwith hopes Foray will serve as an innovation engine for researchers and companies working across agriculture, materials, pharmaceuticals, and conservation. Foray already uses Pando internally to create plant solutions that overcome limitations in natural production.       

“Fabricated seeds are one capability that we’re really excited about,” Beckwith says. “Being able to grow seeds from cells lets you create really timely and scalable seed supplies to address gaps in restoration, or shorten the path to market for new, resilient crop varieties. There’s a lot to be gained by making our plant systems more adaptive.”

“We want to shorten plant development timelines, so solutions can be built in months, not decades,” Beckwith says. “We’re excited to be building tools that represent a step change in the way plant production can be done.”

As Foray’s products scale and more researchers use its platform, the company is hoping to help the plant science industry respond to some of our planet’s most pressing challenges.

“Right now, we’re focused on plants in labs,” Beckwith says. “In five years, we aim to be the operating system for all of plant science, making it possible to build anything from a single plant cell.”

Tackling industry’s burdensome bubble problem

MIT researchers uncovered the physics behind bubble-removing membranes that could improve bioreactors, chemical production, and more.

In industrial plants around the world, tiny bubbles cause big problems. Bubbles clog filters, disrupt chemical reactions, reduce throughput during biomanufacturing, and can even cause overheating in electronics and nuclear power plants.

MIT Professor Kripa Varanasi has long studied methods to reduce bubble disruption. In a new study, Varanasi, along with PhD candidate Bert Vandereydt and former postdoc Saurabh Nath, have uncovered the physics behind a promising type of debubbling membrane material that is “aerophilic” — Greek for “air-loving.” The material can be used in systems of all types, allowing anyone to optimize their machine’s performance by breaking free from bubble-borne disruptions.

“We have figured out the structure of these bubble-attracting membrane materials to allow gas to evacuate in the fastest possible manner,” says Varanasi, the senior author of the study. “Think of trying to push honey through a coffee strainer: It’s not going to go through easily, whereas water will move through, and gas will move through even more easily. But even gas will reach a throughput limit, which depends on the properties of the gas and the liquid involved. By uncovering those limits, our research allows engineers to build better membranes for their systems.”

In the paper, which appears in the journal PNAS this week, the researchers distill their findings into a graph that allows anyone to plot a few characteristics of their system — like the viscosity of their gas and the surrounding liquid — and find the best membrane to make bubble removal near-instantaneous. Using their approach, the research team demonstrated a 1,000-fold acceleration in bubble removal in a bioreactor that’s used in the pharmaceutical industry, food and beverage manufacturing, cosmetics, chemical production, and more.

The researchers say the membranes, which repel water, could be used to improve the throughput of a wide range of advanced systems whose operation has been plagued to date by bubbles.

Better bubble breakers

Companies today try everything to burst bubbles. They deploy foam breakers that physically shear them, chemicals that act as antifoaming agents, even ultrasound. Such approaches have drawbacks in tightly controlled environments like bioreactors, where chemical defoamers can be toxic to cells, while mechanical agitation can damage delicate biological materials. Similar limitations apply to other industries where contamination or physical disturbance is unacceptable. As a result, many applications that cannot tolerate chemical defoamers or mechanical intervention remain fundamentally bottlenecked by foam formation.

“Biomanufacturing has really taken off in the last 10 years,” Vandereydt says. “We’re making a lot more out of biologic systems like cells and bacteria, and our reactors have increased in throughput from 5 million cells per millimeter of solution to 100 million cells per millimeter. However, the bubble evacuation and defoaming haven’t kept up — it’s becoming a significant rate-limiting step.”

To better understand the interaction between aerophilic membranes and bubbles, the MIT researchers used MIT.nano facilities to create a series of tiny porous silicon membranes with holes ranging in size from 10 microns to 200 microns. They coated the membranes with hydrophobic silica nanoparticles.

Placing them on the surface of different liquids, the researchers released single bubbles with varying viscosity and recorded the interaction using high-speed imaging as each collided with the membranes.

“We started by trying to take a very complicated system, like foam being generated in a bioreactor, and study it in the simplest form to understand what’s happening,” Vandereydt says.

At first, the bigger the holes, the faster the bubbles disappeared. The researchers also changed the bubble gas from air to hydrogen, which has half the viscosity, and found the speed of bubble destruction doubled.

But after about a 1,000-fold acceleration in bubble destruction, the researchers hit a wall no matter how big the membrane holes were. They had run up against a different physical limit to investigate.

The researchers then tried changing the viscosity of their liquid, from water to something closer to honey. They found viscosity only plays a role in the speed of bubble destruction when the liquid is 200 times the viscosity of liquid. Further experiments revealed the biggest factor for slowing bubble evacuation was inertial resistance in the liquid.

“Through experimentation, we showed there are three different limits [to the speed of bubble destruction],” Vandereydt says. “There is the viscous limit of the gas in a low-viscosity, low-permeability setup. Then there’s the viscous resistance of the liquid in the high-permeability, high-viscosity regime. Then we have the inertial limit of the liquid.”

The team used a bioreactor to experimentally validate their findings and charted them in a map that engineers can use to enter the characteristics of their system and find both the best membrane for their situation and the biggest factor slowing bubble evacuation.

The science of bubbles

The research should be useful for anyone trying to accelerate the destruction of bubbles in their industrial device, but it also improves our understanding of the physics underpinning bubble dynamics.

“We have identified three different throughput limits, and the physics behind those limits, and we have reduced it to very simple laws,” Nath explains. “How fast you can go is first dictated between surface tension and inertia. But you may also hit a different limit, where the pores are extremely small, so the gas finds it difficult to move through them. In that case, the viscosity of the gas is meaningful. But you may also have a bubble which was originally in something like honey, which means it’s not enough the gas is moving, the liquid also must refill the space behind it. No matter what your conditions are, you will be switching between these three limits.”

Varanasi says health care companies, chemical manufacturers, and even breweries have expressed interest in the work. His team plans to commercially develop the membranes for industrial use.

“These physical insights allowed us to design membranes that, quite surprisingly, evacuate bubbles even faster than a free liquid-gas interface,” says Varanasi.

The researchers’ design map could also be used to model natural systems and even liquid-liquid systems, which could be used to create membranes that remove oil spills from water or help efficiently extract hydrogen from water-splitting electrodes. Ultimately the biggest beneficiaries of the findings will be companies grappling with bubbles.

“Though small, bubbles quietly dictate the performance limits of many advanced technologies,” says Varanasi. “Our results provide a way to eliminate that bottleneck and unlock entirely new levels of performance across industries. These membranes can be readily retrofitted into existing systems, and our framework allows them to be rapidly designed and optimized for specific applications. We’re excited to work with industry to translate these insights into impact.”

The work was supported, in part, by MIT Lincoln Laboratory and used MIT.nano facilities.

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller steps. These powerful models are particularly good at challenging tasks like advanced programming and multistep planning.

But developing reasoning models demands an enormous amount of computation and energy due to inefficiencies in the training process. While a few of the high-power processors continuously work through complicated queries, others in the group sit idle.

Researchers from MIT and elsewhere found a way to use this computational downtime to efficiently accelerate reasoning-model training.

Their new method automatically trains a smaller, faster model to predict the outputs of the larger reasoning LLM, which the larger model verifies. This reduces the amount of work the reasoning model must do, accelerating the training process.

The key to this system is its ability to train and deploy the smaller model adaptively, so it kicks in only when some processors are idle. By leveraging computational resources that would otherwise have been wasted, it accelerates training without incurring additional overhead.

When tested on multiple reasoning LLMs, the method doubled the training speed while preserving accuracy. This could reduce the cost and increase the energy efficiency of developing advanced LLMs for applications such as forecasting financial trends or detecting risks in power grids.

“People want models that can handle more complex tasks. But if that is the goal of model development, then we need to prioritize efficiency. We found a lossless solution to this problem and then developed a full-stack system that can deliver quite dramatic speedups in practice,” says Qinghao Hu, an MIT postdoc and co-lead author of a paper on this technique.

He is joined on the paper by co-lead author Shang Yang, an electrical engineering and computer science (EECS) graduate student; Junxian Guo, an EECS graduate student; senior author Song Han, an associate professor in EECS, member of the Research Laboratory of Electronics and a distinguished scientist of NVIDIA; as well as others at NVIDIA, ETH Zurich, the MIT-IBM Watson AI Lab, and the University of Massachusetts at Amherst. The research will be presented at the ACM International Conference on Architectural Support for Programming Languages and Operating Systems.

Training bottleneck

Developers want reasoning LLMs to identify and correct mistakes in their critical thinking process. This capability allows them to ace complicated queries that would trip up a standard LLM.

To teach them this skill, developers train reasoning LLMs using a technique called reinforcement learning (RL). The model generates multiple potential answers to a query, receives a reward for the best candidate, and is updated based on the top answer. These steps repeat thousands of times as the model learns.

But the researchers found that the process of generating multiple answers, called rollout, can consume as much as 85 percent of the execution time needed for RL training.

“Updating the model — which is the actual ‘training’ part — consumes very little time by comparison,” Hu says.

This bottleneck occurs in standard RL algorithms because all processors in the training group must finish their responses before they can move on to the next step. Because some processors might be working on very long responses, others that generated shorter responses wait for them to finish.

“Our goal was to turn this idle time into speedup without any wasted costs,” Hu adds.

They sought to use an existing technique, called speculative decoding, to speed things up. Speculative decoding involves training a smaller model called a drafter to rapidly guess the future outputs of the larger model.

The larger model verifies the drafter’s guesses, and the responses it accepts are used for training.

Because the larger model can verify all the drafter’s guesses at once, rather than generating each output sequentially, it accelerates the process.

An adaptive solution

But in speculative decoding, the drafter model is typically trained only once and remains static. This makes the technique infeasible for reinforcement learning, since the reasoning model is updated thousands of times during training.

A static drafter would quickly become stale and useless after a few steps.

To overcome this problem, the researchers created a flexible system known as “Taming the Long Tail,” or TLT.

The first part of TLT is an adaptive drafter trainer, which uses free time on idle processors to train the drafter model on the fly, keeping it well-aligned with the target model without using extra computational resources.

The second component, an adaptive rollout engine, manages speculative decoding to automatically select the optimal strategy for each new batch of inputs. This mechanism changes the speculative decoding configuration based on the training workload features, such as the number of inputs processed by the draft model and the number of inputs accepted by the target model during verification.

In addition, the researchers designed the draft model to be lightweight so it can be trained quickly. TLT reuses some components of the reasoning model training process to train the drafter, leading to extra gains in acceleration.

“As soon as some processors finish their short queries and become idle, we immediately switch them to do draft model training using the same data they are using for the rollout process. The key mechanism is our adaptive speculative decoding — these gains wouldn’t be possible without it,” Hu says.

They tested TLT across multiple reasoning LLMs that were trained using real-world datasets. The system accelerated training between 70 and 210 percent while preserving the accuracy of each model.

As an added bonus, the small drafter model could readily be utilized for efficient deployment as a free byproduct.

In the future, the researchers want to integrate TLT into more types of training and inference frameworks and find new reinforcement learning applications that could be accelerated using this approach.

“As reasoning continues to become the major workload driving the demand for inference, Qinghao’s TLT is great work to cope with the computation bottleneck of training these reasoning models. I think this method will be very helpful in the context of efficient AI computing,” Han says.

This work is funded by the MIT-IBM Watson AI Lab, the MIT AI Hardware Program, the MIT Amazon Science Hub, Hyundai Motor Company, and the National Science Foundation.

AI to help researchers see the bigger picture in cell biology

By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.

Studying gene expression in a cancer patient’s cells can help clinical biologists understand the cancer’s origin and predict the success of different treatments. But cells are complex and contain many layers, so how the biologist conducts measurements affects which data they can obtain. For instance, measuring proteins in a cell could yield different information about the effects of cancer than measuring gene expression or cell morphology.

Where in the cell the information comes from matters. But to capture complete information about the state of the cell, scientists often must conduct many measurements using different techniques and analyze them one at a time. Machine-learning methods can speed up the process, but existing methods lump all the information from each measurement modality together, making it difficult to figure out which data came from which part of the cell.

To overcome this problem, researchers at the Broad Institute of MIT and Harvard and ETH Zurich/Paul Scherrer Institute (PSI) developed an artificial intelligence-driven framework that learns which information about a cell’s state is shared across different measurement modalities and which information is unique to a particular measurement type.

By pinpointing which information came from which cell parts, the approach provides a more holistic view of the cell’s state, making it easier for a biologist to see the complete picture of cellular interactions. This could help scientists understand disease mechanisms and track the progression of cancer, neurodegenerative disorders such as Alzheimer’s, and metabolic diseases like diabetes.

“When we study cells, one measurement is often not sufficient, so scientists develop new technologies to measure different aspects of cells. While we have many ways of looking at a cell, at the end of the day we only have one underlying cell state. By putting the information from all these measurement modalities together in a smarter way, we could have a fuller picture of the state of the cell,” says lead author Xinyi Zhang SM ’22, PhD ’25, a former graduate student in the MIT Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, who is now a group leader at AITHYRA in Vienna, Austria.

Zhang is joined on a paper about the work by G.V. Shivashankar, a professor in the Department of Health Sciences and Technology at ETH Zurich and head of the Laboratory of Multiscale Bioimaging at PSI; and senior author Caroline Uhler, a professor in EECS and the Institute for Data, Systems, and Society (IDSS) at MIT, member of MIT’s Laboratory for Information and Decision Systems (LIDS), and director of the Eric and Wendy Schmidt Center at the Broad Institute. The research appears today in Nature Computational Science.

Manipulating multiple measurements

There are many tools scientists can use to capture information about a cell’s state. For instance, they can measure RNA to see if the cell is growing, or they can measure chromatin morphology to see if the cell is dealing with external physical or chemical signals.

“When scientists perform multimodal analysis, they gather information using multiple measurement modalities and integrate it to better understand the underlying state of the cell. Some information is captured by one modality only, while other information is shared across modalities. To fully understand what is happening inside the cell, it is important to know where the information came from,” says Shivashankar.

Often, for scientists, the only way to sort this out is to conduct multiple individual experiments and compare the results. This slow and cumbersome process limits the amount of information they can gather.

In the new work, the researchers built a machine-learning framework that specifically understands which information overlaps between different modalities, and which information is unique to a particular modality but not captured by others.

“As a user, you can simply input your cell data and it automatically tells you which data are shared and which data are modality-specific,” Zhang says.

To build this framework, the researchers rethought the typical way machine-learning models are designed to capture and interpret multimodal cellular measurements.

Usually these methods, known as autoencoders, have one model for each measurement modality, and each model encodes a separate representation for the data captured by that modality. The representation is a compressed version of the input data that discards any irrelevant details.

The MIT method has a shared representation space where data that overlap between multiple modalities are encoded, as well as separate spaces where unique data from each modality are encoded.

In essence, one can think of it like a Venn diagram of cellular data.

The researchers also used a special, two-step training procedure that helps their model handle the complexity involved in deciding which data are shared across multiple data modalities. After training, the model can identify which data are shared and which are unique when fed cell data it has never seen before.

Distinguishing data

In tests on synthetic datasets, the framework correctly captured known shared and modality-specific information. When they applied their method to real-world single-cell datasets, it comprehensively and automatically distinguished between gene activity captured jointly by two measurement modalities, such as transcriptomics and chromatin accessibility, while also correctly identifying which information came from only one of those modalities.

In addition, the researchers used their method to identify which measurement modality captured a certain protein marker that indicates DNA damage in cancer patients. Knowing where this information came from would help a clinical scientist determine which technique they should use to measure that marker.

“There are too many modalities in a cell and we can’t possibly measure them all, so we need a prediction tool. But then the question is: Which modalities should we measure and which modalities should we predict? Our method can answer that question,” Uhler says.

In the future, the researchers want to enable the model to provide more interpretable information about the state of the cell. They also want to conduct additional experiments to ensure it correctly disentangles cellular information and apply the model to a wider range of clinical questions.

“It is not sufficient to just integrate the information from all these modalities,” Uhler says. “We can learn a lot about the state of a cell if we carefully compare the different modalities to understand how different components of cells regulate each other.”

This research is funded, in part, by the Eric and Wendy Schmidt Center at the Broad Institute, the Swiss National Science Foundation, the U.S. National Institutes of Health, the U.S. Office of Naval Research, AstraZeneca, the MIT-IBM Watson AI Lab, the MIT J-Clinic for Machine Learning and Health, and a Simons Investigator Award.

MIT’s delta v accelerator receives $6M gift to supercharge startups being built by student founders

Ed Hallen MBA ’12 and Andrew Bialecki, co-founders of tech firm Klaviyo, will help to meet increased student demand for building impactful ventures.

With the impact artificial intelligence is having on how companies operate, the environment for how MIT students are learning entrepreneurship and choosing to create new ventures is seeing rapid changes as well. To address how these student startups are being built, the Martin Trust Center for MIT Entrepreneurship undertook a months-long series of discussions with key stakeholders to help shape a new direction for delta v, MIT’s capstone entrepreneurship accelerator for student founders.

Two of Boston’s most successful tech entrepreneurs have stepped forward to fund this growth of new MIT ventures through a combined $6 million gift that supports the delta v accelerator run out of the Trust Center. Ed Hallen MBA ’12 and Andrew Bialecki, co-founders of Boston-based customer relationship management firm Klaviyo, are providing the donation to support the next wave of innovation-driven entrepreneurship taking place at MIT.

“In the early days of Klaviyo, we learned almost everything by building, testing assumptions, making mistakes, and figuring things out as we went,” Hallen says. “MIT delta v creates that same learning-by-doing environment for students, while surrounding them with mentorship and resources that help founders build with clarity and momentum. We’ve seen the difference delta v can make for founders, and we’re excited to help the Trust Center extend that opportunity to the next generation of students.”

“We’ve always believed the world needs more entrepreneurs, and that Boston should be one of the places leading the way,” adds Bialecki. “Boston is a hub of innovation with ambitious students and a strong community of builders. MIT delta v plays a critical role in developing founders early, not just helping them start companies but helping them build companies that last. Supporting that mission is something Ed and I care deeply about.”

The Martin Trust Center plans to “accelerate the accelerator” with the funding. Recognizing the opportunity that exists as AI impacts how students are able to build companies, along with the increased interest being shown by students to learn about entrepreneurship during their time on campus, is a major driver for these changes. One of the main impacts will be the ability of delta v participants to earn up to $75,000 in equity-free funding during the program, an increase from $20,000 in years past. 

Also, delta v will be introducing a partner model composed of leading founders from companies such as HubSpot, Okta, and Kayak, C-suite operators, subject matter experts, and early-stage investors who will all be providing significant guidance and mentorship to the student ventures.

“Core to MIT’s mission is developing the innovative technologies and solutions that can help solve tough problems at global scale,” says MIT Provost Anantha Chandrakasan. “The AI revolution is creating exciting new opportunities for MIT students to build the next wave of impactful companies, and the delta v accelerator is a perfect vehicle to help them make that happen.”

In recent years MIT-founded startups such as Cursor and Delve who use AI as a core part of their business have seen explosive growth in both customers and revenue as well as valuation. In addition, delta v alumni entrepreneurs and their companies such as Klarity and Reducto are providing software-as-a-service (SaaS) platforms using AI tools while Vertical Semiconductor is growing thanks to providing the energy solutions that data centers need to power today’s computing demands. These are just some of the businesses MIT students are looking to as models they can follow to build and launch successfully, whether they are working on solutions in health care, climate, finance, the future of work, or another global challenge.

“MIT Sloan is the place for entrepreneurship education, part of a unique ecosystem of collaboration across MIT to solve problems," says Richard M. Locke, the John C Head III Dean at the MIT Sloan School of Management. “The delta v program is a great example of how MIT students dedicate their energy to starting a venture, connect with mentors, and incorporate proven frameworks for disciplined entrepreneurship. This gift from Ed Hallen and Andrew Bialecki will provide additional funding for this important program, and I’m so grateful for their support of entrepreneurship education at MIT.” 

“I remember when Ed and Andrew were giving birth to Klaviyo at the Trust Center,” says Bill Aulet, the Ethernet Inventors Professor of the Practice and managing director of the Trust Center. “Through their ingenuity and drive, they have created an iconic tech company here in Boston with the support of our ecosystem. Through their willingness to give back, many more students will now be able to follow their path and become entrepreneurs who can create extraordinary positive impact in the world.”

Applications for the next delta v cohort will open on March 1 and close on April 1. Teams will be announced in May for the summer 2026 accelerator.

“MIT delta v is about creating belief in our most exceptional entrepreneurial talent — and turning that belief into consequential impact for the world. By supporting early-stage founders who take bold ideas from improbable to possible, we help them build companies that matter,” says Ana Bakshi, the Trust Center’s executive director. “Our students are the next generation of job creators, economic drivers, and thought leaders. To realize this potential, it is critical that we continue to invest in and scale startup programs and spaces so they can build at unprecedented levels. Ed and Andrew’s generosity gives us a powerful opportunity to change velocity—and make that future possible.”

Founded in 1991, the award-winning Martin Trust Center for MIT Entrepreneurship is today focused on teaching entrepreneurship as a craft. It combines evidence-based entrepreneurship frameworks, used in over a thousand other organizations, with experiential learning, experiences, and community building inside and outside the classroom to create the next generation of innovation-driven entrepreneurs. Alumni who have gone through Trust Center programs have started companies including Cursor, Delve, Okta, HubSpot, PillPack, Honey, WHOOP, Reducto, Klarity, and Biobot Analytics, and thousands more in industries as diverse as biotech, climate and energy, AI, health care, fintech, business and consumer software, and more. 

In the first 10 years of delta v, the program's alumni have helped create entrepreneurs who have gone on to experience extraordinary success. The five-year survival rate of their companies has been 69%, and they have raised well over $3 billion in funding while addressing the world’s greatest challenges — evidenced by the fact that 89% are directly aligned with the UN Sustainable Development goals.

More trees where they matter, please

An international study reveals disparities in urban shade levels, exacerbating the “heat island” effect in big cities.

One of the best forms of heat relief is pretty simple: trees. In cities, as studies have documented, more tree cover lowers surface temperatures and heat-related health risks.

However, as a new study led by MIT researchers shows, the amount of tree cover varies widely within cities, and is generally connected to wealth levels. After examining a cross-section of cities on four continents at different latitudes, the research finds a consistent link between wealth and neighborhood tree abundance within a city, with better-off residents usually enjoying much more shade on nearby sidewalks.

“Shade is the easiest way to counter warm weather,” says Fabio Duarte, an MIT urban studies scholar and co-author of a new paper detailing the study’s results. “Strictly by looking at which areas are shaded, we can tell where rich people and poor people live.”

That disparity is evident within a range of cities, and is present whether a city contains a large amount of tree cover overall or just a little. Either way, there are more trees in wealthier spots.

“When we compare the most well-shaded city in our study, Stockholm, with the worst-shaded, Belem in northern Brazil, we still see marked inequality,” says Duarte, the associate director of MIT’s Senseable City Lab in the Department of Urban Studies and Planning (DUSP). “Even though the most-shaded parts of Belem are less shaded than the least-shaded parts of Stockholm, shade inequality in Stockholm is greater. Rich people in Stockholm have much better shade provison as pedestrians than we see in poor areas of Stockholm.”

The paper, “Global patterns of pedestrian shade inequality,” is published today in Nature Communications. The authors are Xinyue Gu of Hong Kong Polytechnic University; Lukas Beuster, a research fellow at the Amsterdam Institute for Advanced Metropolitan Solutions and MIT’s Senseable City Lab; Xintao Liu, an associate professor at Hong Kong Polytechnic University; Eveline van Leeuwen, scientific director at the Amsterdam Institute for Advanced Metropolitan Solutions; Titus Venverloo, who leads the MIT Senseable City Amsterdam lab; and Duarte, who is also a lecturer in DUSP.

From Stockholm to Sydney

To conduct the study, the researchers used satellite data from multiple sources, along with urban mapping programs and granular economic data about the cities they examined. There are nine cities in the study: Amsterdam, Barcelona, Belem, Boston, Hong Kong, Milan, Rio de Janeiro, Stockholm, and Sydney. Those places are intended to create a cross-section of cities with different characteristics, including latitude, wealth levels, urban form, and more.

The scholars looked at the amount of shade available on city sidewalks on summer solistice day, as well as the hottest recorded day each year from 1991 to 2020. They then created a scale, ranging from 0 to 1, to rate the amount of shade available on sidewalks, both citywide and within neighborhoods.

“We focused on sidewalks because they are a major counduit of urban activity, even on hot summer days,” Gu says. “Adding tree cover for sidewalks is one crucial way cities can pursue heat-reduction measures.”

Duarte adds: “When it comes to those who are not protected by air conditioning, they are also using the city, walking, taking buses, and anybody who takes a bus is walking or biking to or from bus stops. They are using sidewalks as the main infrastructure.”

The cities in the study offer very different levels of tree coverage. On the 0-to-1 scale the researchers developed, much of Stockholm falls in the 0.6-0.9 range, with some neighborhoods being over 0.9. By contrast, large swaths of Rio de Janeiro are under the 0.1 mark. Much of Boston ranges from 0.15 to 0.4, with a few neighborhoods reaching 0.45 on the scale.

The overall pattern of disparities, however, is very consistent, and includes the more affluent cities. The bottom 20 percent of neighborhoods in Stockholm, in terms of shade coverage, are rated at 0.58 on the scale, while the top 20 percent of Belem neighborhoods rate at 0.37; Stockholm has a greater disparity between most-covered and least-covered. To be sure, there is variety within many cities: Milan and Barcelona have some lower-income neighborhoods with abundant shade, for instance. But the aggregate trend is clear. Amsterdam, another well-off place on average, has a distinct pattern of less shade in lower-income areas.

“In rich cities like Amsterdam, even though it’s relatively well-shaded, the disparity is still very high,” Beuster says. “For us the most surprising point was not that in poor cities and more unequal societies the disparity would be notable — that was expected. What was unexpected was how the disparity still happens and is sometimes more pronounced in rich countries.”

“Follow transit”

If the tree-shade disparity issue is quite persistent, then it raises the matter of what to do about it. The researchers have a basic answer: Add trees in areas with public transit, which generate a lot of pedestrian mileage.

“In each city, from Sydney to Rio to Amsterdam, there are people who, regardless of the weather, need to walk,” Duarte says. “And it’s those people who also take public transportation. Therefore, link a tree-planting scheme to a public transportation network. And secondly, they are also the medium-and low-income part of the population. So the action deriving from this result is quite clear: If you need to increase your tree coverage and don’t know where, follow transit. If you follow transit, you will have the right shading.”

Indeed, one takeaway from the study is to think of trees not just as a nice-to-have part of urban aesthetics, but in functional terms.

“Planners and city officials should think about tree placement at least partly in terms of the heat-mitigating effect they have,” Beuster says.

“It’s not just about planting trees,” Duarte observes. “It’s about providing shade by planting trees. If you remove a tree that’s providing shade in a pedestrian area and you plant two other trees in a park, you are still removing part of the public function of the tree.”

He adds: “With increasing temperatures, providing shade is an essential public amenity. Along with providing transportation, I think providing shade in pedestrian spaces should almost be a public right.”

The Amsterdam Institute for Advanced Metropolitan Solutions and all members of the MIT Senseable City Consortium (including FAE Technology, Dubai Foundation, Sondotécnica, Seoul AI Foundation, Arnold Ventures, Sidara, Toyota, Abu Dhabi’s Department of Municipal Transportation, A2A, UnipolTech, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Hospital Israelita Albert Einstein, KACST, KAIST, and the cities of Laval, Amsterdam, and Rio de Janeiro) supported the research.

Study reveals climatic fingerprints of wildfires and volcanic eruptions

In research that could help elucidate humans’ role in global warming, scientists showed how three major natural events impacted global atmospheric temperatures.

Volcanoes and wildfires can inject millions of tons of gases and aerosol particles into the air, affecting temperatures on a global scale. But picking out the specific impact of individual events against a background of many contributing factors is like listening for one person’s voice from across a crowded concourse.

MIT scientists now have a way to quiet the noise and identify the specific signal of wildfires and volcanic eruptions, including their effects on Earth’s global atmospheric temperatures.

In a study appearing this week in the Proceedings of the National Academy of Sciences, the researchers report that they detected statistically significant changes in global atmospheric temperatures in response to three major natural events: the eruption of Mount Pinatubo in 1991, the Australian wildfires in 2019-2020, and the eruption of the underwater volcano Hunga Tonga in the South Pacific in 2022.

While the specifics of each event differed, all three events appeared to significantly affect temperatures in the stratosphere. The stratosphere lies above the troposphere, which is the lowest layer of the atmosphere, closest to the surface, where global warming has accelerated in recent years. In the new study, Pinatubo showed the classic pattern of stratospheric warming paired with tropospheric cooling. The Australian wildfires and the Hunga Tonga eruption also showed significant warming or cooling in the stratosphere, respectively, but they did not produce a robust, globally detectable tropospheric signal over the first two years following each event. This new understanding will help scientists further pin down the effect of human-related emissions on global temperature change.

“Understanding the climate responses to natural forcings is essential for us to interpret anthropogenic climate change,” says study author Yaowei Li, a former postdoc and currently a visiting scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “Unlike the global tropospheric and surface cooling caused by Pinatubo, our results also indicate that the Australian wildfires and Hunga Tonga eruption may not have played a role in the acceleration of global surface warming in recent years. So, there must be some other factors.”

The study’s co-authors include Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry at MIT, along with Benjamin Santer of the University of East Anglia, David Thompson of the University of East Anglia and Colorado State University, and Qiang Fu of the University of Washington.

Extraordinary events

The past several years have set back-to-back records for global average surface temperatures. The World Meteorological Organization recently confirmed that the years 2023 to 2025 were the three warmest years on record, while the past 11 years have been the 11 warmest years ever recorded. The world is warming, due mainly to human activities that have emitted huge amounts of greenhouse gases into the atmosphere over centuries.

In addition to greenhouse gases, the atmosphere has been on the receiving end of other large-scale emissions, including sulfur gases and water vapor from volcanic eruptions and smoke particles from wildfires. Li and his colleagues have wondered whether such natural events could have any global impact on temperatures, and whether such an effect would be detectable.

“These events are extraordinary and very unique in terms of the different materials they inject into different altitudes,” Li says. “So we asked the question: Do these events actually perturb the global temperature to a degree that could be identifiable from natural, meteorological noise, and could they contribute to some of the exceptional global surface warming we’ve seen in the last few years?”

In particular, the team looked for signals of global temperature change in response to three large-scale natural events. The Pinatubo eruption resulted in around 20 million tons of volcanic aerosols in the stratosphere, which was the largest volume ever recorded by modern satellite instruments. The Australian fires injected around 1 million tons of smoke particles into the upper troposphere and stratosphere. And the Hunga Tonga eruption produced the largest atmospheric explosion on satellite record, launching nearly 150 million tons of water vapor into the stratosphere.

If any natural event could measurably shift global temperatures, the team reasoned, it would be any of these three.

Natural signals

For their new study, the team took a signal-to-noise approach. They looked to minimize “noise” from other known influences on global temperatures in order to isolate the “signal,” such as a change in temperature associated specifically with one of the three natural events.

To do so, they looked first through satellite measurements taken by the Stratospheric Sounding Unit (SSU) and the Microwave and Advanced Microwave Sounding Units (MSU), which have been measuring global temperatures at different altitudes throughout the atmosphere since 1979. The team compiled SSU and MSU measurements from 1986 to the present day. From these measurements, the researchers could see long-term trends of steady tropospheric warming and stratospheric cooling. Those long-term trends are largely associated with anthropogenic greenhouse gases, which the team subtracted from the dataset.

What was left over was more of a level baseline, which still contained some confounding noise, in the form of natural variability. Global temperature changes can also be affected by phenomena such as El Niño and La Niña, which naturally warm and cool the Earth every few years. The sun also swings global temperatures on a roughly 11-year cycle. The team took this natural variability into account, and subtracted out the effects of these influences.

After minimizing such noise from their dataset, the team reasoned that whatever temperature changes remained could be more easily traced to the three large-scale natural events and quantified. And indeed, when they pinned the events to the temperature measurements, at the times that they occurred, they could plainly see how each event influenced temperatures around the world.

The team found that Pinatubo decreased global tropospheric temperatures by up to about 0.7 degree Celsius, for more than two years following the eruption. The volcanic sulfate aerosols essentially acted as many tiny reflectors, cooling the troposphere and surface by scattering sunlight back into space. At the same time, the aerosols, which remained in the stratosphere, also absorbed heat that was emitted from the surface, subsequently warming the stratosphere.

This finding agreed with many other studies of the event, which confirmed that the team’s approach is accurate. They applied the same method to the 2019-2020 Australian wildfires, and the 2022 underwater eruption — events where the influence on global temperatures is less clear.

For the Australian wildfires, they found that the smoke particles caused the global stratosphere to warm up, by up to about 0.77 degree Celsius, which persisted for about five months but did not produce a clear global tropospheric signal.

“In the end we found that the wildfire smoke caused a very strong warming in the stratosphere, because these materials are very different chemically from sulfate,” Li explains. “They are particles that are dark colored, meaning they are efficient at absorbing solar radiation. So, a relatively small amount of smoke particles can cause a dramatic warming.”

In the case of the Hunga Tonga, the underwater eruption triggered a global cooling effect in the middle-to-upper stratosphere, of up to about half a degree Celsius, lasting for several years.

“The Australian fires and the Hunga Tonga really packed a punch at stratospheric altitudes, and this study shows for the first time how to quantify how strong that punch was,” says Solomon. “I find their impact up high quite remarkable, but the ongoing issue is why the last several years have been so warm lower down, in the troposphere — ruling out those natural events points even more strongly at human influences.”

Fragile X study uncovers brain wave biomarker bridging humans and mice

Researchers find mice modeling the autism spectrum disorder fragile X syndrome exhibit the same pattern of differences in low-frequency waves as humans — a new marker for treatment studies.

Numerous potential treatments for neurological conditions, including autism spectrum disorders, have worked well in mice but then disappointed in humans. What would help is a non-invasive, objective readout of treatment efficacy that is shared in both species. 

In a new open-access study in Nature Communications, a team of MIT researchers, backed by collaborators across the United States and in the United Kingdom, identifies such a biomarker in fragile X syndrome, the most common inherited form of autism.

Led by postdoc Sara Kornfeld-Sylla and Picower Professor Mark Bear, the team measured the brain waves of human boys and men, with or without fragile X syndrome, and comparably aged male mice, with or without the genetic alteration that models the disorder. The novel approach Kornfeld-Sylla used for analysis enabled her to uncover specific and robust patterns of differences in low-frequency brain waves between typical and fragile X brains shared between species at each age range. In further experiments, the researchers related the brain waves to specific inhibitory neural activity in the mice and showed that the biomarker was able to indicate the effects of even single doses of a candidate treatment for fragile X called arbaclofen, which enhances inhibition in the brain.

Both Kornfeld-Sylla and Bear praised and thanked colleagues at Boston Children’s Hospital, the Phelan-McDermid Syndrome Foundation, Cincinnati Children’s Hospital, the University of Oklahoma, and King’s College London for gathering and sharing data for the study.

“This research weaves together these different datasets and finds the connection between the brain wave activity that’s happening in fragile X humans that is different from typically developed humans, and in the fragile X mouse model that is different than the ‘wild-type’ mice,” says Kornfeld-Sylla, who earned her PhD in Bear’s lab in 2024 and continued the research as a FRAXA postdoc. “The cross-species connection and the collaboration really makes this paper exciting.”

Bear, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT, says having a way to directly compare brain waves can advance treatment studies.

“Because that is something we can measure in mice and humans minimally invasively, you can pose the question: If drug treatment X affects this signature in the mouse, at what dose does that same drug treatment change that same signature in a human?” Bear says. “Then you have a mapping of physiological effects onto measures of behavior. And the mapping can go both ways.”

Peaks and powers

In the study, the researchers measured EEG over the occipital lobe of humans and on the surface of the visual cortex of the mice. They measured power across the frequency spectrum, replicating previous reports of altered low-frequency brain waves in adult humans with fragile X and showing for the first time how these disruptions differ in children with fragile X.

To enable comparisons with mice, Kornfeld-Sylla subtracted out background activity to specifically isolate only “periodic” fluctuations in power (i.e., the brain waves) at each frequency. She also disregarded the typical way brain waves are grouped by frequency (into distinct bands with Greek letter designations delta, theta, alpha, beta, and gamma) so that she could simply juxtapose the periodic power spectra of the humans and mice without trying to match them band by band (e.g., trying to compare the mouse “alpha” band to the human one). This turned out to be crucial because the significant, similar patterns exhibited by the mice actually occurred in a different low-frequency band than in the humans (theta vs. alpha). Both species also had alterations in higher-frequency bands in fragile X, but Kornfeld-Sylla noted that the differences in the low-frequency brainwaves are easier to measure and more reliable in humans, making them a more promising biomarker.

So what patterns constitute the biomarker? In adult men and mice alike, a peak in the power of low-frequency waves is shifted to a significantly slower frequency in fragile X cases compared to in neurotypical cases. Meanwhile, in fragile X boys and juvenile mice, while the peak is somewhat shifted to a slower frequency, what is really significant is a reduced power in that same peak.

The researchers were also able to discern that the peak in question is actually made of two distinct subpeaks, and that the lower-frequency subpeak is the one that varies specifically with fragile X syndrome.

Curious about the neural activity underlying the measurements, the researchers engaged in experiments in which they turned off activity of two different kinds of inhibitory neurons that are known to help produce and shape brain wave patterns: somatostatin-expressing and parvalbumin-expressing interneurons. Manipulating the somatostatin neurons specifically affected the lower-frequency subpeak that contained the newly discovered biomarker in fragile X model mice.

Drug testing

Somatostatin interneurons exert their effects on the neurons they connect to via the neurotransmitter chemical GABA, and evidence from prior studies suggest that GABA receptivity is reduced in fragile X syndrome. A therapeutic approach pioneered by Bear and others has been to give the drug arbaclofen, which enhances GABA activity. In the new study, the researchers treated both control and fragile X model mice with arbaclofen to see how it affected the low-frequency biomarker.

Even the lowest administered single dose made a significant difference in the neurotypical mice, which is consistent with those mice having normal GABA responsiveness. Fragile X mice needed a higher dose, but after one was administered, there was a notable increase in the power of the key subpeak, reducing the deficit exhibited by juvenile mice.

The arbaclofen experiments therefore demonstrated that the biomarker provides a significant readout of an underlying pathophysiology of fragile X: the reduced GABA responsiveness. Bear also noted that it helped to identify a dose at which arbaclofen exerted a corrective effect, even though the drug was only administered acutely, rather than chronically. An arbaclofen therapy would, of course, be given over a long time frame, not just once.

“This is a proof of concept that a drug treatment could move this phenotype acutely in a direction that makes it closer to wild-type,” Bear says. “This effort reveals that we have readouts that can be sensitive to drug treatments.”

Meanwhile, Kornfeld-Sylla notes, there is a broad spectrum of brain disorders in which human patients exhibit significant differences in low-frequency (alpha) brain waves compared to neurotypical peers.

“Disruptions akin to the biomarker we found in this fragile X study might prove to be evident in mouse models of those other disorders, too,” she says. “Identifying this biomarker could broadly impact future translational neuroscience research.”

The paper’s other authors are Cigdem Gelegen, Jordan Norris, Francesca Chaloner, Maia Lee, Michael Khela, Maxwell Heinrich, Peter Finnie, Lauren Ethridge, Craig Erickson, Lauren Schmitt, Sam Cooke, and Carol Wilkinson.

The National Institutes of Health, the National Science Foundation, the FRAXA Foundation, the Pierce Family Fragile X Foundation, the Autism Science Foundation, the Thrasher Research Fund, Harvard University, the Simons Foundation, Wellcome, the Biotechnology and Biological Sciences Research Council, and the Freedom Together Foundation provided support for the research.

Chip-processing method could assist cryptography schemes to keep data secure

By enabling two chips to authenticate each other using a shared fingerprint, this technique can improve privacy and energy efficiency.

Just like each person has unique fingerprints, every CMOS chip has a distinctive “fingerprint” caused by tiny, random manufacturing variations. Engineers can leverage this unforgeable ID for authentication, to safeguard a device from attackers trying to steal private data.

But these cryptographic schemes typically require secret information about a chip’s fingerprint to be stored on a third-party server. This creates security vulnerabilities and requires additional memory and computation.

To overcome this limitation, MIT engineers developed a manufacturing method that enables secure, fingerprint-based authentication, without the need to store secret information outside the chip.

They split a specially designed chip during fabrication in such a way that each half has an identical, shared fingerprint that is unique to these two chips. Each chip can be used to directly authenticate the other. This low-cost fingerprint fabrication method is compatible with standard CMOS foundry processes and requires no special materials.

The technique could be useful in power-constrained electronic systems with non-interchangeable device pairs, like an ingestible sensor pill and its paired wearable patch that monitor gastrointestinal health conditions. Using a shared fingerprint, the pill and patch can authenticate each other without a device in between to mediate.

“The biggest advantage of this security method is that we don’t need to store any information. All the secrets will always remain safe inside the silicon. This can give a higher level of security. As long as you have this digital key, you can always unlock the door,” says Eunseok Lee, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this security method.

Lee is joined on the paper by EECS graduate students Jaehong Jung and Maitreyi Ashok; as well as co-senior authors Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science, and Ruonan Han, a professor of EECS and a member of the MIT Research Laboratory of Electronics. The research was recently presented at the IEEE International Solid-States Circuits Conference.

“Creation of shared encryption keys in trusted semiconductor foundries could help break the tradeoffs between being more secure and more convenient to use for protection of data transmission,” Han says. “This work, which is digital-based, is still a preliminary trial in this direction; we are exploring how more complex, analog-based secrecy can be duplicated — and only duplicated once.”

Leveraging variations

Even though they are intended to be identical, each CMOS chip is slightly different due to unavoidable microscopic variations during fabrication. These randomizations give each chip a unique identifier, known as a physical unclonable function (PUF), that is nearly impossible to replicate.

A chip’s PUF can be used to provide security just like the human fingerprint identification system on a laptop or door panel.

For authentication, a server sends a request to the device, which responds with a secret key based on its unique physical structure. If the key matches an expected value, the server authenticates the device.

But the PUF authentication data must be registered and stored in a server for access later, creating a potential security vulnerability.

“If we don’t need to store information on these unique randomizations, then the PUF becomes even more secure,” Lee says.

The researchers wanted to accomplish this by developing a matched PUF pair on two chips. One could authenticate the other directly, without the need to store PUF data on third-party servers.

As an analogy, consider a sheet of paper torn in half. The torn edges are random and unique, but the pieces have a shared randomness because they fit back together perfectly along the torn edge.

While CMOS chips aren’t torn in half like paper, many are fabricated at once on a silicon wafer which is diced to separate the individual chips.

By incorporating shared randomness at the edge of two chips before they are diced to separate them, the researchers could create a twin PUF that is unique to these two chips.

“We needed to find a way to do this before the chip leaves the foundry, for added security. Once the fabricated chip enters the supply chain, we won’t know what might happen to it,” Lee explains.

Sharing randomness

To create the twin PUF, the researchers change the properties of a set of transistors fabricated along the edge of two chips, using a process called gate oxide breakdown.

Essentially, they pump high voltage into a pair of transistors by shining light with a low-cost LED until the first transistor breaks down. Because of tiny manufacturing variations, each transistor has a slightly different breakdown time. The researchers can use this unique breakdown state as the basis for a PUF.

To enable a twin PUF, the MIT researchers fabricate two pairs of transistors along the edge of two chips before they are diced to separate them. By connecting the transistors with metal layers, they create paired structures that have correlated breakdown states. In this way, they enable a unique PUF to be shared by each pair of transistors.

After shining LED light to create the PUF, they dice the chips between the transistors so there is one pair on each device, giving each separate chip a shared PUF.

“In our case, transistor breakdown has not been modeled well in many of the simulations we had, so there was a lot of uncertainty about how the process would work. Figuring out all the steps, and the order they needed to happen, to generate this shared randomness is the novelty of this work,” Lee says.

After finetuning their PUF generation process, the researchers developed a prototype pair of twin PUF chips in which the randomization was matched with more than 98 percent reliability. This would ensure the generated PUF key matches consistently, enabling secure authentication.

Because they generated this twin PUF using circuit techniques and low-cost LEDs, the process would be easier to implement at scale than other methods that are more complicated or not compatible with standard CMOS fabrication.

“In the current design, shared randomness generated by transistor breakdown is immediately converted into digital data. Future versions could preserve this shared randomness directly within the transistors, strengthening security at the most fundamental physical level of the chip,” Lee says.

“There is a rapidly increasing demand for physical-layer security for edge devices, such as between medical sensors and devices on a body, which often operate under strict energy constraints. A twin-paired PUF approach enables secure communication between nodes without the burden of heavy protocol overhead, thereby delivering both energy efficiency and strong security. This initial demonstration paves the way for innovative advancements in secure hardware design,” Chandrakasan adds.

This work is funded by Lockheed Martin, the MIT School of Engineering MathWorks Fellowship, and the Korea Foundation for Advanced Studies Fellowship.

MIT faculty, alumni named 2026 Sloan Research Fellows

Annual award honors early-career researchers for creativity, innovation, and research accomplishments.

Eight MIT faculty and 22 additional MIT alumni are among 126 early-career researchers honored with 2026 Sloan Research Fellowships by the Alfred P. Sloan Foundation.

The fellowships honor exceptional researchers at U.S. and Canadian educational institutions, whose creativity, innovation, and research accomplishments make them stand out as the next generation of leaders. Winners receive a two-year, $75,000 fellowship that can be used flexibly to advance the fellow’s research.

"The Sloan Research Fellows are among the most promising early-career researchers in the U.S. and Canada, already driving meaningful progress in their respective disciplines," says Stacie Bloom, president and chief executive officer of the Alfred P. Sloan Foundation. "We look forward to seeing how these exceptional scholars continue to unlock new scientific advancements, redefine their fields, and foster the well-being and knowledge of all."

Including this year’s recipients, a total of 341 MIT faculty have received Sloan Research Fellowships since the program’s inception in 1955. The MIT recipients are:

Jacopo Borga is interested in probability theory and its connections to combinatorics, and in mathematical physics. He studies various random combinatorial structures — mathematical objects such as graphs or permutations — and their patterns and behavior at a large scale. This research includes random permutons, meanders, multidimensional constrained Brownian motions, Schramm-Loewner evolutions, and Liouville quantum gravity. Borga earned bachelor’s and master’s degrees in mathematics from the Università degli Studi di Padova in Italy, and a master’s degree in mathematics from Université Sorbonne Paris Cité in France, then proceeded to complete a PhD in mathematics at Unstitut für Mathematik at the Universität Zürich in Switzerland. Borga was an assistant professor at Stanford University before joining MIT as an assistant professor of mathematics in 2024.

Anna-Christina Eilers is an astrophysicist and assistant professor at MIT’s Department of Physics as well as a member of the MIT Kavli Institute for Astrophysics and Space Research. Her work explores how black holes form and evolve across cosmic time, studying their origins and the role they play in shaping our universe. She leverages multi-wavelength data from telescopes all around the world and in space to study how the first galaxies, black holes, and quasars emerged during an epoch known as the Cosmic Dawn of our universe. She grew up in Germany and completed her PhD at the Max Planck Institute for Astronomy in Heidelberg. Subsequently, she was awarded a NASA Hubble Fellowship and a Pappalardo Fellowship to continue her research at MIT, where she joined the faculty in 2023. Her work has been recognized with several honors, including the PhD Prize of the International Astronomical Union, the Otto Hahn Medal of the Max Planck Society, and the Ludwig Biermann Prize of the German Astronomical Society.

Linlin Fan is the Samuel A. Goldblith Career Development Assistant Professor of Applied Biology in the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT. Her lab focuses on the development and application of advanced all-optical physiological techniques to understand the plasticity mechanisms underlying learning and memory. She has developed and applied high-speed, cellular-precision all-optical physiological techniques for simultaneously mapping and controlling membrane potential in specific neurons in behaving mammals. Prior to joining MIT, Fan was a Helen Hay Whitney Postdoctoral Fellow in Karl Deisseroth’s laboratory at Stanford University. She obtained her PhD in chemical biology from Harvard University in 2019 with Adam Cohen. Her work has been recognized by several awards, including the Larry Katz Memorial Lecture Award from the Cold Spring Harbor Laboratory, Helen Hay Whitney Fellowship, Career Award at the Scientific Interface from the Burroughs Wellcome Fund, Klingenstein-Simons Fellowship Award, Searle Scholar Award, and NARSAD Young Investigator Award.

Yoon Kim is an associate professor in the Department of EECS and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT-IBM Watson AI Lab, where he works on natural language processing and machine learning. Kim earned a PhD in computer science at Harvard University, an MS in data science from New York University, an MA in statistics from Columbia University, and BA in both math and economics from Cornell University. He joined EECS in 2021, after spending a year as a postdoc at MIT-IBM Watson AI Lab.

Haihao Lu PhD ’19 is the Cecil and Ida Green Career Development Assistant Professor, and an assistant professor of operations research/statistics at the MIT Sloan School of Management. Lu’s research lies at the intersection of optimization, computation, and data science, with a focus on pushing the computational and mathematical frontiers of large-scale optimization. Much of his work is inspired by real-world challenges faced by leading technology companies and optimization software companies, such as first-order methods and scalable solvers and data-driven optimization for resource allocation. His research has had real-world impact, generating substantial revenue and advancing the state of practice in large-scale optimization, and has been recognized by several research awards. Before joining MIT Sloan, he was an assistant professor at the University of Chicago Booth School of Business and a faculty researcher at Google Research’s large-scale optimization team. He obtained his PhD in mathematics and operations research at MIT in 2019.

Brett McGuire is the Class of 1943 Career Development Associate Professor of Chemistry at MIT. He completed his undergraduate studies at the University of Illinois at Urbana-Champaign before earning an MS from Emory University and a PhD from the Caltech, both in physical chemistry. After Jansky and Hubble postdoctoral fellowships at the National Radio Astronomy Observatory, he joined the MIT faculty in 2020 and was promoted to associate professor in 2025. The McGuire Group integrates physical chemistry, molecular spectroscopy, and observational astrophysics to explore how the chemical building blocks of life evolve alongside the formation of stars and planets.

Anand Natarajan PhD ’18 is an associate professor in EECS and a principal investigator in CSAIL and the MIT-IBM Watson AI Lab. His research is mainly in quantum complexity theory, with a focus on the power of interactive proofs and arguments in a quantum world. Essentially, his work attempts to assess the complexity of computational problems in a quantum setting, determining both the limits of quantum computers’ capability and the trustworthiness of their output. Natarajan earned his PhD in physics from MIT, and an MS in computer science and BS in physics from Stanford University. Prior to joining MIT in 2020, he spent time as a postdoc at the Institute for Quantum Information and Matter at Caltech.

Mengjia Yan is an associate professor in the Department of EECS and a principal investigator in CSAIL. She is a security computer architect whose research advances secure processor design by bridging computer architecture, systems security, and formal methods. Her work identifies critical blind spots in hardware threat models and improves the resilience of real-world systems against information leakage and exploitation. Several of her discoveries have influenced commercial processor designs and contributed to changes in how hardware security risks are evaluated in practice. In parallel, Yan develops architecture-driven techniques to improve the scalability of formal verification and introduces new design principles toward formally verifiable processors. She also designed the Secure Hardware Design (SHD) course, now widely adopted by universities worldwide to teach computer architecture security from both offensive and defensive perspectives.

The following MIT alumni also received fellowships:

Ashok Ajoy PhD ’16
Chibueze Amanchukwu PhD ’17
Annie M. Bauer PhD ’17
Kimberly K. Boddy ’07
danah boyd SM ’02
Yuan Cao SM ’16, PhD ’20
Aloni Cohen SM ’15, PhD ’19
Fei Dai PhD ’19
Madison M. Douglas ’16
Philip Engel ’10
Benjamin Eysenbach ’17
Tatsunori B. Hashimoto SM ’14, PhD ’16
Xin Jin ’10
Isaac Kim ’07
Christina Patterson PhD ’19
Katelin Schutz ’14
Karthik Shekhar PhD ’15
Shriya S. Srinivasan PhD ’20
Jerzy O. Szablowski ’09
Anna Wuttig PhD ’18
Zoe Yan PhD ’20
Lingfu Zhang ’18

Exposing biases, moods, personalities, and abstract concepts hidden in large language models

A new method developed at MIT could root out vulnerabilities and improve LLM safety and performance.

By now, ChatGPT, Claude, and other large language models have accumulated so much human knowledge that they’re far from simple answer-generators; they can also express abstract concepts, such as certain tones, personalities, biases, and moods. However, it’s not obvious exactly how these models represent abstract concepts to begin with from the knowledge they contain.

Now a team from MIT and the University of California San Diego has developed a way to test whether a large language model (LLM) contains hidden biases, personalities, moods, or other abstract concepts. Their method can zero in on connections within a model that encode for a concept of interest. What’s more, the method can then manipulate, or “steer” these connections, to strengthen or weaken the concept in any answer a model is prompted to give.

The team proved their method could quickly root out and steer more than 500 general concepts in some of the largest LLMs used today. For instance, the researchers could home in on a model’s representations for personalities such as “social influencer” and “conspiracy theorist,” and stances such as “fear of marriage” and “fan of Boston.” They could then tune these representations to enhance or minimize the concepts in any answers that a model generates.

In the case of the “conspiracy theorist” concept, the team successfully identified a representation of this concept within one of the largest vision language models available today. When they enhanced the representation, and then prompted the model to explain the origins of the famous “Blue Marble” image of Earth taken from Apollo 17, the model generated an answer with the tone and perspective of a conspiracy theorist.

The team acknowledges there are risks to extracting certain concepts, which they also illustrate (and caution against). Overall, however, they see the new approach as a way to illuminate hidden concepts and potential vulnerabilities in LLMs, that could then be turned up or down to improve a model’s safety or enhance its performance.

“What this really says about LLMs is that they have these concepts in them, but they’re not all actively exposed,” says Adityanarayanan “Adit” Radhakrishnan, assistant professor of mathematics at MIT. “With our method, there’s ways to extract these different concepts and activate them in ways that prompting cannot give you answers to.”

The team published their findings today in a study appearing in the journal Science. The study’s co-authors include Radhakrishnan, Daniel Beaglehole and Mikhail Belkin of UC San Diego, and Enric Boix-Adserà of the University of Pennsylvania.

A fish in a black box

As use of OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and other artificial intelligence assistants has exploded, scientists are racing to understand how models represent certain abstract concepts such as “hallucination” and “deception.” In the context of an LLM, a hallucination is a response that is false or contains misleading information, which the model has “hallucinated,” or constructed erroneously as fact.

To find out whether a concept such as “hallucination” is encoded in an LLM, scientists have often taken an approach of “unsupervised learning” — a type of machine learning in which algorithms broadly trawl through unlabeled representations to find patterns that might relate to a concept such as “hallucination.” But to Radhakrishnan, such an approach can be too broad and computationally expensive.

“It’s like going fishing with a big net, trying to catch one species of fish. You’re gonna get a lot of fish that you have to look through to find the right one,” he says. “Instead, we’re going in with bait for the right species of fish.”

He and his colleagues had previously developed the beginnings of a more targeted approach with a type of predictive modeling algorithm known as a recursive feature machine (RFM). An RFM is designed to directly identify features or patterns within data by leveraging a mathematical mechanism that neural networks — a broad category of AI models that includes LLMs — implicitly use to learn features.

Since the algorithm was an effective, efficient approach for capturing features in general, the team wondered whether they could use it to root out representations of concepts, in LLMs, which are by far the most widely used type of neural network and perhaps the least well-understood.

“We wanted to apply our feature learning algorithms to LLMs to, in a targeted way, discover representations of concepts in these large and complex models,” Radhakrishnan says.

Converging on a concept

The team’s new approach identifies any concept of interest within a LLM and “steers” or guides a model’s response based on this concept. The researchers looked for 512 concepts within five classes: fears (such as of marriage, insects, and even buttons); experts (social influencer, medievalist); moods (boastful, detachedly amused); a preference for locations (Boston, Kuala Lumpur); and personas (Ada Lovelace, Neil deGrasse Tyson).

The researchers then searched for representations of each concept in several of today’s large language and vision models. They did so by training RFMs to recognize numerical patterns in an LLM that could represent a particular concept of interest.

A standard large language model is, broadly, a neural network that takes a natural language prompt, such as “Why is the sky blue?” and divides the prompt into individual words, each of which is encoded mathematically as a list, or vector, of numbers. The model takes these vectors through a series of computational layers, creating matrices of many numbers that, throughout each layer, are used to identify other words that are most likely to be used to respond to the original prompt. Eventually, the layers converge on a set of numbers that is decoded back into text, in the form of a natural language response.

The team’s approach trains RFMs to recognize numerical patterns in an LLM that could be associated with a specific concept. As an example, to see whether an LLM contains any representation of a “conspiracy theorist,” the researchers would first train the algorithm to identify patterns among LLM representations of 100 prompts that are clearly related to conspiracies, and 100 other prompts that are not. In this way, the algorithm would learn patterns associated with the conspiracy theorist concept. Then, the researchers can mathematically modulate the activity of the conspiracy theorist concept by perturbing LLM representations with these identified patterns. 

The method can be applied to search for and manipulate any general concept in an LLM. Among many examples, the researchers identified representations and manipulated an LLM to give answers in the tone and perspective of a “conspiracy theorist.” They also identified and enhanced the concept of “anti-refusal,” and showed that whereas normally, a model would be programmed to refuse certain prompts, it instead answered, for instance giving instructions on how to rob a bank.

Radhakrishnan says the approach can be used to quickly search for and minimize vulnerabilities in LLMs. It can also be used to enhance certain traits, personalities, moods, or preferences, such as emphasizing the concept of “brevity” or “reasoning” in any response an LLM generates. The team has made the method’s underlying code publicly available.

“LLMs clearly have a lot of these abstract concepts stored within them, in some representation,” Radhakrishnan says. “There are ways where, if we understand these representations well enough, we can build highly specialized LLMs that are still safe to use but really effective at certain tasks.”

This work was supported, in part, by the National Science Foundation, the Simons Foundation, the TILOS institute, and the U.S. Office of Naval Research. 

Parking-aware navigation system could prevent frustration and emissions

By minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.

It happens every day — a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.

Most popular navigation systems send drivers to a location without considering the extra time that could be needed to find parking. This causes more than just a headache for drivers. It can worsen congestion and increase emissions by causing motorists to cruise around looking for a parking spot. This underestimation could also discourage people from taking mass transit because they don’t realize it might be faster than driving and parking.

MIT researchers tackled this problem by developing a system that can be used to identify parking lots that offer the best balance of proximity to the desired location and likelihood of parking availability. Their adaptable method points users to the ideal parking area rather than their destination.

In simulated tests with real-world traffic data from Seattle, this technique achieved time savings of up to 66 percent in the most congested settings. For a motorist, this would reduce travel time by about 35 minutes, compared to waiting for a spot to open in the closest parking lot.

While they haven’t designed a system ready for the real world yet, their demonstrations show the viability of this approach and indicate how it could be implemented.

“This frustration is real and felt by a lot of people, and the bigger issue here is that systematically underestimating these drive times prevents people from making informed choices. It makes it that much harder for people to make shifts to public transit, bikes, or alternative forms of transportation,” says MIT graduate student Cameron Hickert, lead author on a paper describing the work.

Hickert is joined on the paper by Sirui Li PhD ’25; Zhengbing He, a research scientist in the Laboratory for Information and Decision Systems (LIDS); and senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of LIDS. The research appears today in Transactions on Intelligent Transportation Systems.

Probable parking

To solve the parking problem, the researchers developed a probability-aware approach that considers all possible public parking lots near a destination, the distance to drive there from a point of origin, the distance to walk from each lot to the destination, and the likelihood of parking success.

The approach, based on dynamic programming, works backward from good outcomes to calculate the best route for the user.

Their method also considers the case where a user arrives at the ideal parking lot but can’t find a space. It takes into the account the distance to other parking lots and the probability of success of parking at each.

“If there are several lots nearby that have slightly lower probabilities of success, but are very close to each other, it might be a smarter play to drive there rather than going to the higher-probability lot and hoping to find an opening. Our framework can account for that,” Hickert says.

In the end, their system can identify the optimal lot that has the lowest expected time required to drive, park, and walk to the destination.

But no motorist expects to be the only one trying to park in a busy city center. So, this method also incorporates the actions of other drivers, which affect the user’s probability of parking success.

For instance, another driver may arrive at the user’s ideal lot first and take the last parking spot. Or another motorist could try parking in another lot but then park in the user’s ideal lot if unsuccessful. In addition, another motorist may park in a different lot and cause spillover effects that lower the user’s chances of success.

“With our framework, we show how you can model all those scenarios in a very clean and principled manner,” Hickert says.

Crowdsourced parking data

The data on parking availability could come from several sources. For example, some parking lots have magnetic detectors or gates that track the number of cars entering and exiting.

But such sensors aren’t widely used, so to make their system more feasible for real-world deployment, the researchers studied the effectiveness of using crowdsourced data instead.

For instance, users could indicate available parking using an app. Data could also be gathered by tracking the number of vehicles circling to find parking, or how many enter a lot and exit after being unsuccessful.

Someday, autonomous vehicles could even report on open parking spots they drive by.

“Right now, a lot of that information goes nowhere. But if we could capture it, even by having someone simply tap ‘no parking’ in an app, that could be an important source of information that allows people to make more informed decisions,” Hickert adds.

The researchers evaluated their system using real-world traffic data from the Seattle area, simulating different times of day in a congested urban setting and a suburban area. In congested settings, their approach cut total travel time by about 60 percent compared to sitting and waiting for a spot to open, and by about 20 percent compared to a strategy of continually driving to the next closet parking lot.

They also found that crowdsourced observations of parking availability would have an error rate of only about 7 percent, compared to actual parking availability. This indicates it could be an effective way to gather parking probability data.

In the future, the researchers want to conduct larger studies using real-time route information in an entire city. They also want to explore additional avenues for gathering data on parking availability, such as using satellite images, and estimate potential emissions reductions.

“Transportation systems are so large and complex that they are really hard to change. What we look for, and what we found with this approach, is small changes that can have a big impact to help people make better choices, reduce congestion, and reduce emissions,” says Wu.

This research was supported, in part, by Cintra, the MIT Energy Initiative, and the National Science Foundation.

Personalization features can make LLMs more agreeable

The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.

Many of the latest large language models (LLMs) are designed to remember details from past conversations or store user profiles, enabling these models to personalize responses.

But researchers from MIT and Penn State University found that, over long conversations, such personalization features often increase the likelihood an LLM will become overly agreeable or begin mirroring the individual’s point of view.

This phenomenon, known as sycophancy, can prevent a model from telling a user they are wrong, eroding the accuracy of the LLM’s responses. In addition, LLMs that mirror someone’s political beliefs or worldview can foster misinformation and distort a user’s perception of reality.

Unlike many past sycophancy studies that evaluate prompts in a lab setting without context, the MIT researchers collected two weeks of conversation data from humans who interacted with a real LLM during their daily lives. They studied two settings: agreeableness in personal advice and mirroring of user beliefs in political explanations.

Although interaction context increased agreeableness in four of the five LLMs they studied, the presence of a condensed user profile in the model’s memory had the greatest impact. On the other hand, mirroring behavior only increased if a model could accurately infer a user’s beliefs from the conversation.

The researchers hope these results inspire future research into the development of personalization methods that are more robust to LLM sycophancy.

“From a user perspective, this work highlights how important it is to understand that these models are dynamic and their behavior can change as you interact with them over time. If you are talking to a model for an extended period of time and start to outsource your thinking to it, you may find yourself in an echo chamber that you can’t escape. That is a risk users should definitely remember,” says Shomik Jain, a graduate student in the Institute for Data, Systems, and Society (IDSS) and lead author of a paper on this research.

Jain is joined on the paper by Charlotte Park, an electrical engineering and computer science (EECS) graduate student at MIT; Matt Viana, a graduate student at Penn State University; as well as co-senior authors Ashia Wilson, the Lister Brothers Career Development Professor in EECS and a principal investigator in LIDS; and Dana Calacci PhD ’23, an assistant professor at the Penn State. The research will be presented at the ACM CHI Conference on Human Factors in Computing Systems.

Extended interactions

Based on their own sycophantic experiences with LLMs, the researchers started thinking about potential benefits and consequences of a model that is overly agreeable. But when they searched the literature to expand their analysis, they found no studies that attempted to understand sycophantic behavior during long-term LLM interactions.

“We are using these models through extended interactions, and they have a lot of context and memory. But our evaluation methods are lagging behind. We wanted to evaluate LLMs in the ways people are actually using them to understand how they are behaving in the wild,” says Calacci.

To fill this gap, the researchers designed a user study to explore two types of sycophancy: agreement sycophancy and perspective sycophancy.

Agreement sycophancy is an LLM’s tendency to be overly agreeable, sometimes to the point where it gives incorrect information or refuses the tell the user they are wrong. Perspective sycophancy occurs when a model mirrors the user’s values and political views.

“There is a lot we know about the benefits of having social connections with people who have similar or different viewpoints. But we don’t yet know about the benefits or risks of extended interactions with AI models that have similar attributes,” Calacci adds.

The researchers built a user interface centered on an LLM and recruited 38 participants to talk with the chatbot over a two-week period. Each participant’s conversations occurred in the same context window to capture all interaction data.

Over the two-week period, the researchers collected an average of 90 queries from each user.

They compared the behavior of five LLMs with this user context versus the same LLMs that weren’t given any conversation data.

“We found that context really does fundamentally change how these models operate, and I would wager this phenomenon would extend well beyond sycophancy. And while sycophancy tended to go up, it didn’t always increase. It really depends on the context itself,” says Wilson.

Context clues

For instance, when an LLM distills information about the user into a specific profile, it leads to the largest gains in agreement sycophancy. This user profile feature is increasingly being baked into the newest models.

They also found that random text from synthetic conversations also increased the likelihood some models would agree, even though that text contained no user-specific data. This suggests the length of a conversation may sometimes impact sycophancy more than content, Jain adds.

But content matters greatly when it comes to perspective sycophancy. Conversation context only increased perspective sycophancy if it revealed some information about a user’s political perspective.

To obtain this insight, the researchers carefully queried models to infer a user’s beliefs then asked each individual if the model’s deductions were correct. Users said LLMs accurately understood their political views about half the time.

“It is easy to say, in hindsight, that AI companies should be doing this kind of evaluation. But it is hard and it takes a lot of time and investment. Using humans in the evaluation loop is expensive, but we’ve shown that it can reveal new insights,” Jain says.

While the aim of their research was not mitigation, the researchers developed some recommendations.

For instance, to reduce sycophancy one could design models that better identify relevant details in context and memory. In addition, models can be built to detect mirroring behaviors and flag responses with excessive agreement. Model developers could also give users the ability to moderate personalization in long conversations.

“There are many ways to personalize models without making them overly agreeable. The boundary between personalization and sycophancy is not a fine line, but separating personalization from sycophancy is an important area of future work,” Jain says.

“At the end of the day, we need better ways of capturing the dynamics and complexity of what goes on during long conversations with LLMs, and how things can misalign during that long-term process,” Wilson adds.

3D-printing platform rapidly produces complex electric machines

Overcoming challenges of 3D printing with multiple functional materials, MIT researchers fabricated an electric linear motor in hours.

A broken motor in an automated machine can bring production on a busy factory floor to a halt. If engineers can’t find a replacement part, they may have to order one from a distributor hundreds of miles away, leading to costly production delays.

It would be easier, faster, and cheaper to make a new motor onsite, but fabricating electric machines typically requires specialized equipment and complicated processes, which restricts production to a few manufacturing centers.

In an effort to democratize the manufacturing of complex devices, MIT researchers have developed a multimaterial 3D-printing platform that could be used to fully print electric machines in a single step.

They designed their system to process multiple functional materials, including electrically conductive materials and magnetic materials, using four extrusion tools that can handle varied forms of printable material. The printer switches between extruders, which deposit material by squeezing it through a nozzle as it fabricates a device one layer at a time.

The researchers used this system to produce a fully 3D-printed electric linear motor in a matter of hours using five materials. They only needed to perform one post-processing step for the motor to be fully functional.

The assembled device performed as well or better than similar motors that require more complex fabrication methods or additional post-processing steps.

In the long run, this 3D printing platform could be used to rapidly fabricate customizable electronic components for robots, vehicles, or medical equipment with much less waste.

“This is a great feat, but it is just the beginning. We have an opportunity to fundamentally change the way things are made by making hardware onsite in one step, rather than relying on a global supply chain. With this demonstration, we’ve shown that this is feasible,” says Luis Fernando Velásquez-García, a principal research scientist in MIT’s Microsystems Technology Laboratories (MTL) and senior author of a paper describing the 3D-printing platform, which appears today in Virtual and Physical Prototyping.

He is joined on the paper by electrical engineering and computer science (EECS) graduate students Jorge Cañada, who is the lead author, and Zoey Bigelow.

More materials

The researchers focused on extrusion 3D printing, a tried-and-true method that involves squirting material through a nozzle to fabricate an object one layer at a time.

To fabricate an electric machine, the researchers needed to be able to switch between multiple materials that offer different functionalities. For instance, the device would need an electrically conductive material to carry electric current and hard magnetic materials to generate magnetic fields for efficient energy conversion.

Most multimaterial extrusion 3D printing systems can only switch between two materials that come in the same form, such as filament or pellets, so the researchers had to design their own. They retrofit an existing printer with four extruders that can each handle a different form of feedstock.

They carefully designed each extruder to balance the requirements and limitations of the material. For instance, the electrically conductive material must be able to harden without the use of too much heat or UV light because this can degrade the dielectric material.

At the same time, the best-performing electrically conductive materials come in the form of inks which are extruded using a pressure system. This process has vastly different requirements than standard extruders that use heated nozzles to squirt melted filament or pellets.

“There were significant engineering challenges. We had to figure out how to marry together many different expressions of the same printing method — extrusion — seamlessly into one platform,” Velásquez-García says.

The researchers utilized strategically placed sensors and a novel control framework so each tool is picked up and put down consistently by the platform’s robotic arms, and so each nozzle moves precisely and predictably.

This ensures each layer of material lines up properly — even a slight misalignment can derail the performance of the finished machine.

Making a motor

After perfecting the printing platform, the researchers fabricated a linear motor, which generates straight-line motion (as opposed to a rotating motor, like the one in a car). Linear motors are used in applications like pick-and-place robotics, optical systems, and baggage conveyers.

They fabricated the motor in about three hours and only needed to magnetize the hard magnetic materials after printing to enable full functionality. The researchers estimate total material costs would be about 50 cents per device. Their 3D-printed motor was able to generate several times more actuation than a common type of linear engine that relies on complex hydraulic amplifiers. 

“Even though we are excited by this engine and its performance, we are equally inspired because this is just an example of so many other things to come that could dramatically change how electronics are manufactured,” says Velásquez-García.

In the future, the researchers want to integrate the magnetization step into the multimaterial extrusion process, demonstrate the fabrication of fully 3D-printed rotary electrical motors, and add more tools to the platform to enable monolithic fabrication of more complex electronic devices.

This research is funded, in part, by Empiriko Corporation and the La Caixa Foundation.

New study unveils the mechanism behind “boomerang” earthquakes

These ricocheting ruptures may be more common than previously thought.

An earthquake typically sets off ruptures that ripple out from its underground origins. But on rare occasions, seismologists have observed quakes that reverse course, further shaking up areas that they passed through only seconds before. These “boomerang” earthquakes often occur in regions with complex fault systems. But a new study by MIT researchers predicts that such ricochet ruptures can occur even along simple faults.

The study, which appears today in the journal AGU Advances, reports that boomerang earthquakes can happen along a simple fault under several conditions: if the quake propagates out in just one direction, over a large enough distance, and if friction along the rupturing fault builds and subsides rapidly during the quake. Under these conditions, even a simple straight fault, like some segments of the San Andreas fault in California, could experience a boomerang quake.

These newly identified conditions are relatively common, suggesting that many earthquakes that have occurred along simple faults may have experienced a boomerang effect, or what scientists term “back-propagating fronts.”

“Our work suggests that these boomerang quakes may have been undetected in a number of cases,” says study author Yudong Sun, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “We do think this behavior may be more common than we have seen so far in the seismic data.”

The new results could help scientists better assess future hazards in simple fault zones where boomerang quakes could potentially strike twice.

“In most cases, it would be impossible for a person to tell that an earthquake has propagated back just from the ground shaking, because ground motion is complex and affected by many factors,” says co-author Camilla Cattania, the Cecil and Ida Green Career Development Professor of Geophysics at MIT. “However, we know that shaking is amplified in the direction of rupture, and buildings would shake more in response. So there is a real effect in terms of the damage that results. That’s why understanding where these boomerang events could occur matters.”

Keep it simple

There have been a handful of instances where scientists have recorded seismic data suggesting that a quake reversed direction. In 2016, an earthquake in the middle of the Atlantic Ocean rippled eastward, and then seconds later richocheted back west. Similar return rumblers may have occurred in 2011 during the magnitude 9 earthquake in Tohoku, Japan, and in 2023 during the destructive magnitude 7.8 quake in Turkey and Syria, among others.

These events took place in various fault regions, from complex zones of multiple intersecting fault lines to regions with just a single, straight fault. While seismologists have assumed that such complex quakes would be more likely to occur in multifault systems, the rare examples along simple faults got Sun and Cattania wondering: Could an earthquake reverse course along a simple fault? And if so, what could cause such a bounce-back in a seemingly simple system?

“When you see this boomerang-like behavior, it is tempting to explain this in terms of some complexity in the Earth,” Cattania says. “For instance, there may be many faults that interact, with earthquakes jumping between fault segments, or fault surfaces with prominent kinks and bends. In many cases, this could explain back-propagating behavior. But what we found was, you could have a very simple fault and still get this complex behavior.”

Faulty friction

In their new study, the team looked to simulate an earthquake along a simple fault system. In geology, a fault is a crack or fracture that runs through the Earth’s crust. An earthquake begins when the stress between rocks on either side of the fault, suddenly decreases, and one side slides against the other, setting off seismic waves that rupture rocks all along the fault. This seismic activity, which initiates deep in the crust, can sometimes reach and shake up the surface.

Cattania and Sun used a computer model to represent the fundamental physics at play during an earthquake along a simple fault. In their model, they simulated the Earth’s crust as a simple elastic material, in which they embedded a single straight fault. They then simulated how the fault would exhibit an earthquake under different scenarios. For instance, the team varied the length of the fault and the location of the quake’s initation point below the surface, as well as whether the quake traveled in one versus two directions.

Over multiple simulations, they observed that only the unilateral quakes — those that traveled in one direction — exhibited a boomerang effect. Specifically, these quakes seemed to include a type that seismologists term “back-propagating” events, in which the rumbler splits at some point along the fault, partly continuing in the same direction and partly reversing back the way it came.

“When you look at a simulation, sometimes you don’t fully understand what causes a given behavior,” Cattania says. “So we developed mathematical models to understand it. And we went back and forth, to ultimately develop a simple theory that tells you should only see this back-propagation under these certain conditions.”

Those conditions, as the team’s new theory lays out, have to do with the friction along the fault. In standard earthquake physics, it’s generally understood that an earthquake is triggered when the stress built up between rocks on either side of a fault, is suddenly released. Rocks slide against each other in response, decreasing a fault’s friction. The reduction in fault friction creates a positive feedback that facilitates further sliding, sustaining the earthquake.

However, in their simulations, the team observed that when a quake travels along a fault in one direction, it can back-propagate when friction along the fault goes down, then up, and then down again.

“When the quake propagates in one direction, it produces a “breaking’’ effect that reduces the sliding velocity, increases friction, and allows only a narrow section of the fault to slide at a time,” Cattania says. “The region behind the quake, which stops sliding, can then rupture again, because it has accumulated more stress to slide again.”

The team found that, in addition to traveling in one direction and along a fault with changing friction, a boomerang is likely to occur if a quake has traveled over a large enough distance.

“This implies that large earthquakes are not simply ‘scaled-up’ versions of small earthquakes, but instead they have their own unique rupture behavior,” Sun says.

The team suspects that back-propagating quakes may be more common than scientists have thought, and they may occur along simple, straight faults, which are typically older than more complex fault systems.

“You shouldn’t only expect this complex behavior on a young, complex fault system. You can also see it on mature, simple faults,” Cattania says. “The key open question now is how often rupture reversals, or ‘boomerang’ earthquakes, occur in nature. Many observational studies so far have used methods that can’t detect back-propagating fronts. Our work motivates actively looking for them, to further advance our understanding of earthquake physics and ultimately mitigate seismic risk.”

MIT community members elected to the National Academy of Engineering for 2026

Seven faculty members, along with 12 additional alumni, are honored for significant contributions to engineering research, practice, and education.

Seven MIT researchers are among the 130 new members and 28 international members recently elected to the National Academy of Engineering (NAE) for 2026. Twelve additional MIT alumni were also elected as new members.

One of the highest professional distinctions for engineers, membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education,” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education.”

The seven MIT electees this year include:

Moungi Gabriel Bawendi, the Lester Wolfe Professor of Chemistry in the Department of Chemistry, was honored for the synthesis and characterization of semiconductor quantum dots and their applications in displays, photovoltaics, and biology.

Charles Harvey, a professor in the Department of Civil and Environmental Engineering, was honored for contributions to hydrogeology regarding groundwater arsenic contamination, transport, and consequences.

Piotr Indyk, the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory, was honored for contributions to approximate nearest neighbor search, streaming, and sketching algorithms for massive data processing.

John Henry Lienhard, the Abdul Latif Jameel Professor of Water and Mechanical Engineering in the Department of Mechanical Engineering, was honored for advances and technological innovations in desalination.

Ram Sasisekharan, the Alfred H. Caspary Professor of Biological Physics and Physics in the Department of Biological Engineering, was honored for discovering the U.S. heparin contaminant in 2008 and creating clinical antibodies for Zika, dengue, SARS-CoV-2, and other diseases.

Frances Ross, the TDK Professor in the Department of Materials Science and Engineering, was honored for ultra-high vacuum and liquid-cell transmission electron microscopies and their worldwide adoptions for materials research and semiconductor technology development.

Zoltán Sandor Spakovszky SM ’99, PhD ’01, the T. Wilson (1953) Professor in Aeronautics in the Department of Aeronautics and Astronautics, was honored for contributions, through rigorous discoveries and advancements, in aeroengine aerodynamic and aerostructural stability and acoustics.

“Each of the MIT faculty and alumni elected to the National Academy of Engineering has made extraordinary contributions to their fields through research, education, and innovation,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor in the Department of Chemical Engineering. "They represent the breadth of excellence we have here at MIT. This honor reflects the impact of their work, and I’m proud to celebrate their achievement and offer my warmest congratulations.”

Twelve additional alumni were elected to the National Academy of Engineering this year. They are: Anne Hammons Aunins PhD ’91; Lars James Blackmore PhD ’07; John-Paul Clarke ’91, SM ’92, SCD ’97; Michael Fardis SM ’77, SM ’78, PhD ’79; David Hays PhD ’98; Stephen Thomas Kent ’76, EE ’78, ENG ’78, PhD ’81; Randal D. Koster SM ’85, SCD ’88; Fred Mannering PhD ’83; Peyman Milanfar SM ’91, EE ’93, ENG ’93, PhD ’93; Amnon Shashua PhD ’93; Michael Paul Thien SCD ’88; and Terry A. Winograd PhD ’70.

The strength of “infinite hope”

MIT Dean of Engineering Paula Hammond keynotes the 52nd MLK Celebration, with a message of resilience and determination.

Dean of Engineering Paula Hammond ’84 PhD ’93 made a resounding call for the MIT community to “embrace endless hope” and “never stop looking forward,” in a keynote address at the Institute’s annual MLK Celebration on Wednesday, Feb. 11.

“We each have a role to play in contributing to our future, and we each must embrace endless hope and continuously renew our faith in ourselves to accomplish that dream,” Hammond said, to an audience of hundreds at the event.

She added: “Whether it is through caring for those in our community, teaching others, providing inspiration, leadership, or critical support to others in their moment of need, we provide support for one another on our journey … It is that future that will feed the optimism and faith that we need to move forward, to inspire and encourage, and to never stop looking forward.”

The MLK Celebration is an annual tribute to the life and legacy of Martin Luther King Jr., and is always thematically organized around a quotation of King’s. This year, that passage was, “We must accept finite disappointment, but never lose infinite hope.”

Hammond and multiple other speakers at the event organized their remarks around that idea, while weaving in personal reflections about the importance of community, family, and mentorship.

As Hammond noted, “We can lay the path toward a better, greater time with the steps that we take today even in the face of incredible disappointment, shock and disruption.” She added: “Principles founded in fear, ignorance, or injustice ultimately fail because they do not meet the needs of a growing and prosperous nation and world.”

The event, which took place in MIT’s Walker Memorial (Building 50), featured remarks by students, staff, and campus leaders, as well as musical performances by the recently reconstituted MIT Gospel Choir. (Listen to one of those performances by clicking on the player at the end of this article.)

MIT President Sally A. Kornbluth provided introductory remarks, noting that this year’s event was occurring during “a time when feeling fractured, isolated, and pitted against each other feels exhaustingly routine. A time when it’s easy to feel discouraged.” As such, she added, “the solace we take from [coming together at this event] couldn’t be more relevant now.”

Kornbluth also offered laudatory thoughts about Hammond, a highly accomplished research scientist who has held numerous leadership roles at MIT and elsewhere. Hammond, a chemical engineer, was named dean of the MIT School of Engineering in December. Prior to that, she has served as vice provost for faculty, from 2023 to 2025, and head of the Department of Chemical Engineering, from 2015 to 2023. In honor of her accomplishments, Hammond was named an Institute Professor, MIT’s highest faculty honor. A member of MIT’s Koch Institute for Integrative Cancer Research, Hammond has developed polymers and nanoscale materials with multiple applications, including drug delivery, imaging, and even battery advances.

Hammond was awarded the National Medal of Technology and Innovation in 2024. That year she also received MIT’s Killian Award, for faculty achievement. And she has earned the rare distinction of having been elected to all three national academies — the National Academy of Engineering, the National Academy of Medicine, and the National Academy of Sciences.

“I’ve never met anyone who better represents MIT’s highest values and aspirations than Paula Hammond,” Kornbluth said, citing both Hammond’s record of academic excellence and Institute service.

Among other things, Kornbluth observed, “Paula has been a longtime champion of MIT’s culture of openness to people and ideas from everywhere. In fact, it’s hard to think of anyone more open to sharing what she knows — and more interested in hearing your point of view. And the respect she shows to everyone — no matter their job or background — is an example for us all.”

Michael Ewing ’27, a mechanical engineering major, provided welcoming remarks while introducing the speakers as well as the MLK Celebration planning committee.

Ewing noted that the event remains “extremely and vitally important” to the MIT community, and reflected on the meaning of this year’s motif, for individuals and larger communities.

“Dr. King’s hope constitutes the belief that one can make things better, even when current conditions are poor,” Ewing said. “In the face of adversity, we must remain connected to what’s most important, be grateful for both the challenges and the opportunities, and hold on to the long-term belief that no matter what, no matter what, there’s an opportunity for us to learn, grow, and improve.”

The annual MLK Celebration also highlighted further reflections from students and staff on King’s life and legacy and the value of his work.

“Everyone that has fought for a greater good in this world has left the battle without something that they came with,” said Oluwadara Deru, a senior in mechanical engineering and the featured undergraduate speaker. “But what they gained is invaluable.”

Ekua Beneman, a graduate student in chemistry, offered thoughts relating matters of academic achievement, and helping others in a university setting, to the larger themes of the celebration.

“Hope is not pretending disappointment doesn’t exist,” Beneman said. “Hope is choosing to pass forward what was once given to you. At a place like MIT, infinite hope looks like mentorship. It looks like making space. It looks like sharing knowledge instead of guarding or gatekeeping it. If we truly want to honor Dr. King’s legacy, beyond this beautiful celebration today, we do it by choosing community, mentorship, and hope in action.”

Denzil Streete, associate dean and director of the Office of Graduate Education, related the annual theme to everyday life at the Institute, as well as social life everywhere.

“Hope lies in small, often uncelebrated acts,” Streete said. “Showing up. Being present. Responding with patience. Translating complicated processes into next steps. Making one more call. Sending one more email.”

He concluded: “See your daily work as moral work … Every day, through joy and care, we choose infinite hope, for our students, and for one another.”

Reverend Thea Keith-Lucas, chaplain to the Institute and associate dean in the Office of Religious, Spiritual, and Ethical Life, offered both an invocation and a benediction at the event.

The annual celebration includes the Dr. Martin Luther King Jr. Leadership Awards Recipients, given this year to Melissa Smith PhD ’12, Fred Harris, Carissma McGee, Janine Medrano, and Edwin Marrero.

For all the turbulence in the world, Hammond said toward the conclusion of her address, people can continue to make progress in their own communities, and can be intentional about focusing, in part, on the possibilities of progress ahead.

At MIT, Hammond noted, “The commitment of our faculty, students, and staff to continuously learn, to ask deep questions and to apply our knowledge, our perspectives and our insights to the biggest world problems is something that gives me infinite hope and optimism for the future.”

New AI model could cut the costs of developing protein drugs

MIT researchers used a large language model to optimize the genetic sequences of proteins manufactured by yeast, making production more efficient.

Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.

Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii — specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.

The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.

“Having predictive tools that consistently work well is really important to help shorten the time from having an idea to getting it into production. Taking away uncertainty ultimately saves time and money,” says J. Christopher Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering at MIT, a member of the Koch Institute for Integrative Cancer Research, and faculty co-director of the MIT Initiative for New Manufacturing (MIT INM).

Love is the senior author of the new study, which appears this week in the Proceedings of the National Academy of Sciences. Former MIT postdoc Harini Narayanan is the paper’s lead author.

Codon optimization

Yeast such as K. phaffii and Saccharomyces cerevisiae (baker’s yeast) are the workhorses of the biopharmaceutical industry, producing billions of dollars of protein drugs and vaccines every year.

To engineer yeast for industrial protein production, researchers take a gene from another organism, such as the insulin gene, and modify it so that the microbe will produce it in large quantities. This requires coming up with an optimal DNA sequence for the yeast cells, integrating it into the yeast’s genome, devising favorable growth conditions for it, and finally purifying the end product.

For new biologic drugs — large, complex drugs produced by living organisms — this development process might account for 15 to 20 percent of the overall cost of commercializing the drug.

“Today, those steps are all done by very laborious experimental tasks,” Love says. “We have been looking at the question of where could we take some of the concepts that are emerging in machine learning and apply them to make different aspects of the process more reliable and simpler to predict.”

In this study, the researchers wanted to try to optimize the sequence of DNA codons that make up the gene for a protein of interest. There are 20 naturally occurring amino acids, but 64 possible codon sequences, so most of these amino acids can be encoded by more than one codon. Each codon corresponds to a unique transfer RNA (tRNA) molecule, which carries the correct amino acid to the ribosome, where amino acids are strung together into proteins.

Different organisms use each of these codons at different rates, and designers of engineered proteins often optimize the production of their proteins by choosing the codons that occur the most frequently in the host organism. However, this doesn’t necessarily produce the best results. If the same codon is always used to encode arginine, for example, the cell may run low on the tRNA molecules that correspond to that codon.

To take a more nuanced approach, the MIT team deployed a type of large language model known as an encoder-decoder. Instead of analyzing text, the researchers used it to analyze DNA sequences and learn the relationships between codons that are used in specific genes.

Their training data, which came from a publicly available dataset from the National Center for Biotechnology Information, consisted of the amino acid sequences and corresponding DNA sequences for all of the approximately 5,000 proteins naturally produced by K. phaffii.

“The model learns the syntax or the language of how these codons are used,” Love says. “It takes into account how codons are placed next to each other, and also the long-distance relationships between them.”

Once the model was trained, the researchers asked it to optimize the codon sequences of six different proteins, including human growth hormone, human serum albumin, and trastuzumab, a monoclonal antibody used to treat cancer.

They also generated optimized sequences of these proteins using four commercially available codon optimization tools. The researchers inserted each of these sequences into K. phaffii cells and measured how much of the target protein each sequence generated. For five of the six proteins, the sequences from the new MIT model worked the best, and for the sixth, it was the second-best.

“We made sure to cover a variety of different philosophies of doing codon optimization and benchmarked them against our approach,” Narayanan says. “We’ve experimentally compared these approaches and showed that our approach outperforms the others.”

Learning the language of proteins

K. phaffii, formerly known as Pichia pastoris, is used to produce dozens of commercial products, including insulin, hepatitis B vaccines, and a monoclonal antibody used to treat chronic migraines. It is also used in the production of nutrients added to foods, such as hemoglobin.

Researchers in Love’s lab have started using the new model to optimize proteins of interest for K. phaffii, and they have made the code available for other researchers who wish to use it for K. phaffii or other organisms.

The researchers also tested this approach on datasets from different organisms, including humans and cows. Each of the resulting models generated different predictions, suggesting that species-specific models are needed to optimize codons of target proteins.

By looking into the inner workings of the model, the researchers found that it appeared to learn some of the biological principles of how the genome works, including things that the researchers did not teach it. For example, it learned not to include negative repeat elements — DNA sequences that can inhibit the expression of nearby genes. The model also learned to categorize amino acids based on traits such as hydrophobicity and hydrophilicity.

“Not only was it learning this language, but it was also contextualizing it through aspects of biophysical and biochemical features, which gives us additional confidence that it is learning something that’s actually meaningful and not simply an optimization of the task that we gave it,” Love says.

The research was funded by the Daniel I.C. Wang Faculty Research Innovation Fund at MIT, the MIT AltHost Research Consortium, the Mazumdar-Shaw International Oncology Fellowship, and the Koch Institute.