The MIT School of Engineering welcomes new faculty members across six of its academic units. This new cohort of faculty members, who have recently started their roles at MIT, conduct research across a diverse range of disciplines.
“We are thrilled to welcome these accomplished scholars to the School of Engineering,” says Maria C. Yang, interim dean of engineering and William E. Leonhard (1940) Professor in the Department of Mechanical Engineering. “Each brings unique expertise across a wide range of fields and is advancing knowledge with real-world impact. They all share a deep commitment to research excellence and a passion for teaching and mentorship.”
Faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS) report into both the School of Engineering and the MIT Stephen A. Schwarzman College of Computing.
The new engineering faculty include:
Masha Folk joined the Department of Aeronautics and Astronautics as an assistant professor in July 2024 and is currently the Charles Stark Draper Career Development Professor. Her research focuses on sustainable aerospace technology driven by a deep desire to accelerate carbon-neutral aviation. She previously worked as an aerodynamics specialist for Rolls-Royce. Folk received her BS in aerospace engineering from Ohio State University, her MS in aerospace engineering from Purdue University, and her PhD in energy, fluids, and turbomachinery from the University of Cambridge.
Sophia Henneberg joined the Department of Nuclear Science and Engineering (NSE) as an assistant professor in September. Her research focuses on developing, utilizing, and extending optimization tools to identify new, promising stellarator designs, which are a promising path toward fusion energy. Previously, she was the principal investigator of EUROfusion’s Stellarator Optimization Theory, Simulation, Validation, and Verification group. Henneberg received a BS in physics at the Goethe-Universität, an MA in physics at the University of Wisconsin at Madison, and a PhD in physics at the University of York.
Omar Khattab joined the Department of Electrical Engineering and Computer Science as an assistant professor in July. He is also affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research develops new algorithms and abstractions for declarative AI programming and for composing retrieval and reasoning. Khattab previously worked as a research scientist at Databricks. He received a BS in computer science from Carnegie Mellon University and a PhD in computer science from Stanford University.
Tania Lopez-Silva joined the Department of Materials Science and Engineering as an assistant professor in July. Her research focuses on supramolecular hydrogels — soft materials made from self-assembling molecules, primarily peptides. Previously, she served as a postdoc at the National Cancer Institute. Lopez-Silva earned her BS in chemistry from Tecnológico de Monterrey and her MA and PhD in chemistry from Rice University.
Ethan Peterson ’13 joined the Department of Nuclear Science and Engineering as an assistant professor in July 2024. His research focuses on improving radiation transport and transmutation methods for the design of fusion technologies, as well as whole-facility modeling for fusion power plants. Previously, he worked as a research scientist at MIT’s Plasma Science and Fusion Center. Peterson received his BS in nuclear engineering and physics from MIT and his PhD in plasma physics from the University of Wisconsin at Madison.
Dean Price joined the Department of Nuclear Science and Engineering as the Atlantic Richfield Career Development Professor in Energy Studies and an assistant professor in September. His work focuses on the simulation and control of advanced reactors, with expertise in uncertainty quantification, scientific machine learning, and artificial intelligence for nuclear applications. Previously, he was the Russell L. Heath Distinguished Postdoctoral Fellow at Idaho National Laboratory. He earned his BS in nuclear engineering from the University of Illinois and his PhD in nuclear engineering from the University of Michigan.
Daniel Varon joined the Department of Aeronautics and Astronautics as the Boeing Assistant Professor, holding an MIT Schwarzman College of Computing shared position with IDSS, in July. Varon’s research focuses on using satellite observations of atmospheric composition to better understand human impacts on the environment and identify opportunities to reduce them. Previously, he held a visiting postdoctoral fellowship at the Princeton School of Public and International Affairs. Varon earned a BS in physics and a BA in English literature from McGill University, and an MS in applied mathematics and PhD in atmospheric chemistry from Harvard University.
Raphael Zufferey joined the Department of Mechanical Engineering as an assistant professor in January. He studies bioinspired methods and unconventional designs to solve seamless aerial and aquatic locomotion for applications in ocean sciences. Zufferey previously worked as a Marie Curie postdoc at the École Polytechnique Fédérale de Lausanne (EPFL). He received his BA in micro-engineering and MS in robotics from EPFL and a PhD in robotics and aeronautics from Imperial College London.
The School of Engineering is also welcoming a number of faculty in the Department of EECS and the IDSS who hold shared positions with the MIT Schwarzman College of Computing and other departments. These include: Bailey Flanigan, Brian Hedden, Yunha Hwang, Benjamin Lindquist, Paris Smaragdis, Pu “Paul" Liang, Mariana Popescu, and Daniel Varon. For more information about these faculty members, read the Schwarzman College of Computing’s recent article.
Additionally, the School of Engineering has adopted the shared faculty search model to hire its first shared faculty member: Mark Rau. For more information, read the School of Humanities, Arts, and Social Sciences recent article.
MIT Schwarzman College of Computing welcomes 11 new faculty for 2025The faculty members occupy core computing and shared positions, bringing varied backgrounds and expertise to the MIT community.The MIT Schwarzman College of Computing welcomes 11 new faculty members in core computing and shared positions to the MIT community. They bring varied backgrounds and expertise spanning sustainable design, satellite remote sensing, decision theory, and the development of new algorithms for declarative artificial intelligence programming, among others.
“I warmly welcome this talented group of new faculty members. Their work lies at the forefront of computing and its broader impact in the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.
College faculty include those with appointments in the Department of Electrical Engineering and Computer Science (EECS) or in the Institute for Data, Systems, and Society (IDSS), which report into both the MIT Schwarzman College of Computing and the School of Engineering. There are also several new faculty members in shared positions between the college and other MIT departments and sections, including Political Science, Linguistics and Philosophy, History, and Architecture.
“Thanks to another successful year of collaborative searches, we have hired six additional faculty in shared positions, bringing the total to 20,” says Huttenlocher.
The new shared faculty include:
Bailey Flanigan is an assistant professor in the Department of Political Science, holding an MIT Schwarzman College of Computing shared position with EECS. Her research combines tools from social choice theory, game theory, algorithms, statistics, and survey methods to advance political methodology and strengthen democratic participation. She is interested in sampling algorithms, opinion measurement, and the design of democratic innovations like deliberative minipublics and participatory budgeting. Flanigan was a postdoc at Harvard University’s Data Science Initiative, and she earned her PhD in computer science from Carnegie Mellon University.
Brian Hedden PhD ’12 is a professor in the Department of Linguistics and Philosophy, holding an MIT Schwarzman College of Computing shared position with EECS. His research focuses on how we ought to form beliefs and make decisions. His works span epistemology, decision theory, and ethics, including ethics of AI. He is the author of “Reasons without Persons: Rationality, Identity, and Time” (Oxford University Press, 2015) and articles on topics such as collective action problems, legal standards of proof, algorithmic fairness, and political polarization. Prior to joining MIT, he was a faculty member at the Australian National University and the University of Sydney, and a junior research fellow at Oxford University. He received his BA from Princeton University and his PhD from MIT, both in philosophy.
Yunha Hwang is an assistant professor in the Department of Biology, holding an MIT Schwarzman College of Computing shared position with EECS. She is also a member of the Laboratory for Information and Decision Systems. Her research interests span machine learning for sustainable biomanufacturing, microbial evolution, and open science. She serves as the co-founder and chief scientist at Tatta Bio, a scientific nonprofit dedicated to advancing genomic AI for biological discovery. She holds a BS in computer science from Stanford University and a PhD in biology from Harvard University.
Ben Lindquist is an assistant professor in the History Section, holding an MIT Schwarzman College of Computing shared position with EECS. Through a historical lens, his work observes the ways that computing has circulated with ideas of religion, emotion, and divergent thinking. His book, “The Feeling Machine” (University of Chicago Press, forthcoming), follows the history of synthetic speech to examine how emotion became a subject of computer science. He was a postdoc in the Science in Human Culture Program at Northwestern University and earned his PhD in history from Princeton University.
Mariana Popescu is an assistant professor in the Department of Architecture, holding an MIT Schwarzman College of Computing shared position with EECS. She is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). A computational architect and structural designer, Popescu has a strong interest and experience in innovative ways of approaching the fabrication process and use of materials in construction. Her area of expertise is computational and parametric design, with a focus on digital fabrication and sustainable design. Popescu earned her doctorate at ETH Zurich.
Paris Smaragdis SM ’97, PhD ’01 is a professor in the Music and Theater Arts Section, holding an MIT Schwarzman College of Computing shared position with EECS. His research focus lies at the intersection of signal processing and machine learning, especially as it relates to sound and music. Prior to coming to MIT, he worked as a research scientist at Mitsubishi Electric Research Labs, a senior research scientist at Adobe Research, and an Amazon Scholar with Amazon’s AWS. He spent 15 years as a professor at the University of Illinois Urbana Champaign in the Computer Science Department, where he spearheaded the design of the CS+Music program, and served as an associate director of the School of Computer and Data Science. He holds a BMus from Berklee College of Music and earned his PhD in perceptual computing from MIT.
Daniel Varon is an assistant professor in the Department of Aeronautics and Astronautics, holding an MIT Schwarzman College of Computing shared position with IDSS. His work focuses on using satellite observations of atmospheric composition to better understand human impacts on the environment and identify opportunities to reduce them. An atmospheric scientist, Varon is particularly interested in greenhouse gasses, air pollution, and satellite remote sensing. He holds an MS in applied mathematics and a PhD in atmospheric chemistry, both from Harvard University.
In addition, the School of Engineering has adopted the shared faculty search model to hire its first shared faculty member:
Mark Rau is an assistant professor in the Music and Theater Arts Section, holding a School of Engineering shared position with EECS. He is involved in developing graduate programming focused on music technology. He has an interest in musical acoustics, vibration and acoustic measurement, audio signal processing, and physical modeling synthesis. His work focuses on musical instruments and creative audio effects. He holds an MA in music, science, and technology from Stanford, as well as a BS in physics and BMus in jazz from McGill University. He earned his PhD at Stanford’s Center for Computer Research in Music and Acoustics.
The new core faculty are:
Mitchell Gordon is an assistant professor in EECS. He is also a member of CSAIL. In his research, Gordon designs interactive systems and evaluation approaches that bridge principles of human-computer interaction with the realities of machine learning. His work has won awards at conferences in human-computer interaction and artificial intelligence, including a best paper award at CHI and an Oral at NeurIPS. Gordon received a BS from the University of Rochester, and MS and PhD from Stanford University, all in computer science.
Omar Khattab is an assistant professor in EECS. He is also a member of CSAIL. His work focuses on natural language processing, information retrieval, and AI systems. His research includes developing new algorithms and abstractions for declarative AI programming and for composing retrieval and reasoning. He received his BS from Carnegie Mellon University and his PhD from Stanford University, both in computer science.
Rachit Nigam will join EECS as an assistant professor in January 2026. He will also be a member of CSAIL and the Microsystems Technology Laboratories. He works on programming languages and computer architecture to address the design, verification, and usability challenges of specialized hardware. He was previously a visiting scholar at MIT. Nigam earned an MS and PhD in computer science from Cornell University.
Lincoln Laboratory and Haystack Observatory team up to unveil hidden parts of the galaxyA proposed telescope made of thousands of tiny, identical satellites will work together to reveal low-frequency radio waves in space.For centuries, humans have sought to study the stars and celestial bodies, whether through observations made by naked eye or by telescopes on the ground and in space that can view the universe across nearly the entire electromagnetic spectrum. Each view unlocks new information about the denizens of space — X-ray pulsars, gamma-ray bursts — but one is still missing: the low-frequency radio sky.
Researchers from MIT Lincoln Laboratory, the MIT Haystack Observatory, and Lowell Observatory are working on a NASA-funded concept study called the Great Observatory for Long Wavelengths, or GO-LoW, that outlines a method to view the universe at as-of-yet unseen low frequencies using a constellation of thousands of small satellites. The wavelengths of these frequencies are 15 meters to several kilometers in length, which means they require a very big telescope in order to see clearly.
"GO-LoW will be a new kind of telescope, made up of many thousands of spacecraft that work together semi-autonomously, with limited input from Earth," says Mary Knapp, the principal investigator for GO-LoW at the MIT Haystack Observatory. "GO-LoW will allow humans to see the universe in a new light, opening up one of the very last frontiers in the electromagnetic spectrum."
The difficulty in viewing the low-frequency radio sky comes from Earth's ionosphere, a layer of the atmosphere that contains charged particles that prevent very low-frequency radio waves from passing through. Therefore, a space-based instrument is required to observe these wavelengths. Another challenge is that long-wavelength observations require correspondingly large telescopes, which would need to be many kilometers in length if built using traditional dish antenna designs. GO-LoW will use interferometry — a technique that combines signals from many spatially separated receivers that, when put together, will function as one large telescope — to obtain highly detailed data from exoplanets and other sources in space. A similar technique was used to make the first image of a black hole and, more recently, an image of the first known extrasolar radiation belts.
Melodie Kao, a member of the team from Lowell Observatory, says the data could reveal details about an exoplanet's makeup and potential for life. "[The radio wave aurora around an exoplanet] carries important information, such as whether or not the planet has a magnetic field, how strong it is, how fast the planet is rotating, and even hints about what's inside," she says. "Studying exoplanet radio aurorae and the magnetic fields that they trace is an important piece of the habitability puzzle, and it's a key science goal for GO-LoW."
Several recent trends and technology developments will make GO-LoW possible in the near future, such as the declining cost of mass-produced small satellites, the rise of mega-constellations, and the return of large, high-capacity launch vehicles like NASA's Space Launch System. Go-LoW would be the first mega-constellation that uses interferometry for scientific purposes.
The GO-LoW constellation will be built through several successive launches, each containing thousands of spacecraft. Once they reach low-Earth orbit, the spacecraft will be refueled before journeying on to their final destination — an Earth-sun Lagrange point where they will then be deployed. Lagrange points are regions in space where the gravitational forces of two large celestial bodies (like the sun and Earth) are in equilibrium, such that a spacecraft requires minimal fuel to maintain its position relative to the two larger bodies. At this long distance from Earth (1 astronomical unit, or approximately 93 million miles), there will also be much less radio-frequency interference that would otherwise obscure GO-LoW’s sensitive measurements.
"GO-LoW will have a hierarchical architecture consisting of thousands of small listener nodes and a smaller number of larger communication and computation nodes (CCNs)," says Kat Kononov, a team member from Lincoln Laboratory's Applied Space Systems Group, who has been working with MIT Haystack staff since 2020, with Knapp serving as her mentor during graduate school. A node refers to an individual small satellite within the constellation. "The listener nodes are small, relatively simple 3U CubeSats — about the size of a loaf of bread — that collect data with their low-frequency antennas, store it in memory, and periodically send it to their communication and computation node via a radio link." In comparison, the CCNs are about the size of a mini-fridge.
The CCN will keep track of the positions of the listener nodes in their neighborhood; collect and reduce the data from their respective listener nodes (around 100 of them); and then transmit that data back to Earth, where more intensive data processing can be performed.
At full strength, with approximately 100,000 listener nodes, the GO-LoW constellation should be able to see exoplanets with magnetic fields in the solar neighborhood — within 5 to 10 parsecs — many for the very first time.
The GO-LoW research team recently published the results of their findings from Phase I of the study, which identified a type of advanced antenna called a vector sensor as the best type for this application. In 2024, Lincoln Laboratory designed a compact deployable version of the sensor suitable for use in space.
The team is now working on Phase II of the program, which is to build a multi-agent simulation of constellation operations.
"What we learned during the Phase I study is that the hard part for GO-LoW is not any specific technology … the hard part is the system: the system engineering and the autonomy to run the system," says Knapp. "So, how do we build this constellation such that it's a tractable problem? That's what we’re exploring in this next part of the study."
GO-LoW is one of many civil space programs at Lincoln Laboratory that aim to harness advanced technologies originally developed for national security to enable new space missions that support science and society. "By adapting these capabilities to serve new stakeholders, the laboratory helps open novel frontiers of discovery while building resilient, cost-effective systems that benefit the nation and the world," says Laura Kennedy, who is the deputy lead of Lincoln Laboratory's Civil Space Systems and Technology Office.
"Like landing on the moon in 1969, or launching Hubble in the 1990s, GO-LoW is envisioned to let us see something we've never seen before and generate scientific breakthroughs," says Kononov.
Go-LoW is a collaboration between Lincoln Laboratory, Haystack Observatory, and Lowell University, as well as Lenny Paritsky from LeafLabs and Jacob Turner from Cornell University.
New software designs eco-friendly clothing that can reassemble into new items To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.It’s hard to keep up with the ever-changing trends of the fashion world. What’s “in” one minute is often out of style the next season, potentially causing you to re-evaluate your wardrobe.
Staying current with the latest fashion styles can be wasteful and expensive, though. Roughly 92 million tons of textile waste are produced annually, including the clothes we discard when they go out of style or no longer fit. But what if we could simply reassemble our clothes into whatever outfits we wanted, adapting to trends and the ways our bodies change?
A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe are attempting to bring eco-friendly, versatile garments to life. Their new “Refashion” software system breaks down fashion design into modules — essentially, smaller building blocks — by allowing users to draw, plan, and visualize each element of a clothing item. The tool turns fashion ideas into a blueprint that outlines how to assemble each component into reconfigurable clothing, such as a pair of pants that can be transformed into a dress.
With Refashion, users simply draw shapes and place them together to develop an outline for adaptable fashion pieces. It’s a visual diagram that shows how to cut garments, providing a straightforward way to design things like a shirt with an attachable hood for rainy days. One could also create a skirt that can then be reconfigured into a dress for a formal dinner, or maternity wear that fits during different stages of pregnancy.
“We wanted to create garments that consider reuse from the start,” says Rebecca Lin, MIT Department of Electrical Engineering and Computer Science (EECS) PhD student, CSAIL and Media Lab researcher, and lead author on a paper presenting the project. “Most clothes you buy today are static, and are discarded when you no longer want them. Refashion instead makes the most of our garments by helping us design items that can be easily resized, repaired, or restyled into different outfits.”
Modules à la mode
The researchers conducted a preliminary user study where both designers and novices explored Refashion and were able to create garment prototypes. Participants assembled pieces such as an asymmetric top that could be extended into a jumpsuit, or remade into a formal dress, often within 30 minutes. These results suggest that Refashion has the potential to make prototyping garments more approachable and efficient. But what features might contribute to this ease of use?
Its interface first presents a simple grid in its “Pattern Editor” mode, where users can connect dots to outline the boundaries of a clothing item. It’s essentially drawing rectangular panels and specifying how different modules will connect to each other.
Users can customize the shape of each component, create a straight design for garments (which might be useful for less form-fitting items, like chinos) or perhaps tinkering with one of Refashion’s templates. A user can edit pre-designed blueprints for things like a T-shirt, fitted blouse, or trousers.
Another, more creative route is to change the design of individual modules. One can choose the “pleat” feature to fold a garment over itself, similar to an accordion, for starters. It’s a useful way to design something like a maxi dress. The “gather” option adds an artsy flourish, where a garment is crumpled together to create puffy skirts or sleeves. A user might even go with the “dart” module, which removes a triangular piece from the fabric. It allows for shaping a garment at the waist (perhaps for a pencil skirt) or tailor to the upper body (fitted shirts, for instance).
While it might seem that each of these components needs to be sewn together, Refashion enables users to connect garments through more flexible, efficient means. Edges can be seamed together via double-sided connectors such as metal snaps (like the buttons used to close a denim jacket) or Velcro dots. A user could also fasten them in pins called brads, which have a pointed side that they stick through a hole and split into two “legs” to attach to another surface; it’s a handy way to secure, say, a picture on a poster board. Both connective methods make it easy to reconfigure modules, should they be damaged or a “fit check” calls for a new look.
As a user designs their clothing piece, the system automatically creates a simplified diagram of how it can be assembled. The pattern is divided into numbered blocks, which is dragged onto different parts of a 2D mannequin to specify the position of each component. The user can then simulate how their sustainable clothing will look on 3D models of a range of body types (one can also upload a model).
Finally, a digital blueprint for sustainable clothing can extend, shorten, or combine with other pieces. Thanks to Refashion, a new piece could be emblematic of a potential shift in fashion: Instead of buying new clothes every time we want a new outfit, we can simply reconfigure existing ones. Yesterday’s scarf could be today’s hat, and today’s T-shirt could be tomorrow’s jacket.
“Rebecca’s work is at an exciting intersection between computation and art, craft, and design,” says MIT EECS professor and CSAIL principal investigator Erik Demaine, who advises Lin. “I’m excited to see how Refashion can make custom fashion design accessible to the wearer, while also making clothes more reusable and sustainable.”
Constant change
While Refashion presents a greener vision for the future of fashion, the researchers note that they’re actively improving the system. They intend to revise the interface to support more durable items, stepping beyond standard prototyping fabrics. Refashion may soon support other modules, like curved panels, as well. The CSAIL-Adobe team may also evaluate whether their system can use as few materials as possible to minimize waste, and whether it can help “remix” old store-bought outfits.
Lin also plans to develop new computational tools that help designers create unique, personalized outfits using colors and textures. She’s exploring how to design clothing by patchwork — essentially, cutting out small pieces from materials like decorative fabrics, recycled denim, and crochet blocks and assembling them into a larger item.
“This is a great example of how computer-aided design can also be key in supporting more sustainable practices in the fashion industry,” says Adrien Bousseau, a senior researcher at Inria Centre at Université Côte d'Azur who wasn’t involved in the paper. “By promoting garment alteration from the ground up, they developed a novel design interface and accompanying optimization algorithm that helps designers create garments that can undergo a longer lifetime through reconfiguration. While sustainability often imposes additional constraints on industrial production, I am confident that research like the one by Lin and her colleagues will empower designers in innovating despite these constraints.”
Lin wrote the paper with Adobe Research scientists Michal Lukáč and Mackenzie Leake, who is the paper’s senior author and a former CSAIL postdoc. Their work was supported, in part, by the MIT Morningside Academy for Design, an MIT MAKE Design-2-Making Mini-Grant, and the Natural Sciences and Engineering Research Council of Canada. The researchers presented their work recently at the ACM Symposium on User Interface Software and Technology.
Before cells can divide, they first need to replicate all of their chromosomes, so that each of the daughter cells can receive a full set of genetic material. Until now, scientists had believed that as division occurs, the genome loses the distinctive 3D internal structure that it typically forms.
Once division is complete, it was thought, the genome gradually regains that complex, globular structure, which plays an essential role in controlling which genes are turned on in a given cell.
However, a new study from MIT shows that in fact, this picture is not fully accurate. Using a higher-resolution genome mapping technique, the research team discovered that small 3D loops connecting regulatory elements and genes persist in the genome during cell division, or mitosis.
“This study really helps to clarify how we should think about mitosis. In the past, mitosis was thought of as a blank slate, with no transcription and no structure related to gene activity. And we now know that that’s not quite the case,” says Anders Sejr Hansen, an associate professor of biological engineering at MIT. “What we see is that there’s always structure. It never goes away.”
The researchers also discovered that these regulatory loops appear to strengthen when chromosomes become more compact in preparation for cell division. This compaction brings genetic regulatory elements closer together and encourages them to stick together. This may help cells “remember” interactions present in one cell cycle and carry it to the next one.
“The findings help to bridge the structure of the genome to its function in managing how genes are turned on and off, which has been an outstanding challenge in the field for decades,” says Viraat Goel PhD ’25, the lead author of the study.
Hansen and Edward Banigan, a research scientist in MIT’s Institute for Medical Engineering and Science, are the senior authors of the paper, which appears today in Nature Structural and Molecular Biology. Leonid Mirny, a professor in MIT’s Institute for Medical Engineering and Science and the Department of Physics, and Gerd Blobel, a professor at the Perelman School of Medicine at the University of Pennsylvania, are also authors of the study.
A surprising finding
Over the past 20 years, scientists have discovered that inside the cell nucleus, DNA organizes itself into 3D loops. While many loops enable interactions between genes and regulatory regions that may be millions of base pairs away from each other, others are formed during cell division to compact chromosomes. Much of the mapping of these 3D structures has been done using a technique called Hi-C, originally developed by a team that included MIT researchers and was led by Job Dekker at the University of Massachusetts Chan Medical School. To perform Hi-C, researchers use enzymes to chop the genome into many small pieces and biochemically link pieces that are near each other in 3D space within the cell’s nucleus. They then determine the identities of the interacting pieces by sequencing them.
However, that technique doesn’t have high enough resolution to pick out all specific interactions between genes and regulatory elements such as enhancers. Enhancers are short sequences of DNA that can help to activate the transcription of a gene by binding to the gene’s promoter — the site where transcription begins.
In 2023, Hansen and others developed a new technique that allows them to analyze 3D genome structures with 100 to 1,000 times greater resolution than was previously possible. This technique, known as Region-Capture Micro-C (RC-MC), uses a different enzyme that cuts the genome into small fragments of similar size. It also focuses on a smaller segment of the genome, allowing for high-resolution 3-D mapping of a targeted genome region.
Using this technique, the researchers were able to identify a new kind of genome structure that hadn’t been seen before, which they called “microcompartments.” These are tiny highly connected loops that form when enhancers and promoters located near each other stick together.
In that paper, experiments revealed that these loops were not formed by the same mechanisms that form other genome structures, but the researchers were unable to determine exactly how they do form. In hopes of answering that question, the team set out to study cells as they undergo cell division. During mitosis, chromosomes become much more compact, so that they can be duplicated, sorted, and divvied up between two daughter cells. As this happens, larger genome structures called A/B compartments and topologically associating domains (TADs) disappear completely.
The researchers believed that the microcompartments they had discovered would also disappear during mitosis. By tracking cells through the entire cell division process, they hoped to learn how the microcompartments appear after mitosis is completed.
“During mitosis, it has been thought that almost all gene transcription is shut off. And before our paper, it was also thought that all 3D structure related to gene regulation was lost and replaced by compaction. It’s a complete reset every cell cycle,” Hansen says.
However, to their surprise, the researchers found that microcompartments could still be seen during mitosis, and in fact they become more prominent as the cell goes through cell division.
“We went into this study thinking, well, the one thing we know for sure is that there’s no regulatory structure in mitosis, and then we accidentally found structure in mitosis,” Hansen says.
Using their technique, the researchers also confirmed that larger structures such as A/B compartments and TADs do disappear during mitosis, as had been seen before.
“This study leverages the unprecedented genomic resolution of the RC-MC assay to reveal new and surprising aspects of mitotic chromatin organization, which we have overlooked in the past using traditional 3C-based assays. The authors reveal that, contrary to the well-described dramatic loss of TADs and compartmentalization during mitosis, fine-scale “microcompartments” — nested interactions between active regulatory elements — are maintained or even transiently strengthened,” says Effie Apostolou, an associate professor of molecular biology in medicine at Weill Cornell Medicine, who was not involved in the study.
A spike in transcription
The findings may offer an explanation for a spike in gene transcription that usually occurs near the end of mitosis, the researchers say. Since the 1960s, it had been thought that transcription ceased completely during mitosis, but in 2016 and 2017, a few studies showed that cells undergo a brief spike of transcription, which is quickly suppressed until the cell finishes dividing.
In their new study, the MIT team found that during mitosis, microcompartments are more likely to be found near the genes that spike during cell division. They also discovered that these loops appear to form as a result of the genome compaction that occurs during mitosis. This compaction brings enhancers and promoters closer together, allowing them to stick together to form microcompartments.
Once formed, the loops that constitute microcompartments may activate gene transcription somewhat by accident, which is then shut off by the cell. When the cell finishes dividing, entering a state known as G1, many of these small loops become weaker or disappear.
“It almost seems like this transcriptional spiking in mitosis is an undesirable accident that arises from generating a uniquely favorable environment for microcompartments to form during mitosis,” Hansen says. “Then, the cell quickly prunes and filters many of those loops out when it enters G1.”
Because chromosome compaction can also be influenced by a cell’s size and shape, the researchers are now exploring how variations in those features affect the structure of the genome and in turn, gene regulation.
“We are thinking about some natural biological settings where cells change shape and size, and whether we can perhaps explain some 3D genome changes that previously lack an explanation,” Hansen says. “Another key question is how does the cell then pick what are the microcompartments to keep and what are the microcompartments to remove when you enter G1, to ensure fidelity of gene expression?”
The research was funded in part by the National Institutes of Health, a National Science Foundation CAREER Award, the Gene Regulation Observatory of the Broad Institute, a Pew-Steward Scholar Award for Cancer Research, the Mathers Foundation, the MIT Westaway Fund, the Bridge Project of the Koch Institute and Dana-Farber/Harvard Cancer Center, and the Koch Institute Support (core) Grant from the National Cancer Institute.
Book reviews technologies aiming to remove carbon from the atmosphereIn “Carbon Removal,” Howard Herzog and Niall MacDowell assess proposed methods of removing carbon already in the atmosphere as a means of mitigating climate change.Two leading experts in the field of carbon capture and sequestration (CCS) — Howard J. Herzog, a senior research engineer in the MIT Energy Initiative, and Niall Mac Dowell, a professor in energy systems engineering at Imperial College London — explore methods for removing carbon dioxide already in the atmosphere in their new book, “Carbon Removal.” Published in October, the book is part of the Essential Knowledge series from the MIT Press, which consists of volumes “synthesizing specialized subject matter for nonspecialists” and includes Herzog’s 2018 book, “Carbon Capture.”
Burning fossil fuels, as well as other human activities, cause the release of carbon dioxide (CO2) into the atmosphere, where it acts like a blanket that warms the Earth, resulting in climate change. Much attention has focused on mitigation technologies that reduce emissions, but in their book, Herzog and Mac Dowell have turned their attention to “carbon dioxide removal” (CDR), an approach that removes carbon already present in the atmosphere.
In this new volume, the authors explain how CO2 naturally moves into and out of the atmosphere and present a brief history of carbon removal as a concept for dealing with climate change. They also describe the full range of “pathways” that have been proposed for removing CO2 from the atmosphere. Those pathways include engineered systems designed for “direct air capture” (DAC), as well as various “nature-based” approaches that call for planting trees or taking steps to enhance removal by biomass or the oceans. The book offers easily accessible explanations of the fundamental science and engineering behind each approach.
The authors compare the “quality” of the different pathways based on the following metrics:
Accounting. For public acceptance of any carbon-removal strategy, the authors note, the developers need to get the accounting right — and that’s not always easy. “If you’re going to spend money to get CO2 out of the atmosphere, you want to get paid for doing it,” notes Herzog. It can be tricky to measure how much you have removed, because there’s a lot of CO2 going in and out of the atmosphere all the time. Also, if your approach involves, say, burning fossil fuels, you must subtract the amount of CO2 that’s emitted from the total amount you claim to have removed. Then there’s the timing of the removal. With a DAC device, the removal happens right now, and the removed CO2 can be measured. “But if I plant a tree, it’s going to remove CO2 for decades. Is that equivalent to removing it right now?” Herzog queries. How to take that factor into account hasn’t yet been resolved.
Permanence. Different approaches keep the CO2 out of the atmosphere for different durations of time. How long is long enough? As the authors explain, this is one of the biggest issues, especially with nature-based solutions, where events such as wildfires or pestilence or land-use changes can release the stored CO2 back into the atmosphere. How do we deal with that?
Cost. Cost is another key factor. Using a DAC device to remove CO2 costs far more than planting trees, but it yields immediate removal of a measurable amount of CO2 that can then be locked away forever. How does one monetize that trade-off?
Additionality. “You’re doing this project, but would what you’re doing have been done anyway?” asks Herzog. “Is your effort additional to business as usual?” This question comes into play with many of the nature-based approaches involving trees, soils, and so on.
Permitting and governance. These issues are especially important — and complicated — with approaches that involve doing things in the ocean. In addition, Herzog points out that some CCS projects could also achieve carbon removal, but they would have a hard time getting permits to build the pipelines and other needed infrastructure.
The authors conclude that none of the CDR strategies now being proposed is a clear winner on all the metrics. However, they stress that carbon removal has the potential to play an important role in meeting our climate change goals — not by replacing our emissions-reduction efforts, but rather by supplementing them. However, as Herzog and Mac Dowell make clear in their book, many challenges must be addressed to move CDR from today’s speculation to deployment at scale, and the book supports the wider discussion about how to move forward. Indeed, the authors have fulfilled their stated goal: “to provide an objective analysis of the opportunities and challenges for CDR and to separate myth from reality.”
Breaking the old model of education with MIT Open LearningFree MIT study materials enabled 16-year-old Vivan Mirchandani’s nontraditional learning path, opening up scientific research and academic opportunities.At an age when many kids prefer to play games on their phones, 11-year-old Vivan Mirchandani wanted to explore physics videos. Little did he know that MIT Open Learning’s free online resources would change the course of his life.
Now, at 16, Mirchandani is well on his way to a career as a physics scholar — all because he forged his own unconventional educational journey.
Nontraditional education has granted Mirchandani the freedom to pursue topics he’s personally interested in. This year, he wrote a paper on cosmology that proposes a new framework for understanding Einstein’s general theory of relativity. Other projects include expanding on fluid dynamics laws for cats, training an AI model to resemble the consciousness of his late grandmother, and creating his own digital twin. That’s in addition to his regular studies, regional science fairs, Model United Nations delegation, and a TEDEd Talk.
Mirchandani started down this path between the ages of 10 and 12, when he decided to read books and find online content about physics during the early Covid-19 lockdown in India. He was shocked to find that MIT Open Learning offers free course videos, lecture notes, exams, and other resources from the Institute on sites like MIT OpenCourseWare and the newly launched MIT Learn.
“My first course was 8.01 (Classical Mechanics), and it completely changed how I saw physics,” Mirchandani says. “Physics sounded like elegance. It’s the closest we’ve ever come to have a theory of everything.”
Experiencing “real learning”
Mirchandani discovered MIT Open Learning through OpenCourseWare, which offers free, online, open educational resources from MIT undergraduate and graduate courses. He says MIT Open Learning’s “academically rigorous” content prepares learners to ask questions and think like a scientist.
“Instead of rote memorization, I finally experienced real learning,” Mirchandani says. “OpenCourseWare was a holy grail. Without it, I would still be stuck on the basic concepts.”
Wanting to follow in the footsteps of physicists like Sir Isaac Newton, Albert Einstein, and Stephen Hawking, Mirchandani decided at age 12 he would sacrifice his grade point average to pursue a nontraditional educational path that gave him hands-on experience in science.
“The education system doesn’t prepare you for actual scientific research, it prepares you for exams,” Mirchandani says. “What draws me to MIT Open Learning and OpenCourseWare is it breaks the old model of education. It’s not about sitting in a lecture hall, it’s about access and experimentation.”
With guidance from his physics teacher, Mirchandani built his own curriculum using educational materials on MIT OpenCourseWare to progress from classical physics to computer science to quantum physics. He has completed more than 27 online MIT courses to date.
“The best part of OpenCourseWare is you get to study from the greatest institution in the world, and you don’t have to pay for it,” he says.
Innovating in the real world
6.0001 (Introduction to Computer Science and Programming Using Python) and slides from 2.06 (Fluid Dynamics) gave Mirchandani the foundation to help with the family business, Dynamech Engineers, which sells machinery for commercial snack production. Some of the recent innovations he has assisted with include a zero-oil frying technology that cuts 300 calories per kilogram, a gas-based heat exchange system, and a simplified, singular machine combining the processes of two separate machines. Using the modeling techniques he learned through MIT OpenCourseWare, Mirchandani designed how these products would work without losing efficiency.
But when you ask Mirchandani which achievement he is most proud of, he’ll say it’s being one of 35 students accepted for the inaugural RSI-India cohort, an academic program for high school students modeled after the Research Science Institute program co-sponsored by MIT and the Center for Excellence in Education. Competing against other Indian students who had perfect scores on their board exams and SATs, he didn’t expect to get in, but the program valued the practical research experience he was able to pursue thanks to the knowledge he gained from his external studies.
“None of it would have happened without MIT OpenCourseWare,” he says. “It’s basically letting curiosity get the better of us. If everybody does that, we’d have a better scientific community.”
Method teaches generative AI models to locate personalized objectsAfter being trained with this technique, vision-language models can better identify a unique item in a new scene.Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.
But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.
To address this shortcoming, researchers from MIT, the MIT-IBM Watson AI Lab, the Weizmann Institute of Science, and elsewhere have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.
Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames. They designed the dataset so the model must focus on contextual clues to identify the personalized object, rather than relying on knowledge it previously memorized.
When given a few example images showing a personalized object, like someone’s pet, the retrained model is better able to identify the location of that same pet in a new image.
Models retrained with their method outperformed state-of-the-art systems at this task. Importantly, their technique leaves the rest of the model’s general abilities intact.
This new approach could help future AI systems track specific objects across time, like a child’s backpack, or localize objects of interest, such as a species of animal in ecological monitoring. It could also aid in the development of AI-driven assistive technologies that help visually impaired users find certain items in a room.
“Ultimately, we want these models to be able to learn from context, just like humans do. If a model can do this well, rather than retraining it for each new task, we could just provide a few examples and it would infer how to perform the task from that context. This is a very powerful ability,” says Jehanzeb Mirza, an MIT postdoc and senior author of a paper on this technique.
Mirza is joined on the paper by co-lead authors Sivan Doveh, a postdoc at Stanford University who was a graduate student at Weizmann Institute of Science when this research was conducted; and Nimrod Shabtay, a researcher at IBM Research; James Glass, a senior research scientist and the head of the Spoken Language Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); and others. The work will be presented at the International Conference on Computer Vision.
An unexpected shortcoming
Researchers have found that large language models (LLMs) can excel at learning from context. If they feed an LLM a few examples of a task, like addition problems, it can learn to answer new addition problems based on the context that has been provided.
A vision-language model (VLM) is essentially an LLM with a visual component connected to it, so the MIT researchers thought it would inherit the LLM’s in-context learning capabilities. But this is not the case.
“The research community has not been able to find a black-and-white answer to this particular problem yet. The bottleneck could arise from the fact that some visual information is lost in the process of merging the two components together, but we just don’t know,” Mirza says.
The researchers set out to improve VLMs abilities to do in-context localization, which involves finding a specific object in a new image. They focused on the data used to retrain existing VLMs for a new task, a process called fine-tuning.
Typical fine-tuning data are gathered from random sources and depict collections of everyday objects. One image might contain cars parked on a street, while another includes a bouquet of flowers.
“There is no real coherence in these data, so the model never learns to recognize the same object in multiple images,” he says.
To fix this problem, the researchers developed a new dataset by curating samples from existing video-tracking data. These data are video clips showing the same object moving through a scene, like a tiger walking across a grassland.
They cut frames from these videos and structured the dataset so each input would consist of multiple images showing the same object in different contexts, with example questions and answers about its location.
“By using multiple images of the same object in different contexts, we encourage the model to consistently localize that object of interest by focusing on the context,” Mirza explains.
Forcing the focus
But the researchers found that VLMs tend to cheat. Instead of answering based on context clues, they will identify the object using knowledge gained during pretraining.
For instance, since the model already learned that an image of a tiger and the label “tiger” are correlated, it could identify the tiger crossing the grassland based on this pretrained knowledge, instead of inferring from context.
To solve this problem, the researchers used pseudo-names rather than actual object category names in the dataset. In this case, they changed the name of the tiger to “Charlie.”
“It took us a while to figure out how to prevent the model from cheating. But we changed the game for the model. The model does not know that ‘Charlie’ can be a tiger, so it is forced to look at the context,” he says.
The researchers also faced challenges in finding the best way to prepare the data. If the frames are too close together, the background would not change enough to provide data diversity.
In the end, finetuning VLMs with this new dataset improved accuracy at personalized localization by about 12 percent on average. When they included the dataset with pseudo-names, the performance gains reached 21 percent.
As model size increases, their technique leads to greater performance gains.
In the future, the researchers want to study possible reasons VLMs don’t inherit in-context learning capabilities from their base LLMs. In addition, they plan to explore additional mechanisms to improve the performance of a VLM without the need to retrain it with new data.
“This work reframes few-shot personalized object localization — adapting on the fly to the same object across new scenes — as an instruction-tuning problem and uses video-tracking sequences to teach VLMs to localize based on visual context rather than class priors. It also introduces the first benchmark for this setting with solid gains across open and proprietary VLMs. Given the immense significance of quick, instance-specific grounding — often without finetuning — for users of real-world workflows (such as robotics, augmented reality assistants, creative tools, etc.), the practical, data-centric recipe offered by this work can help enhance the widespread adoption of vision-language foundation models,” says Saurav Jha, a postdoc at the Mila-Quebec Artificial Intelligence Institute, who was not involved with this work.
Additional co-authors are Wei Lin, a research associate at Johannes Kepler University; Eli Schwartz, a research scientist at IBM Research; Hilde Kuehne, professor of computer science at Tuebingen AI Center and an affiliated professor at the MIT-IBM Watson AI Lab; Raja Giryes, an associate professor at Tel Aviv University; Rogerio Feris, a principal scientist and manager at the MIT-IBM Watson AI Lab; Leonid Karlinsky, a principal research scientist at IBM Research; Assaf Arbelle, a senior research scientist at IBM Research; and Shimon Ullman, the Samy and Ruth Cohn Professor of Computer Science at the Weizmann Institute of Science.
This research was funded, in part, by the MIT-IBM Watson AI Lab.
Darcy McRose and Mehtaab Sawhney ’20, PhD ’24 named 2025 Packard Fellows for Science and EngineeringMcRose, an environmental microbiologist, is recognized for researching the ecological roles of antibiotics in shaping ecosystems, agriculture, and health.The David and Lucile Packard Foundation has announced that two MIT affiliates have been named 2025 Packard Fellows for Science and Engineering. Darcy McRose, the Thomas D. and Virginia W. Cabot Career Development Assistant Professor in the MIT Department of Civil and Environmental Engineering, has been honored, along with Mehtaab Sawhney ’20, PhD ’24, a graduate of the Department of Mathematics who is now at Columbia University.
The honorees are among 20 junior faculty named among the nation’s most innovative early-career scientists and engineers. Each Packard Fellow receives an unrestricted research grant of $875,000 over five years to support their pursuit of pioneering research and bold new ideas.
“I’m incredibly grateful and honored to be awarded a Packard Fellowship,” says McRose. “It will allow us to continue our work exploring how small molecules control microbial communities in soils and on plant roots, with much-appreciated flexibility to follow our imagination wherever it leads us.”
McRose and her lab study secondary metabolites — small organic molecules that microbes and plants release into soils. Often known as antibiotics, these compounds do far more than fight infections; they can help unlock soil nutrients, shape microbial communities around plant roots, and influence soil fertility.
“Antibiotics made by soil microorganisms are widely used in medicine, but we know surprisingly little about what they do in nature,” explains McRose. “Just as healthy microbiomes support human health, plant microbiomes support plant health, and secondary metabolites can help to regulate the microbial community, suppressing pathogens and promoting beneficial microbes.”
Her lab integrates techniques from genetics, chemistry, and geosciences to investigate how these molecules shape interactions between microbes and plants in soil — one of Earth’s most complex and least-understood environments. By using secondary metabolites as experimental tools, McRose aims to uncover the molecular mechanisms that govern processes like soil fertility and nutrient cycling that are foundational to sustainable agriculture and ecosystem health.
Studying antibiotics in the environments where they evolved could also yield new strategies for combating soil-borne pathogens and improving crop resilience. “Soil is a true scientific frontier,” McRose says. “Studying these environments has the potential to reveal fascinating, fundamental insights into microbial life — many of which we can’t even imagine yet.”
A native of California, McRose earned her bachelor’s and master’s degrees from Stanford University, followed by a PhD in geosciences from Princeton University. Her graduate thesis focused on how bacteria acquire trace metals from the environment. Her postdoctoral research on secondary metabolites at Caltech was supported by multiple fellowships, including the Simons Foundation Marine Microbial Ecology Postdoctoral Fellowship, the L’Oréal USA For Women in Science Fellowship, and a Division Fellowship from Biology and Biological Engineering at Caltech.
McRose joined the MIT faculty in 2022. In 2025, she was named a Sloan Foundation Research Fellow in Earth System Science and awarded the Maseeh Excellence in Teaching Award.
Past Packard Fellows have gone on to earn the highest honors, including Nobel Prizes in chemistry and physics, the Fields Medal, Alan T. Waterman Awards, Breakthrough Prizes, Kavli Prizes, and elections to the National Academies of Science, Engineering, and Medicine. Each year, the foundation reviews 100 nominations for consideration from 50 invited institutions. The Packard Fellowships Advisory Panel, a group of 12 internationally recognized scientists and engineers, evaluates the nominations and recommends 20 fellows for approval by the Packard Foundation Board of Trustees.
MIT-Toyota collaboration powers driver assistance in millions of vehiclesA decade-plus alliance between MIT’s AgeLab and Toyota’s Collaborative Safety Research Center is recognized as a key contributor to advancements in automotive safety and human-machine interaction.A decade-plus collaboration between MIT’s AgeLab and the Toyota Motor Corporation is recognized as a key contributor to advancements in automotive safety and human-machine interaction. Through the AgeLab at the MIT Center for Transportation and Logistics (CTL), researchers have collected and analyzed vast real-world driving datasets that have helped inform Toyota’s vehicle design and safety systems.
Toyota recently marked the completion of its 100th project through the Collaborative Safety Research Center (CSRC), celebrating MIT’s role in shaping technologies that enhance driver-assistance features and continue to forge the path for automated mobility. A key foundation for the 100th project is CSRC’s ongoing support for MIT CTL’s Advanced Vehicle Technology (AVT) Consortium.
Real-world data, real-world impact
“AVT was conceptualized over a decade ago as an academic-industry partnership to promote shared investment in real-world, naturalistic data collection, analysis, and collaboration — efforts aimed at advancing safer, more convenient, and more comfortable automobility,” says Bryan Reimer, founder and co-director of AVT. “Since its founding, AVT has drawn together over 25 organizations — including vehicle manufacturers, suppliers, insurers, and consumer research groups — to invest in understanding how automotive technologies function, how they influence driver behavior, and where further innovation is needed. This work has enabled stakeholders like Toyota to make more-informed decisions in product development and deployment.”
“CSRC’s 100th project marks a significant milestone in our collaboration,” Reimer adds. “We deeply value CSRC’s sustained investment, and commend the organization’s commitment to global industry impact and the open dissemination of research to advance societal benefit.”
“Toyota, through its Collaborative Safety Research Center, is proud to be a founding member of the AVT Consortium,” says Jason Hallman, senior manager of Toyota CSRC. “Since 2011, CSRC has collaborated with researchers such as AVT and MIT AgeLab on projects that help inform future products and policy, and to promote a future safe mobility society for all. The AVT specifically has helped us to study the real-world use of several vehicle technologies now available.”
Among these technologies are lane-centering assistance and adaptive cruise control — widely-used technologies that benefit from an understanding of how drivers interact with automation. “AVT uniquely combines vehicle and driver data to help inform future products and highlight the interplay between the performance of these features and the drivers using them,” says Josh Domeyer, principal scientist at CSRC.
Influencing global standards and Olympic-scale innovation
Insights from MIT’s pedestrian-driver interaction research with CSRC also helped shape Toyota’s automated vehicle communication systems. “These data helped develop our foundational understanding that drivers and pedestrians use their movements to communicate during routine traffic encounters,” said Domeyer. “This concept informed the deployment of Toyota’s e-Palette at the Tokyo Olympics, and it has been captured as a best practice in an ISO standard for automated driving system communication.”
The AVT Consortium's naturalistic driving datasets continue to serve as a foundation for behavioral safety strategies. From identifying moments of distraction to understanding how drivers multitask behind the wheel, the work is guiding subtle but impactful design considerations.
“By studying the natural behaviors of drivers and their contexts in the AVT datasets, we hope to identify new ways to encourage safe habits that align with customer preferences,” Domeyer says. “These can include subtle nudges, or modifications to existing vehicle features, or even communication and education partnerships outside of Toyota that reinforce these safe driving habits.”
Professor Yossi Sheffi, director of MIT CTL, comments, “This partnership exemplifies the impact of MIT collaborative research on industry to make real, practical innovation possible.”
A model for industry-academic collaboration
Founded in 2015, the AVT Consortium brings together automotive manufacturers, suppliers, and insurers to accelerate research in driver behavior, safety, and the transition toward automated systems. The consortium’s interdisciplinary approach — integrating engineering, human factors, and data science — has helped generate one of the world’s most unique and actionable real-world driving datasets.
As Toyota celebrates its research milestone, MIT reflects on a partnership that exemplifies the power of industry-academic collaboration to shape safer, smarter mobility.
MIT engineers solve the sticky-cell problem in bioreactors and other industriesTheir system uses electrochemically generated bubbles to detach cells from surfaces, which could accelerate the growth of carbon-absorbing algae and lifesaving cell therapies.To help mitigate climate change, companies are using bioreactors to grow algae and other microorganisms that are hundreds of times more efficient at absorbing CO2 than trees. Meanwhile, in the pharmaceutical industry, cell culture is used to manufacture biologic drugs and other advanced treatments, including lifesaving gene and cell therapies.
Both processes are hampered by cells’ tendency to stick to surfaces, which leads to a huge amount of waste and downtime for cleaning. A similar problem slows down biofuel production, interferes with biosensors and implants, and makes the food and beverage industry less efficient.
Now, MIT researchers have developed an approach for detaching cells from surfaces on demand, using electrochemically generated bubbles. In an open-access paper published in Science Advances, the researchers demonstrated their approach in a lab prototype and showed it could work across a range of cells and surfaces without harming the cells.
“We wanted to develop a technology that could be high-throughput and plug-and-play, and that would allow cells to attach and detach on demand to improve the workflow in these industrial processes,” says Professor Kripa Varanasi, senior author of the study. “This is a fundamental issue with cells, and we’ve solved it with a process that can scale. It lends itself to many different applications.”
Joining Varanasi on the study are co-first authors Bert Vandereydt, a PhD student in mechanical engineering, and former postdoc Baptiste Blanc.
Solving a sticky problem
The researchers began with a mission.
“We’ve been working on figuring out how we can efficiently capture CO2 across different sources and convert it into valuable products for various end markets,” Varanasi says. “That’s where this photobioreactor and cell detachment comes into the picture.”
Photobioreactors are used to grow carbon-absorbing algae cells by creating tightly controlled environments involving water and sunlight. They feature long, winding tubes with clear surfaces to let in the light algae need to grow. When algae stick to those surfaces, they block out the light, requiring cleaning.
“You have to shut down and clean up the entire reactor as frequently as every two weeks,” Varanasi says. “It’s a huge operational challenge.”
The researchers realized other industries have similar problem due to many cells’ natural adhesion, or stickiness. Each industry has its own solution for cell adhesion depending on how important it is that the cells survive. Some people scrape the surfaces clean, while others use special coatings that are toxic to cells.
In the pharmaceutical and biotech industries, cell detachment is typically carried out using enzymes. However, this method poses several challenges — it can damage cell membranes, is time-consuming, and requires large amounts of consumables, resulting in millions of liters of biowaste.
To create a better solution, the researchers began by studying other efforts to clear surfaces with bubbles, which mainly involved spraying bubbles onto surfaces and had been largely ineffective.
“We realized we needed the bubbles to form on the surfaces where we don’t want these cells to stick, so when the bubbles detach it creates a local fluid flow that creates shear stress at the interface and removes the cells,” Varanasi explains.
Electric currents generate bubbles by splitting water into hydrogen and oxygen. But previous attempts at using electricity to detach cells were hampered because the cell culture mediums contain sodium chloride, which turns into bleach when combined with an electric current. The bleach damages the cells, making it impractical for many applications.
“The culprit is the anode — that’s where the sodium chloride turns to bleach,” Vandereydt explained. “We figured if we could separate that electrode from the rest of the system, we could prevent bleach from being generated.”
To make a better system, the researchers built a 3-square-inch glass surface and deposited a gold electrode on top of it. The layer of gold is so thin it doesn’t block out light. To keep the other electrode separate, the researchers integrated a special membrane that only allows protons to pass through. The set up allowed the researchers to send a current through without generating bleach.
To test their setup, they allowed algae cells from a concentrated solution to stick to the surfaces. When they applied a voltage, the bubbles separated the cells from the surfaces without harming them.
The researchers also studied the interaction between the bubbles and cells, finding the higher the current density, the more bubbles were created and the more algae was removed. They developed a model for understanding how much current would be needed to remove algae in different settings and matched it with results from experiments involving algae as well as cells from ovarian cancer and bones.
“Mammalian cells are orders of magnitude more sensitive than algae cells, but even with those cells, we were able to detach them with no impact to the viability of the cell,” Vandereydt says.
Getting to scale
The researchers say their system could represent a breakthrough in applications where bleach or other chemicals would harm cells. That includes pharmaceutical and food production.
“If we can keep these systems running without fouling and other problems, then we can make them much more economical,” Varanasi says.
For cell culture plates used in the pharmaceutical industry, the team envisions their system comprising an electrode that could be robotically moved from one culture plate to the next, to detach cells as they’re grown. It could also be coiled around algae harvesting systems.
“This has general applicability because it doesn’t rely on any specific biological or chemical treatments, but on a physical force that is system-agnostic,” Varanasi says. “It’s also highly scalable to a lot of different processes, including particle removal.”
Varanasi cautions there is much work to be done to scale up the system. But he hopes it can one day make algae and other cell harvesting more efficient.
“The burning problem of our time is to somehow capture CO2 in a way that’s economically feasible,” Varanasi says. “These photobioreactors could be used for that, but we have to overcome the cell adhesion problem.”
The work was supported, in part, by Eni S.p.A through the MIT Energy Initiative, the Belgian American Educational Foundation Fellowship, and the Maria Zambrano Fellowship.
Why some quantum materials stall while others scaleIn a new study, MIT researchers evaluated quantum materials’ potential for scalable commercial success — and identified promising candidates.People tend to think of quantum materials — whose properties arise from quantum mechanical effects — as exotic curiosities. But some quantum materials have become a ubiquitous part of our computer hard drives, TV screens, and medical devices. Still, the vast majority of quantum materials never accomplish much outside of the lab.
What makes certain quantum materials commercial successes and others commercially irrelevant? If researchers knew, they could direct their efforts toward more promising materials — a big deal since they may spend years studying a single material.
Now, MIT researchers have developed a system for evaluating the scale-up potential of quantum materials. Their framework combines a material’s quantum behavior with its cost, supply chain resilience, environmental footprint, and other factors. The researchers used their framework to evaluate over 16,000 materials, finding that the materials with the highest quantum fluctuation in the centers of their electrons also tend to be more expensive and environmentally damaging. The researchers also identified a set of materials that achieve a balance between quantum functionality and sustainability for further study.
The team hopes their approach will help guide the development of more commercially viable quantum materials that could be used for next generation microelectronics, energy harvesting applications, medical diagnostics, and more.
“People studying quantum materials are very focused on their properties and quantum mechanics,” says Mingda Li, associate professor of nuclear science and engineering and the senior author of the work. “For some reason, they have a natural resistance during fundamental materials research to thinking about the costs and other factors. Some told me they think those factors are too ‘soft’ or not related to science. But I think within 10 years, people will routinely be thinking about cost and environmental impact at every stage of development.”
The paper appears in Materials Today. Joining Li on the paper are co-first authors and PhD students Artittaya Boonkird, Mouyang Cheng, and Abhijatmedhi Chotrattanapituk, along with PhD students Denisse Cordova Carrizales and Ryotaro Okabe; former graduate research assistants Thanh Nguyen and Nathan Drucker; postdoc Manasi Mandal; Instructor Ellan Spero of the Department of Materials Science and Engineering (DMSE); Professor Christine Ortiz of the Department of DMSE; Professor Liang Fu of the Department of Physics; Professor Tomas Palacios of the Department of Electrical Engineering and Computer Science (EECS); Associate Professor Farnaz Niroui of EECS; Assistant Professor Jingjie Yeo of Cornell University; and PhD student Vsevolod Belosevich and Assostant Professor Qiong Ma of Boston College.
Materials with impact
Cheng and Boonkird say that materials science researchers often gravitate toward quantum materials with the most exotic quantum properties rather than the ones most likely to be used in products that change the world.
“Researchers don’t always think about the costs or environmental impacts of the materials they study,” Cheng says. “But those factors can make them impossible to do anything with.”
Li and his collaborators wanted to help researchers focus on quantum materials with more potential to be adopted by industry. For this study, they developed methods for evaluating factors like the materials’ price and environmental impact using their elements and common practices for mining and processing those elements. At the same time, they quantified the materials’ level of “quantumness” using an AI model created by the same group last year, based on a concept proposed by MIT professor of physics Liang Fu, termed quantum weight.
“For a long time, it’s been unclear how to quantify the quantumness of a material,” Fu says. “Quantum weight is very useful for this purpose. Basically, the higher the quantum weight of a material, the more quantum it is.”
The researchers focused on a class of quantum materials with exotic electronic properties known as topological materials, eventually assigning over 16,000 materials scores on environmental impact, price, import resilience, and more.
For the first time, the researchers found a strong correlation between the material’s quantum weight and how expensive and environmentally damaging it is.
“That’s useful information because the industry really wants something very low-cost,” Spero says. “We know what we should be looking for: high quantum weight, low-cost materials. Very few materials being developed meet that criteria, and that likely explains why they don’t scale to industry.”
The researchers identified 200 environmentally sustainable materials and further refined the list down to 31 material candidates that achieved an optimal balance of quantum functionality and high-potential impact.
The researchers also found that several widely studied materials exhibit high environmental impact scores, indicating they will be hard to scale sustainably. “Considering the scalability of manufacturing and environmental availability and impact is critical to ensuring practical adoption of these materials in emerging technologies,” says Niroui.
Guiding research
Many of the topological materials evaluated in the paper have never been synthesized, which limited the accuracy of the study’s environmental and cost predictions. But the authors say the researchers are already working with companies to study some of the promising materials identified in the paper.
“We talked with people at semiconductor companies that said some of these materials were really interesting to them, and our chemist collaborators also identified some materials they find really interesting through this work,” Palacios says. “Now we want to experimentally study these cheaper topological materials to understand their performance better.”
“Solar cells have an efficiency limit of 34 percent, but many topological materials have a theoretical limit of 89 percent. Plus, you can harvest energy across all electromagnetic bands, including our body heat,” Fu says. “If we could reach those limits, you could easily charge your cell phone using body heat. These are performances that have been demonstrated in labs, but could never scale up. That’s the kind of thing we’re trying to push forward."
This work was supported, in part, by the National Science Foundation and the U.S. Department of Energy.
Earthquake damage at deeper depths occurs long after initial activityWhile the Earth’s upper crust recovers quickly from seismic activity, new research finds the mid-crust recovers much more slowly, if at all.Earthquakes often bring to mind images of destruction, of the Earth breaking open and altering landscapes. But after an earthquake, the area around it undergoes a period of post-seismic deformation, where areas that didn’t break experience new stress as a result of the sudden change in the surroundings. Once it has adjusted to this new stress, it reaches a state of recovery.
Geologists have often thought that this recovery period was a smooth, continuous process. But MIT research published recently in Science has found evidence that while healing occurs quickly at shallow depths — roughly above 10 km — deeper depths recover more slowly, if at all.
“If you were to look before and after in the shallow crust, you wouldn’t see any permanent change. But there’s this very permanent change that persists in the mid-crust,” says Jared Bryan, a graduate student in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and lead author on the paper.
The paper’s other authors include EAPS Professor William Frank and Pascal Audet from the University of Ottawa.
Everything but the quakes
In order to assemble a full understanding of how the crust behaves before, during, and after an earthquake sequence, the researchers looked at seismic data from the 2019 Ridgecrest earthquakes in California. This immature fault zone experienced the largest earthquake in the state in 20 years, and tens of thousands of aftershocks over the following year. They then removed seismic data created by the sequence and only looked at waves generated by other seismic activity around the world to see how their paths through the Earth changed before and after the sequence.
“One person’s signal is another person’s noise,” says Bryan. They also used general ambient noise from sources like ocean waves and traffic that are also picked up by seismometers. Then, using a technique called a receiver function, they were able to see the speed of the waves as they traveled and how it changed due to conditions in the Earth such as rock density and porosity, much in the same way we use sonar to see how acoustic waves change when they interact with objects. With all this information, they were able to construct basic maps of the Earth around the Ridgecrest fault zone before and after the sequence.
What they found was that the shallow crust, extending about 10 km into the Earth, recovered over the course of a few months. In contrast, deeper depths in the mid-crust didn’t experience immediate damage, but rather changed over the same timescale as shallow depths recovered.
“What was surprising is that the healing in the shallow crust was so quick, and then you have this complementary accumulation occurring, not at the time of the earthquake, but instead over the post-seismic phase,” says Bryan.
Balancing the energy budget
Understanding how recovery plays out at different depths is crucial for determining how energy is spent during different parts of the seismic process, which includes activities such as the release of energy as waves, the creation of new fractures, or energy being stored elastically in the surrounding areas. Altogether, this is collectively known as the energy budget, and it is a useful tool for understanding how damage accumulates and recovers over time.
What remains unclear is the timescales at which deeper depths recover, if at all. The paper presents two possible scenarios to explain why that might be: one in which the deep crust recovers over a much longer timescale than they observed, or one where it never recovers at all.
“Either of those are not what we expected,” says Frank. “And both of them are interesting.”
Further research will require more observations to build out a more detailed picture to see at what depth the change becomes more pronounced. In addition, Bryan wants to look at other areas, such as more mature faults that experience higher levels of seismic activity, to see if it changes the results.
“We’ll let you know in 1,000 years whether it’s recovered,” says Bryan.
Engineering next-generation fertilizers MIT postdoc Giorgio Rizzo harnesses plant chemistry to design sustainable fertilizers that could reshape modern farming.Born in Palermo, Sicily, Giorgio Rizzo spent his childhood curious about the natural world. “I have always been fascinated by nature and how plants and animals can adapt and survive in extreme environments,” he says. “Their highly tuned biochemistry, and their incredible ability to create ones of the most complex and beautiful structures in chemistry that we still can’t even achieve in our laboratories.”
As an undergraduate student, he watched as a researcher mounted a towering chromatography column layered with colorful plant chemicals in a laboratory. When the researcher switched on a UV light, the colors turned into fluorescent shades of blue, green, red and pink. “I realized in that exact moment that I wanted to be the same person, separating new unknown compounds from a rare plant with potential pharmaceutical properties,” he recalls.
These experiences set him on a path from a master’s degree in organic chemistry to his current work as a postdoc in the MIT Department of Civil and Environmental Engineering, where he focuses on developing sustainable fertilizers and studying how rare earth elements can boost plant resilience, with the aim of reducing agriculture’s environmental impact.
In the lab of MIT Professor Benedetto Marelli, Rizzo studies plant responses to environmental stressors, such as heat, drought, and prolonged UV irradiation. This includes developing new fertilizers that can be applied as seed coating to help plants grow stronger and enhance their resistance.
“We are working on new formulations of fertilizers that aim to reduce the huge environmental impact of classical practices in agriculture based on NPK inorganic fertilizers,” Rizzo explains. Although they are fundamental to crop yields, their tendency to accumulate in soil is detrimental to the soil health and microbiome living in it. In addition, producing NPK (nitrogen, phosphorus, and potassium) fertilizers is one of the most energy-consuming and polluting chemical processes in the world.
“It is mandatory to reshape our conception of fertilizers and try to rely, at least in part, on alternative products that are safer, cheaper, and more sustainable,” he says.
Recently, Rizzo was awarded a Kavanaugh Fellowship, a program that gives MIT graduate students and postdocs entrepreneurial training and resources to bring their research from the lab to the market. “This prestigious fellowship will help me build a concrete product for a company, adding more value to our research,” he says.
Rizzo hopes their work will help farmers increase their crop yields without compromising soil quality or plant health. A major barrier to adopting new fertilizers is cost, as many farmers rely heavily on each growing season’s output and cannot risk investing in products that may underperform compared to traditional NPK fertilizers. The fertilizers being developed in the Marelli Lab address this challenge by using chitin and chitosan, abundant natural materials that make them far less expensive to produce, which Rizzo hopes will encourage farmers to try them.
“Through the Kavanaugh Fellowship, I will spend this year trying to bring the technology outside the lab to impact the world and meet the need for farmers to support their prosperity,” he says.
Mentorship has been a defining part of his postdoc experience. Rizzo describes Professor Benedetto Marelli as “an incredible mentor” who values his research interests and supports him through every stage of his work. The lab spans a wide range of projects — from plant growth enhancement and precision chemical delivery to wastewater treatment, vaccine development for fish, and advanced biochemical processes. “My colleagues created a stimulant environment with different research topics,” he notes. He is also grateful for the work he does with international institutions, which has helped him build a network of researchers and academics around the world.
Rizzo enjoys the opportunity to mentor students in the lab and appreciates their curiosity and willingness to learn. “It is one of the greatest qualities you can have as a scientist because you must be driven by curiosity to discover the unexpected,” he says.
He describes MIT as a “dynamic and stimulating experience,” but also acknowledges how overwhelming it can be. “You will feel like a small fish in a big ocean,” he says. “But that is exactly what MIT is: an ocean full of opportunities and challenges that are waiting to be solved.”
Beyond his professional work, Rizzo enjoys nature and the arts. An avid reader, he balances his scientific work with literature and history. “I never read about science-related topics — I read about it a lot already for my job,” he says. “I like classic literature, novels, essays, history of nations, and biographies. Often you can find me wandering in museums’ art collections.” Classical art, Renaissance, and Pre-Raphaelites are his favorite artistic currents.
Looking ahead, Rizzo hopes to shift his professional pathway toward startups or companies focused on agrotechnical improvement. His immediate goal is to contribute to initiatives where research has a direct, tangible impact on everyday life.
“I want to pursue the option of being part of a spinout process that would enable my research to have a direct impact in everyday life and help solve agricultural issues,” he adds.
Optimizing food subsidies: Applying digital platforms to maximize nutritionAn algorithm can change the face of food assistance policy in the Global South, says MIT assistant professor and J-WAFS researcher Ali Aouad.Oct. 16 is World Food Day, a global campaign to celebrate the founding of the Food and Agriculture Organization 80 years ago, and to work toward a healthy, sustainable, food-secure future. More than 670 million people in the world are facing hunger. Millions of others are facing rising obesity rates and struggle to get healthy food for proper nutrition.
World Food Day calls on not only world governments, but business, academia, the media, and even the youth to take action to promote resilient food systems and combat hunger. This year, the Abdul Latif Jameel Water and Food Systems Laboratory (J-WAFS) is spotlighting an MIT researcher who is working toward this goal by studying food and water systems in the Global South.
J-WAFS seed grants provide funding to early-stage research projects that are unique to prior work. In an 11th round of seed grant funding in 2025, 10 MIT faculty members received support to carry out their cutting-edge water and food research. Ali Aouad PhD ’17, assistant professor of operations management at the MIT Sloan School of Management, was one of those grantees. “I had searched before joining MIT what kind of research centers and initiatives were available that tried to coalesce research on food systems,” Aouad says. “And so, I was very excited about J-WAFS.”
Aouad gathered more information about J-WAFS at the new faculty orientation session in August 2024, where he spoke to J-WAFS staff and learned about the program’s grant opportunities for water and food research. Later that fall semester, he attended a few J-WAFS seminars on agricultural economics and water resource management. That’s when Aouad knew that his project was perfectly aligned with the J-WAFS mission of securing humankind’s water and food.
Aouad’s seed project focuses on food subsidies. With a background in operations research and an interest in digital platforms, much of his work has centered on aligning supply-side operations with heterogeneous customer preferences. Past projects include ones on retail and matching systems. “I started thinking that these types of demand-driven approaches may be also very relevant to important social challenges, particularly as they relate to food security,” Aouad says. Before starting his PhD at MIT, Aouad worked on projects that looked at subsidies for smallholder farmers in low- and middle-income countries. “I think in the back of my mind, I've always been fascinated by trying to solve these issues,” he noted.
His seed grant project, Optimal subsidy design: Application to food assistance programs, aims to leverage data on preferences and purchasing habits from local grocery stores in India to inform food assistance policy and optimize the design of subsidies. Typical data collection systems, like point-of-sales, are not as readily available in India’s local groceries, making this type of data hard to come by for low-income individuals. “Mom-and-pop stores are extremely important last-mile operators when it comes to nutrition,” he explains.
For this project, the research team gave local grocers point-of-sale scanners to track purchasing habits. “We aim to develop an algorithm that converts these transactions into some sort of ‘revelation’ of the individuals’ latent preferences,” says Aouad. “As such, we can model and optimize the food assistance programs — how much variety and flexibility is offered, taking into account the expected demand uptake.” He continues, “now, of course, our ability to answer detailed design questions [across various products and prices] depends on the quality of our inference from the data, and so this is where we need more sophisticated and robust algorithms.”
Following the data collection and model development, the ultimate goal of this research is to inform policy surrounding food assistance programs through an “optimization approach.” Aouad describes the complexities of using optimization to guide policy. “Policies are often informed by domain expertise, legacy systems, or political deliberation. A lot of researchers build rigorous evidence to inform food policy, but it’s fair to say that the kind of approach that I’m proposing in this research is not something that is commonly used. I see an opportunity for bringing a new approach and methodological tradition to a problem that has been central for policy for many decades.”
The overall health of consumers is the reason food assistance programs exist, yet measuring long-term nutritional impacts and shifts in purchase behavior is difficult. In past research, Aouad notes that the short-term effects of food assistance interventions can be significant. However, these effects are often short-lived. “This is a fascinating question that I don’t think we will be able to address within the space of interventions that we will be considering. However, I think it is something I would like to capture in the research, and maybe develop hypotheses for future work around how we can shift nutrition-related behaviors in the long run.”
While his project develops a new methodology to calibrate food assistance programs, large-scale applications are not promised. “A lot of what drives subsidy mechanisms and food assistance programs is also, quite frankly, how easy it is and how cost-effective it is to implement these policies in the first place,” comments Aouad. Cost and infrastructure barriers are unavoidable to this kind of policy research, as well as sustaining these programs. Aouad’s effort will provide insights into customer preferences and subsidy optimization in a pilot setup, but replicating this approach on a real scale may be costly. Aouad hopes to be able to gather proxy information from customers that would both feed into the model and provide insight into a more cost-effective way to collect data for large-scale implementation.
There is still much work to be done to ensure food security for all, whether it’s advances in agriculture, food-assistance programs, or ways to boost adequate nutrition. As the 2026 seed grant deadline approaches, J-WAFS will continue its mission of supporting MIT faculty as they pursue innovative projects that have practical and real impacts on water and food system challenges.
Checking the quality of materials just got easier with a new AI toolActing as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is helping with the former, with tools that comb through catalogs of materials to quickly tag promising candidates.
But once a material is made, verifying its quality still involves scanning it with specialized instruments to validate its performance — an expensive and time-consuming step that can hold up the development and distribution of new technologies.
Now, a new AI tool developed by MIT engineers could help clear the quality-control bottleneck, offering a faster and cheaper option for certain materials-driven industries.
In a study appearing today in the journal Matter, the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer. The tool takes in “spectra,” or measurements of a material in one scanning modality, such as infrared, and generates what that material’s spectra would look like if it were scanned in an entirely different modality, such as X-ray. The AI-generated spectral results match, with 99 percent accuracy, the results obtained from physically scanning the material with the new instrument.
Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations. Each of these properties is essential in gauging a material’s quality and typically requires tedious workflows on multiple expensive and distinct instruments to measure.
With SpectroGen, the researchers envision that a diversity of measurements can be made using a single and cheaper physical scope. For instance, a manufacturing line could carry out quality control of materials by scanning them with a single infrared camera. Those infrared spectra could then be fed into SpectroGen to automatically generate the material’s X-ray spectra, without the factory having to house and operate a separate, often more expensive X-ray-scanning laboratory.
The new AI tool generates spectra in less than one minute, a thousand times faster compared to traditional approaches that can take several hours to days to measure and validate.
“We think that you don’t have to do the physical measurements in all the modalities you need, but perhaps just in a single, simple, and cheap modality,” says study co-author Loza Tadesse, assistant professor of mechanical engineering at MIT. “Then you can use SpectroGen to generate the rest. And this could improve productivity, efficiency, and quality of manufacturing.”
The study’s lead author is former MIT postdoc Yanmin Zhu.
Beyond bonds
Tadesse’s interdisciplinary group at MIT pioneers technologies that advance human and planetary health, developing innovations for applications ranging from rapid disease diagnostics to sustainable agriculture.
“Diagnosing diseases, and material analysis in general, usually involves scanning samples and collecting spectra in different modalities, with different instruments that are bulky and expensive and that you might not all find in one lab,” Tadesse says. “So, we were brainstorming about how to miniaturize all this equipment and how to streamline the experimental pipeline.”
Zhu noted the increasing use of generative AI tools for discovering new materials and drug candidates, and wondered whether AI could also be harnessed to generate spectral data. In other words, could AI act as a virtual spectrometer?
A spectroscope probes a material’s properties by sending light of a certain wavelength into the material. That light causes molecular bonds in the material to vibrate in ways that scatter the light back out to the scope, where the light is recorded as a pattern of waves, or spectra, that can then be read as a signature of the material’s structure.
For AI to generate spectral data, the conventional approach would involve training an algorithm to recognize connections between physical atoms and features in a material, and the spectra they produce. Given the complexity of molecular structures within just one material, Tadesse says such an approach can quickly become intractable.
“Doing this even for just one material is impossible,” she says. “So, we thought, is there another way to interpret spectra?”
The team found an answer with math. They realized that a spectral pattern, which is a sequence of waveforms, can be represented mathematically. For instance, a spectrum that contains a series of bell curves is known as a “Gaussian” distribution, which is associated with a certain mathematical expression, compared to a series of narrower waves, known as a “Lorentzian” distribution, that is described by a separate, distinct algorithm. And as it turns out, for most materials infrared spectra characteristically contain more Lorentzian waveforms, while Raman spectra are more Gaussian, and X-ray spectra is a mix of the two.
Tadesse and Zhu worked this mathematical interpretation of spectral data into an algorithm that they then incorporated into a generative AI model.
“It’s a physics-savvy generative AI that understands what spectra are,” Tadesse says. “And the key novelty is, we interpreted spectra not as how it comes about from chemicals and bonds, but that it is actually math — curves and graphs, which an AI tool can understand and interpret.”
Data co-pilot
The team demonstrated their SpectroGen AI tool on a large, publicly available dataset of over 6,000 mineral samples. Each sample includes information on the mineral’s properties, such as its elemental composition and crystal structure. Many samples in the dataset also include spectral data in different modalities, such as X-ray, Raman, and infrared. Of these samples, the team fed several hundred to SpectroGen, in a process that trained the AI tool, also known as a neural network, to learn correlations between a mineral’s different spectral modalities. This training enabled SpectroGen to take in spectra of a material in one modality, such as in infrared, and generate what a spectra in a totally different modality, such as X-ray, should look like.
Once they trained the AI tool, the researchers fed SpectroGen spectra from a mineral in the dataset that was not included in the training process. They asked the tool to generate a spectra in a different modality, based on this “new” spectra. The AI-generated spectra, they found, was a close match to the mineral’s real spectra, which was originally recorded by a physical instrument. The researchers carried out similar tests with a number of other minerals and found that the AI tool quickly generated spectra, with 99 percent correlation.
“We can feed spectral data into the network and can get another totally different kind of spectral data, with very high accuracy, in less than a minute,” Zhu says.
The team says that SpectroGen can generate spectra for any type of mineral. In a manufacturing setting, for instance, mineral-based materials that are used to make semiconductors and battery technologies could first be quickly scanned by an infrared laser. The spectra from this infrared scanning could be fed into SpectroGen, which would then generate a spectra in X-ray, which operators or a multiagent AI platform can check to assess the material’s quality.
“I think of it as having an agent or co-pilot, supporting researchers, technicians, pipelines and industry,” Tadesse says. “We plan to customize this for different industries’ needs.”
The team is exploring ways to adapt the AI tool for disease diagnostics, and for agricultural monitoring through an upcoming project funded by Google. Tadesse is also advancing the technology to the field through a new startup and envisions making SpectroGen available for a wide range of sectors, from pharmaceuticals to semiconductors to defense.
Helping scientists run complex data analyses without writing codeCo-founded by an MIT alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.As costs for diagnostic and sequencing technologies have plummeted in recent years, researchers have collected an unprecedented amount of data around disease and biology. Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering.
Now, Watershed Bio is helping scientists and bioinformaticians run experiments and get insights with a platform that lets users analyze complex datasets regardless of their computational skills. The cloud-based platform provides workflow templates and a customizable interface to help users explore and share data of all types, including whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and more.
“Scientists want to learn about the software and data science parts of the field, but they don’t want to become software engineers writing code just to understand their data,” co-founder and CEO Jonathan Wang ’13, SM ’15 says. “With Watershed, they don’t have to.”
Watershed is being used by large and small research teams across industry and academia to drive discovery and decision-making. When new advanced analytic techniques are described in scientific journals, they can be added to Watershed’s platform immediately as templates, making cutting-edge tools more accessible and collaborative for researchers of all backgrounds.
“The data in biology is growing exponentially, and the sequencing technologies generating this data are only getting better and cheaper,” Wang says. “Coming from MIT, this issue was right in my wheelhouse: It’s a tough technical problem. It’s also a meaningful problem because these people are working to treat diseases. They know all this data has value, but they struggle to use it. We want to help them unlock more insights faster.”
No code discovery
Wang expected to major in biology at MIT, but he quickly got excited by the possibilities of building solutions that scaled to millions of people with computer science. He ended up earning both his bachelor’s and master’s degrees from the Department of Electrical Engineering and Computer Science (EECS). Wang also interned at a biology lab at MIT, where he was surprised how slow and labor-intensive experiments were.
“I saw the difference between biology and computer science, where you had these dynamic environments [in computer science] that let you get feedback immediately,” Wang says. “Even as a single person writing code, you have so much at your fingertips to play with.”
While working on machine learning and high-performance computing at MIT, Wang also co-founded a high frequency trading firm with some classmates. His team hired researchers with PhD backgrounds in areas like math and physics to develop new trading strategies, but they quickly saw a bottleneck in their process.
“Things were moving slowly because the researchers were used to building prototypes,” Wang says. “These were small approximations of models they could run locally on their machines. To put those approaches into production, they needed engineers to make them work in a high-throughput way on a computing cluster. But the engineers didn’t understand the nature of the research, so there was a lot of back and forth. It meant ideas you thought could have been implemented in a day took weeks.”
To solve the problem, Wang’s team developed a software layer that made building production-ready models as easy as building prototypes on a laptop. Then, a few years after graduating MIT, Wang noticed technologies like DNA sequencing had become cheap and ubiquitous.
“The bottleneck wasn’t sequencing anymore, so people said, ‘Let’s sequence everything,’” Wang recalls. “The limiting factor became computation. People didn’t know what to do with all the data being generated. Biologists were waiting for data scientists and bioinformaticians to help them, but those people didn’t always understand the biology at a deep enough level.”
The situation looked familiar to Wang.
“It was exactly like what we saw in finance, where researchers were trying to work with engineers, but the engineers never fully understood, and you had all this inefficiency with people waiting on the engineers,” Wang says. “Meanwhile, I learned the biologists are hungry to run these experiments, but there is such a big gap they felt they had to become a software engineer or just focus on the science.”
Wang officially founded Watershed in 2019 with physician Mark Kalinich ’13, a former classmate at MIT who is no longer involved in day-to-day operations of the company.
Wang has since heard from biotech and pharmaceutical executives about the growing complexity of biology research. Unlocking new insights increasingly involves analyzing data from entire genomes, population studies, RNA sequencing, mass spectrometry, and more. Developing personalized treatments or selecting patient populations for a clinical study can also require huge datasets, and there are new ways to analyze data being published in scientific journals all the time.
Today, companies can run large-scale analyses on Watershed without having to set up their own servers or cloud computing accounts. Researchers can use ready-made templates that work with all the most common data types to accelerate their work. Popular AI-based tools like AlphaFold and Geneformer are also available, and Watershed’s platform makes sharing workflows and digging deeper into results easy.
“The platform hits a sweet spot of usability and customizability for people of all backgrounds,” Wang says. “No science is ever truly the same. I avoid the word product because that implies you deploy something and then you just run it at scale forever. Research isn’t like that. Research is about coming up with an idea, testing it, and using the outcome to come up with another idea. The faster you can design, implement, and execute experiments, the faster you can move on to the next one.”
Accelerating biology
Wang believes Watershed is helping biologists keep up with the latest advances in biology and accelerating scientific discovery in the process.
“If you can help scientists unlock insights not a little bit faster, but 10 or 20 times faster, it can really make a difference,” Wang says.
Watershed is being used by researchers in academia and in companies of all sizes. Executives at biotech and pharmaceutical companies also use Watershed to make decisions about new experiments and drug candidates.
“We’ve seen success in all those areas, and the common thread is people understanding research but not being an expert in computer science or software engineering,” Wang says. “It’s exciting to see this industry develop. For me, it’s great being from MIT and now to be back in Kendall Square where Watershed is based. This is where so much of the cutting-edge progress is happening. We’re trying to do our part to enable the future of biology.”
New MIT initiative seeks to transform rare brain disorders researchThe Rare Brain Disorders Nexus aims to accelerate the development of novel therapies for a spectrum of uncommon brain diseases.More than 300 million people worldwide are living with rare disorders — many of which have a genetic cause and affect the brain and nervous system — yet the vast majority of these conditions lack an approved therapy. Because each rare disorder affects fewer than 65 out of every 100,000 people, studying these disorders and creating new treatments for them is especially challenging.
Thanks to a generous philanthropic gift from Ana Méndez ’91 and Rajeev Jayavant ’86, EE ’88, SM ’88, MIT is now poised to fill gaps in this research landscape. By establishing the Rare Brain Disorders Nexus — or RareNet — at MIT's McGovern Institute for Brain Research, the alumni aim to convene leaders in neuroscience research, clinical medicine, patient advocacy, and industry to streamline the lab-to-clinic pipeline for rare brain disorder treatments.
“Ana and Rajeev’s commitment to MIT will form crucial partnerships to propel the translation of scientific discoveries into promising therapeutics and expand the Institute’s impact on the rare brain disorders community,” says MIT President Sally Kornbluth. “We are deeply grateful for their pivotal role in advancing such critical science and bringing attention to conditions that have long been overlooked.”
Building new coalitions
Several hurdles have slowed the lab-to-clinic pipeline for rare brain disorder research. It is difficult to secure a sufficient number of patients per study, and current research efforts are fragmented, since each study typically focuses on a single disorder (there are more than 7,000 known rare disorders, according to the World Health Organization). Pharmaceutical companies are often reluctant to invest in emerging treatments due to a limited market size and the high costs associated with preparing drugs for commercialization.
Méndez and Jayavant envision that RareNet will finally break down these barriers. “Our hope is that RareNet will allow leaders in the field to come together under a shared framework and ignite scientific breakthroughs across multiple conditions. A discovery for one rare brain disorder could unlock new insights that are relevant to another,” says Jayavant. “By congregating the best minds in the field, we are confident that MIT will create the right scientific climate to produce drug candidates that may benefit a spectrum of uncommon conditions.”
Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor in Neuroscience and associate director of the McGovern Institute, will serve as RareNet’s inaugural faculty director. Feng holds a strong record of advancing studies on therapies for neurodevelopmental disorders, including autism spectrum disorders, Williams syndrome, and uncommon forms of epilepsy. His team’s gene therapy for Phelan-McDermid syndrome, a rare and profound autism spectrum disorder, has been licensed to Jaguar Gene Therapy and is currently undergoing clinical trials. “RareNet pioneers a unique model for biomedical research — one that is reimagining the role academia can play in developing therapeutics,” says Feng.
RareNet plans to deploy two major initiatives: a global consortium and a therapeutic pipeline accelerator. The consortium will form an international network of researchers, clinicians, and patient groups from the outset. It seeks to connect siloed research efforts, secure more patient samples, promote data sharing, and drive a strong sense of trust and goal alignment across the RareNet community. Partnerships within the consortium will support the aim of the therapeutic pipeline accelerator: to de-risk early lab discoveries and expedite their translation to clinic. By fostering more targeted collaborations — especially between academia and industry — the accelerator will prepare potential treatments for clinical use as efficiently as possible.
MIT labs are focusing on four uncommon conditions in the first wave of RareNet projects: Rett syndrome, prion disease, disorders linked to SYNGAP1 mutations, and Sturge-Weber syndrome. The teams are working to develop novel therapies that can slow, halt, or reverse dysfunctions in the brain and nervous system.
These efforts will build new bridges to connect key stakeholders across the rare brain disorders community and disrupt conventional research approaches. “Rajeev and I are motivated to seed powerful collaborations between MIT researchers, clinicians, patients, and industry,” says Méndez. “Guoping Feng clearly understands our goal to create an environment where foundational studies can thrive and seamlessly move toward clinical impact.”
“Patient and caregiver experiences, and our foreseeable impact on their lives, will guide us and remain at the forefront of our work,” Feng adds. “For far too long has the rare brain disorders community been deprived of life-changing treatments — and, importantly, hope. RareNet gives us the opportunity to transform how we study these conditions, and to do so at a moment when it’s needed more than ever.”
Geologists discover the first evidence of 4.5-billion-year-old “proto Earth” Materials from ancient rocks could reveal conditions in the early solar system that shaped the early Earth and other planets.Scientists at MIT and elsewhere have discovered extremely rare remnants of “proto Earth,” which formed about 4.5 billion years ago, before a colossal collision irreversibly altered the primitive planet’s composition and produced the Earth as we know today. Their findings, reported today in the journal Nature Geosciences, will help scientists piece together the primordial starting ingredients that forged the early Earth and the rest of the solar system.
Billions of years ago, the early solar system was a swirling disk of gas and dust that eventually clumped and accumulated to form the earliest meteorites, which in turn merged to form the proto Earth and its neighboring planets.
In this earliest phase, Earth was likely rocky and bubbling with lava. Then, less than 100 million years later, a Mars-sized meteorite slammed into the infant planet in a singular “giant impact” event that completely scrambled and melted the planet’s interior, effectively resetting its chemistry. Whatever original material the proto Earth was made from was thought to have been altogether transformed.
But the MIT team’s findings suggest otherwise. The researchers have identified a chemical signature in ancient rocks that is unique from most other materials found in the Earth today. The signature is in the form of a subtle imbalance in potassium isotopes discovered in samples of very old and very deep rocks. The team determined that the potassium imbalance could not have been produced by any previous large impacts or geological processes occurring in the Earth presently.
The most likely explanation for the samples’ chemical composition is that they must be leftover material from the proto Earth that somehow remained unchanged, even as most of the early planet was impacted and transformed.
“This is maybe the first direct evidence that we’ve preserved the proto Earth materials,” says Nicole Nie, the Paul M. Cook Career Development Assistant Professor of Earth and Planetary Sciences at MIT. “We see a piece of the very ancient Earth, even before the giant impact. This is amazing because we would expect this very early signature to be slowly erased through Earth’s evolution.”
The study’s other authors include Da Wang of Chengdu University of Technology in China, Steven Shirey and Richard Carlson of the Carnegie Institution for Science in Washington, Bradley Peters of ETH Zürich in Switzerland, and James Day of Scripps Institution of Oceanography in California.
A curious anomaly
In 2023, Nie and her colleagues analyzed many of the major meteorites that have been collected from sites around the world and carefully studied. Before impacting the Earth, these meteorites likely formed at various times and locations throughout the solar system, and therefore represent the solar system’s changing conditions over time. When the researchers compared the chemical compositions of these meteorite samples to Earth, they identified among them a “potassium isotopic anomaly.”
Isotopes are slightly different versions of an element that have the same number of protons but a different number of neutrons. The element potassium can exist in one of three naturally-occurring isotopes, with mass numbers (protons plus neutrons) of 39, 40, and 41, respectively. Wherever potassium has been found on Earth, it exists in a characteristic combination of isotopes, with potassium-39 and potassium-41 being overwhelmingly dominant. Potassium-40 is present, but at a vanishingly small percentage in comparison.
Nie and her colleagues discovered that the meteorites they studied showed balances of potassium isotopes that were different from most materials on Earth. This potassium anomaly suggested that any material that exhibits a similar anomaly likely predates Earth’s present composition. In other words, any potassium imbalance would be a strong sign of material from the proto Earth, before the giant impact reset the planet’s chemical composition.
“In that work, we found that different meteorites have different potassium isotopic signatures, and that means potassium can be used as a tracer of Earth’s building blocks,” Nie explains.
“Built different”
In the current study, the team looked for signs of potassium anomalies not in meteorites, but within the Earth. Their samples include rocks, in powder form, from Greenland and Canada, where some of the oldest preserved rocks are found. They also analyzed lava deposits collected from Hawaii, where volcanoes have brought up some of the Earth’s earliest, deepest materials from the mantle (the planet’s thickest layer of rock that separates the crust from the core).
“If this potassium signature is preserved, we would want to look for it in deep time and deep Earth,” Nie says.
The team first dissolved the various powder samples in acid, then carefully isolated any potassium from the rest of the sample and used a special mass spectrometer to measure the ratio of each of potassium’s three isotopes. Remarkably, they identified in the samples an isotopic signature that was different from what’s been found in most materials on Earth.
Specifically, they identified a deficit in the potassium-40 isotope. In most materials on Earth, this isotope is already an insignificant fraction compared to potassium’s other two isotopes. But the researchers were able to discern that their samples contained an even smaller percentage of potassium-40. Detecting this tiny deficit is like spotting a single grain of brown sand in a bucket rather than a scoop full of of yellow sand.
The team found that, indeed, the samples exhibited the potassium-40 deficit, showing that the materials “were built different,” says Nie, compared to most of what we see on Earth today.
But could the samples be rare remnants of the proto Earth? To answer this, the researchers assumed that this might be the case. They reasoned that if the proto Earth were originally made from such potassium-40-deficient materials, then most of this material would have undergone chemical changes — from the giant impact and subsequent, smaller meteorite impacts — that ultimately resulted in the materials with more potassium-40 that we see today.
The team used compositional data from every known meteorite and carried out simulations of how the samples’ potassium-40 deficit would change following impacts by these meteorites and by the giant impact. They also simulated geological processes that the Earth experienced over time, such as the heating and mixing of the mantle. In the end, their simulations produced a composition with a slightly higher fraction of potassium-40 compared to the samples from Canada, Greenland, and Hawaii. More importantly, the simulated compositions matched those of most modern-day materials.
The work suggests that materials with a potassium-40 deficit are likely leftover original material from the proto Earth.
Curiously, the samples’ signature isn’t a precise match with any other meteorite in geologists’ collections. While the meteorites in the team’s previous work showed potassium anomalies, they aren’t exactly the deficit seen in the proto Earth samples. This means that whatever meteorites and materials originally formed the proto Earth have yet to be discovered.
“Scientists have been trying to understand Earth’s original chemical composition by combining the compositions of different groups of meteorites,” Nie says. “But our study shows that the current meteorite inventory is not complete, and there is much more to learn about where our planet came from.”
This work was supported, in part, by NASA and MIT.
A new system can dial expression of synthetic genes up or downThe promoter editing system could be used to fine-tune gene therapy or to more efficiently reprogram cells for therapeutic use.For decades, synthetic biologists have been developing gene circuits that can be transferred into cells for applications such as reprogramming a stem cell into a neuron or generating a protein that could help treat a disease such as fragile X syndrome.
These gene circuits are typically delivered into cells by carriers such as nonpathogenic viruses. However, it has been difficult to ensure that these cells end up producing the correct amount of the protein encoded by the synthetic gene.
To overcome that obstacle, MIT engineers have designed a new control mechanism that allows them to establish a desired protein level, or set point, for any gene circuit. This approach also allows them to edit the set point after the circuit is delivered.
“This is a really stable and multifunctional tool. The tool is very modular, so there are a lot of transgenes you could control with this system,” says Katie Galloway, an assistant professor in Chemical Engineering at MIT and the senior author of the new study.
Using this strategy, the researchers showed that they could induce cells to generate consistent levels of target proteins. In one application that they demonstrated, they converted mouse embryonic fibroblasts to motor neurons by delivering high levels of a gene that promotes that conversion.
MIT graduate student Sneha Kabaria is the lead author of the paper, which appears today in Nature Biotechnology. Other authors include Yunbeen Bae ’24; MIT graduate students Mary Ehmann, Brittany Lende-Dorn, Emma Peterman, and Kasey Love; Adam Beitz PhD ’25; and former MIT postdoc Deon Ploessl.
Dialing up gene expression
Synthetic gene circuits are engineered to include not only the gene of interest, but also a promoter region. At this site, transcription factors and other regulators can bind, turning on the expression of the synthetic gene.
However, it’s not always possible to get all of the cells in a population to express the desired gene at a uniform level. One reason for that is that some cells may take up just one copy of the circuit, while others receive many more. Additionally, cells have natural variation in how much protein they produce.
That has made reprogramming cells challenging because it’s difficult to ensure that every cell in a population of skin cells, for example, will produce enough of the necessary transcription factors to successfully transition into a new cell identity, such as a neuron or induced pluripotent stem cell.
In the new paper, the researchers devised a way to control gene expression levels by changing the distance between the synthetic gene and its promoter. They found that when there was a longer DNA “spacer” between the promoter region and the gene, the gene would be expressed at a lower level. That extra distance, they showed, makes it less likely that transcription factors bound to the promoter will effectively turn on gene transcription.
Then, to create set points that could be edited, the researchers incorporated sites within the spacer that can be excised by an enzyme called Cre recombinase. As parts of the spacer are cut out, it helps bring the transcription factors closer to the gene of interest, which turns up gene expression.
The researchers showed they could create spacers with multiple excision points, each targeted by different recombinases. This allowed them to create a system called DIAL, that they could use to establish “high,” “med,” “low” and “off” set points for gene expression.
After the DNA segment carrying the gene and its promoter is delivered into cells, recombinases can be added to the cells, allowing the set point to be edited at any time.
The researchers demonstrated their system in mouse and human cells by delivering the gene for different fluorescent proteins and functional genes, and showed that they could get uniform expression across the a population of cells at the target level.
“We achieved uniform and stable control. This is very exciting for us because lack of uniform, stable control has been one of the things that's been limiting our ability to build reliable systems in synthetic biology. When there are too many variables that affect your system, and then you add in normal biological variation, it’s very hard to build stable systems,” Galloway says.
Reprogramming cells
To demonstrate potential applications of the DIAL system, the researchers then used it to deliver different levels of the gene HRasG12V to mouse embryonic fibroblasts. This HRas variant has previously been shown to increase the rate of conversion of fibroblasts to neurons. The MIT team found that in cells that received a higher dose of the gene, a larger percentage of them were able to successfully transform into neurons.
Using this system, researchers now hope to perform more systematic studies of different transcription factors that can induce cells to transition to different cell types. Such studies could reveal how different levels of those factors affect the success rate, and whether changing the transcription factors levels might alter the cell type that is generated.
In ongoing work, the researchers have shown that DIAL can be combined with a system they previously developed, known as ComMAND, that uses a feedforward loop to help prevent cells from overexpressing a therapeutic gene.
Using these systems together, it could be possible to tailor gene therapies to produce specific, consistent protein levels in the target cells of individual patients, the researchers say.
“This is something we’re excited about because both DIAL and ComMAND are highly modular, so you could not only have a well-controlled gene therapy that’s somewhat general for a population, but you could, in theory, tailor it for any given person or any given cell type,” Galloway says.
The research was funded, in part, by the National Institute of General Medical Sciences, the National Science Foundation, and the Institute for Collaborative Biotechnologies.
MIT releases financials and endowment figures for 2025The Institute’s pooled investments returned 14.8 percent last year; endowment stands at $27.4 billion.The Massachusetts Institute of Technology Investment Management Company (MITIMCo) announced today that MIT’s unitized pool of endowment and other MIT funds generated an investment return of 14.8 percent during the fiscal year ending June 30, 2025, as measured using valuations received within one month of fiscal year end. At the end of the fiscal year, MIT’s endowment funds totaled $27.4 billion, excluding pledges. Over the 10 years ending June 30, 2025, MIT generated an annualized return of 10.7 percent.
The endowment is the bedrock of MIT’s finances, made possible by gifts from alumni and friends for more than a century. The use of the endowment is governed by a state law that requires MIT to maintain each endowed gift as a permanent fund, preserve its purchasing power, and spend it as directed by its original donor. Most of the endowment’s funds are restricted and must be used for a specific purpose. MIT uses the bulk of the income these endowed gifts generate to support financial aid, research, and education.
The endowment supports 50 percent of undergraduate tuition, helping to enable the Institute’s need-blind undergraduate admissions policy, which ensures that an MIT education is accessible to all qualified candidates regardless of financial resources. MIT works closely with all families of undergraduates who qualify for financial aid to develop an individual affordability plan tailored to their financial circumstances. In 2024-25, the average need-based MIT undergraduate scholarship was $62,127. Fifty-seven percent of MIT undergraduates received need-based financial aid, and 39 percent of MIT undergraduate students received scholarship funding from MIT and other sources sufficient to cover the total cost of tuition.
Effective in fiscal 2026, MIT enhanced undergraduate financial aid, ensuring that all families with incomes below $200,000 and typical assets have tuition fully covered by scholarships, and that families with incomes below $100,000 and typical assets pay nothing at all for their students’ MIT education. Eighty-eight percent of seniors who graduated in academic year 2025 graduated with no debt.
MITIMCo is a unit of MIT, created to manage and oversee the investment of the Institute’s endowment, retirement, and operating funds.
MIT’s Report of the Treasurer for fiscal year 2025, which details the Institute’s annual financial performance, was made available publicly today.
Ray Kurzweil ’70 reinforces his optimism in tech progressReceiving the Robert A. Muh award, the technologist and author heralded a bright future for AI, breakthroughs in longevity, and more.Innovator, futurist, and author Ray Kurzweil ’70 emphasized his optimism about artificial intelligence, and technological progress generally, in a lecture on Wednesday while accepting MIT’s Robert A. Muh Alumni Award from the School of Humanities, Arts, and Social Sciences (SHASS).
Kurzweil offered his signature high-profile forecasts about how AI and computing will entirely blend with human functionality, and proposed that AI will lead to monumental gains in longevity, medicine, and other realms of life.
“People do not appreciate that the rate of progress is accelerating,” Kurzweil said, forecasting “incredible breakthroughs” over the next two decades.
Kurzweil delivered his lecture, titled “Reinventing Intelligence,” in the Thomas Tull Concert Hall of the Edward and Joyce Linde Music Building, which opened earlier in 2025 on the MIT campus.
The Muh Award was founded and endowed by Robert A. Muh ’59 and his wife Berit, and is one of the leading alumni honors granted by SHASS and MIT. Muh, a life member emeritus of the MIT Corporation, established the award, which is granted every two years for “extraordinary contributions” by alumni in the humanities, arts, and social sciences.
Robert and Berit Muh were both present at the lecture, along with their daughter Carrie Muh ’96, ’97, SM ’97.
Agustín Rayo, dean of SHASS, offered introductory remarks, calling Kurzweil “one of the most prolific thinkers of our time.” Rayo added that Kurzweil “has built his life and career on the belief that ideas change the world, and change it for the better.”
Kurzweil has been an innovator in language recognition technologies, developing advances and founding companies that have served people who are blind or low-vision, and helped in music creation. He is also a best-selling author who has heralded advances in computing capabilities, and even the merging of human and machines.
The initial segment of Kurzweil’s lecture was autobiographical in focus, reflecting on his family and early years. The families of both of Kurzweil’s parents fled the Nazis in Europe, seeking refuge in the U.S., with the belief that people could create a brighter future for themselves.
“My parents taught me the power of ideas can really change the world,” Kurzweil said.
Showing an early interest in how things worked, Kurzweil had decided to become an inventor by about the age of 7, he recalled. He also described his mother as being tremendously encouraging to him as a child. The two would take walks together, and the young Kurzweil would talk about all the things he imagined inventing.
“I would tell her my ideas and no matter how fantastical they were, she believed them,” he said. “Now other parents might have simply chuckled … but she actually believed my ideas, and that actually gave me my confidence, and I think confidence is important in succeeding.”
He became interested in computing by the early 1960s and majored in both computer science and literature as an MIT undergraduate.
Kurzweil has a long-running association with MIT extending far beyond his undergraduate studies. He served as a member of the MIT Corporation from 2005 to 2012 and was the 2001 recipient of the $500,000 Lemelson-MIT Prize, an award for innovation, for his development of reading technology.
“MIT has played a major role in my personal and professional life over the years,” Kurzweil said, calling himself “truly honored to receive this award.” Addressing Muh, he added: “Your longstanding commitment to our alma mater is inspiring.”
After graduating from MIT, Kurzweil launched a successful career developing innovative computing products, including one that recognized text across all fonts and could produce an audio reading. He also developed leading-edge music synthesizers, among many other advances.
In a corresponding part of his career, Kurzweil has become an energetic author, whose best-known books include “The Age of Intelligent Machines” (1990), “The Age of Spiritual Machines” (1999), “The Singularity Is Near” (2005), and “The Singularity Is Nearer” (2024), among many others.
Kurzweil was recently named chief AI officer of Beyond Imagination, a robotics firm he co-founded; he has also held a position at Google in recent years, working on natural language technologies.
In his remarks, Kurzweil underscored his view that, as exemplified and enabled by the growth of computing power over time, technological innovation moves at an exponential pace.
“People don’t really think about exponential growth; they think about linear growth,” Kurzweil said.
This concept, he said, makes him confident that a string of innovations will continue at remarkable speed.
“One of the bigger transformations we’re going to see from AI in the near term is health and medicine,” Kurweil said, forecasting that human medical trials will be replaced by simulated “digital trials.”
Kurzweil also believes computing and AI advances can lead to so many medical advances it will soon produce a drastic improvement in human longevity.
“These incredible breakthroughs are going to lead to what we’ll call longevity escape velocity,” Kurzweil said. “By roughly 2032 when you live through a year, you’ll get back an entire year from scientific progress, and beyond that point you’ll get back more than a year for every year you live, so you’ll be going back into time as far as your health is concerned,” Kurweil said. He did offer that these advances will “start” with people who are the most diligent about their health.
Kurzweil also outlined one of his best-known forecasts, that AI and people will be combined. “As we move forward, the lines between humans and technology will blur, until we are … one and the same,” Kurzweil said. “This is how we learn to merge with AI. In the 2030s, robots the size of molecules will go into our brains, noninvasively, through the capillaries, and will connect our brains directly to the cloud. Think of it like having a phone, but in your brain.”
“By 2045, once we have fully merged with AI, our intelligence will no longer be constrained … it will expand a millionfold,” he said. “This is what we call the singularity.”
To be sure, Kurzweil acknowledged, “Technology has always been a double-edged sword,” given that a drone can deliver either medical supplies or weaponry. “Threats of AI are real, must be taken seriously, [and] I think we are doing that,” he said. In any case, he added, we have “a moral imperative to realize the promise of new technologies while controlling the peril.” He concluded: “We are not doomed to fail to control any of these risks.”
Gene-Wei Li named associate head of the Department of BiologyThe associate professor aims to help the department continue to be a worldwide leader in education, biological sciences, and fundamental research.Associate Professor Gene-Wei Li has accepted the position of associate head of the MIT Department of Biology, starting in the 2025-26 academic year.
Li, who has been a member of the department since 2015, brings a history of departmental leadership, service, and research and teaching excellence to his new role. He has received many awards, including a Sloan Research Fellowship (2016), an NSF Career Award (2019), Pew and Searle scholarships, and MIT’s Committed to Caring Award (2020). In 2024, he was appointed as a Howard Hughes Medical Institute (HHMI) Investigator.
“I am grateful to Gene-Wei for joining the leadership team,” says department head Amy E. Keating, the Jay A. Stein (1968) Professor of Biology and professor of biological engineering. “Gene will be a key leader in our educational initiatives, both digital and residential, and will be a critical part of keeping our department strong and forward-looking.”
A great environment to do science
Li says he was inspired to take on the role in part because of the way MIT Biology facilitates career development during every stage — from undergraduate and graduate students to postdocs and junior faculty members, as he was when he started in the department as an assistant professor just 10 years ago.
“I think we all benefit a lot from our environment, and I think this is a great environment to do science and educate people, and to create a new generation of scientists,” he says. “I want us to keep doing well, and I’m glad to have the opportunity to contribute to this effort.”
As part of his portfolio as associate department head, Li will continue in the role of scientific director of the Koch Biology Building, Building 68. In the last year, the previous scientific director, Stephen Bell, Uncas and Helen Whitaker Professor of Biology and HHMI Investigator, has continued to provide support and ensured a steady ramp-up, transitioning Li into his new duties. The building, which opened its doors in 1994, is in need of a slate of updates and repairs.
Although Li will be managing more administrative duties, he has provided a stable foundation for his lab to continue its interdisciplinary work on the quantitative biology of gene expression, parsing the mechanisms by which cells control the levels of their proteins and how this enables cells to perform their functions. His recent work includes developing a method that leverages the AI tool AlphaFold to predict whether protein fragments can recapitulate the native interactions of their full-length counterparts.
“I’m still very heavily involved, and we have a lab environment where everyone helps each other. It’s a team, and so that helps elevate everyone,” he says. “It’s the same with the whole building: nobody is working by themselves, so the science and administrative parts come together really nicely.”
Teaching for the future
Li is considering how the department can continue to be a global leader in biological sciences while navigating the uncertainty surrounding academia and funding, as well as the likelihood of reduced staff support and tightening budgets.
“The question is: How do you maintain excellence?” Li says. “That involves recruiting great people and giving them the resources that they need, and that’s going to be a priority within the limitations that we have to work with.”
Li will also be serving as faculty advisor for the MIT Biology Teaching and Learning Group, headed by Mary Ellen Wiltrout, and will serve on the Department of Biology Digital Learning Committee and the new Open Learning Biology Advisory Committee. Li will serve in the latter role in order to represent the department and work with new faculty member and HHMI Investigator Ron Vale on Institute-level online learning initiatives. Li will also chair the Biology Academic Planning Committee, which will help develop a longer-term outlook on faculty teaching assignments and course offerings.
Li is looking forward to hearing from faculty and students about the way the Institute teaches, and how it could be improved, both for the students on campus and for the online learners from across the world.
“There are a lot of things that are changing; what are the core fundamentals that the students need to know, what should we teach them, and how should we teach them?”
Although the commitment to teaching remains unchanged, there may be big transitions on the horizon. With two young children in school, Li is all too aware that the way that students learn today is very different from what he grew up with, and also very different from how students were learning just five or 10 years ago — writing essays on a computer, researching online, using AI tools, and absorbing information from media like short-form YouTube videos.
“There’s a lot of appeal to a shorter format, but it’s very different from the lecture-based teaching style that has worked for a long time,” Li says. “I think a challenge we should and will face is figuring out the best way to communicate the core fundamentals, and adapting our teaching styles to the next generation of students.”
Ultimately, Li is excited about balancing his research goals along with joining the department’s leadership team, and knows he can look to his fellow researchers in Building 68 and beyond for support.
“I’m privileged to be working with a great group of colleagues who are all invested in these efforts,” Li says. “Different people may have different ways of doing things, but we all share the same mission.”
Immune-informed brain aging research offers new treatment possibilities, speakers saySpeakers at MIT’s Aging Brain Initiative symposium described how immune system factors during aging contribute to Alzheimer’s, Parkinson’s and other conditions. The field is leveraging that knowledge to develop new therapies.Understanding how interactions between the central nervous system and the immune system contribute to problems of aging, including Alzheimer’s disease, Parkinson’s disease, arthritis, and more, can generate new leads for therapeutic development, speakers said at MIT’s symposium “The Neuro-Immune Axis and the Aging Brain” on Sept 18.
“The past decade has brought rapid progress in our understanding of how adaptive and innate immune systems impact the pathogenesis of neurodegenerative disorders,” said Picower Professor Li-Huei Tsai, director of The Picower Institute for Learning and Memory and MIT’s Aging Brain Initiative (ABI), in her introduction to the event, which more than 450 people registered to attend. “Together, today’s speakers will trace how the neuro-immune axis shapes brain health and disease … Their work converges on the promise of immunology-informed therapies to slow or prevent neurodegeneration and age-related cognitive decline.”
For instance, keynote speaker Michal Schwartz of the Weizmann Institute in Israel described her decades of pioneering work to understand the neuro-immune “ecosystem.” Immune cells, she said, help the brain heal, and support many of its functions, including its “plasticity,” the ability it has to adapt to and incorporate new information. But Schwartz’s lab also found that an immune signaling cascade can arise with aging that undermines cognitive function. She has leveraged that insight to investigate and develop corrective immunotherapies that improve the brain’s immune response to Alzheimer’s both by rejuvenating the brain’s microglia immune cells and bringing in the help of peripheral immune cells called macrophages. Schwartz has brought the potential therapy to market as the chief science officer of ImmunoBrain, a company testing it in a clinical trial.
In her presentation, Tsai noted recent work from her lab and that of computer science professor and fellow ABI member Manolis Kellis showing that many of the genes associated with Alzheimer’s disease are most strongly expressed in microglia, giving it an expression profile more similar to autoimmune disorders than to many psychiatric ones (where expression of disease-associated genes typically is highest in neurons). The study showed that microglia become “exhausted” over the course of disease progression, losing their cellular identity and becoming harmfully inflammatory.
“Genetic risk, epigenomic instability, and microglia exhaustion really play a central role in Alzheimer’s disease,” Tsai said, adding that her lab is now also looking into how immune T cells, recruited by microglia, may also contribute to Alzheimer’s disease progression.
The body and the brain
The neuro-immune “axis” connects not only the nervous and immune systems, but also extends between the whole body and the brain, with numerous implications for aging. Several speakers focused on the key conduit: the vagus nerve, which runs from the brain to the body’s major organs.
For instance, Sara Prescott, an investigator in the Picower Institute and an MIT assistant professor of biology, presented evidence her lab is amassing that the brain’s communication via vagus nerve terminals in the body’s airways is crucial for managing the body’s defense of respiratory tissues. Given that we inhale about 20,000 times a day, our airways are exposed to many environmental challenges, Prescott noted, and her lab and others are finding that the nervous system interacts directly with immune pathways to mount physiological responses. But vagal reflexes decline in aging, she noted, increasing susceptibility to infection, and so her lab is now working in mouse models to study airway-to-brain neurons throughout the lifespan to better understand how they change with aging.
In his talk, Caltech Professor Sarkis Mazmanian focused on work in his lab linking the gut microbiome to Parkinson’s disease (PD), for instance by promoting alpha-synuclein protein pathology and motor problems in mouse models. His lab hypothesizes that the microbiome can nucleate alpha-synuclein in the gut via a bacterial amyloid protein that may subsequently promote pathology in the brain, potentially via the vagus nerve. Based on its studies, the lab has developed two interventions. One is giving alpha-synuclein overexpressing mice a high-fiber diet to increase short-chain fatty acids in their gut, which actually modulates the activity of microglia in the brain. The high-fiber diet helps relieve motor dysfunction, corrects microglia activity, and reduces protein pathology, he showed. Another is a drug to disrupt the bacterial amyloid in the gut. It prevents alpha synuclein formation in the mouse brain and ameliorates PD-like symptoms. These results are pending publication.
Meanwhile, Kevin Tracey, professor at Hofstra University and Northwell Health, took listeners on a journey up and down the vagus nerve to the spleen, describing how impulses in the nerve regulate immune system emissions of signaling molecules, or “cytokines.” Too great a surge can become harmful, for instance causing the autoimmune disorder rheumatoid arthritis. Tracey described how a newly U.S. Food and Drug Administration-approved pill-sized neck implant to stimulate the vagus nerve helps patients with severe forms of the disease without suppressing their immune system.
The brain’s border
Other speakers discussed opportunities for understanding neuro-immune interactions in aging and disease at the “borders” where the brain’s and body’s immune system meet. These areas include the meninges that surround the brain, the choroid plexus (proximate to the ventricles, or open spaces, within the brain), and the interface between brain cells and the circulatory system.
For instance, taking a cue from studies showing that circadian disruptions are a risk factor for Alzheimer’s disease, Harvard Medical School Professor Beth Stevens of Boston Children’s Hospital described new research in her lab that examined how brain immune cells may function differently around the day-night cycle. The project, led by newly minted PhD Helena Barr, found that “border-associated macrophages” — long-lived immune cells residing in the brain’s borders — exhibited circadian rhythms in gene expression and function. Stevens described how these cells are tuned by the circadian clock to “eat” more during the rest phase, a process that may help remove material draining from the brain, including Alzheimer’s disease-associated peptides such as amyloid-beta. So, Stevens hypothesizes, circadian disruptions, for example due to aging or night-shift work, may contribute to disease onset by disrupting the delicate balance in immune-mediated “clean-up” of the brain and its borders.
Following Stevens at the podium, Washington University Professor Marco Colonna traced how various kinds of macrophages, including border macrophages and microglia, develop from the embryonic stage. He described the different gene-expression programs that guide their differentiation into one type or another. One gene he highlighted, for instance, is necessary for border macrophages along the brain’s vasculature to help regulate the waste-clearing cerebrospinal fluid (CSF) flow that Stevens also discussed. Knocking out the gene also impairs blood flow. Importantly, his lab has found that versions of the gene may be somewhat protective against Alzheimer’s, and that regulating expression of the gene could be a therapeutic strategy.
Colonna’s WashU colleague Jonathan Kipnis (a former student of Schwartz) also discussed macrophages that are associated with the particular border between brain tissue and the plumbing alongside the vasculature that carries CSF. The macrophages, his lab showed in 2022, actively govern the flow of CSF. He showed that removing the macrophages let Alzheimer’s proteins accumulate in mice. His lab is continuing to investigate ways in which these specific border macrophages may play roles in disease. He’s also looking in separate studies of how the skull’s brain marrow contributes to the population of immune cells in the brain and may play a role in neurodegeneration.
For all the talk of distant organs and the brain’s borders, neurons themselves were never far from the discussion. Harvard Medical School Professor Isaac Chiu gave them their direct due in a talk focusing on how they participate in their own immune defense, for instance by directly sensing pathogens and giving off inflammation signals upon cell death. He discussed a key molecule in that latter process, which is expressed among neurons all over the brain.
Whether they were looking within the brain, at its border, or throughout the body, speakers showed that age-related nervous system diseases are not only better understood but also possibly better treated by accounting not only for the nerve cells, but their immune system partners.
MIT Schwarzman College of Computing and MBZUAI launch international collaboration to shape the future of AIThe MIT–MBZUAI Collaborative Research Program will unite faculty and students from both institutions to advance AI and accelerate its use in pressing scientific and societal challenges.The MIT Schwarzman College of Computing and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) recently celebrated the launch of the MIT–MBZUAI Collaborative Research Program, a new effort to strengthen the building blocks of artificial intelligence and accelerate its use in pressing scientific and societal challenges.
Under the five-year agreement, faculty, students, and research staff from both institutions will collaborate on fundamental research projects to advance the technological foundations of AI and its applications in three core areas: scientific discovery, human thriving, and the health of the planet.
“Artificial intelligence is transforming nearly every aspect of human endeavor. MIT’s leadership in AI is greatly enriched through collaborations with leading academic institutions in the U.S. and around the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our collaboration with MBZUAI reflects a shared commitment to advancing AI in ways that are responsible, inclusive, and globally impactful. Together, we can explore new horizons in AI and bring broad benefits to society.”
“This agreement will unite the efforts of researchers at two world-class institutions to advance frontier AI research across scientific discovery, human thriving, and the health of the planet. By combining MBZUAI’s focus on foundational models and real-world deployment with MIT’s depth in computing and interdisciplinary innovation, we are creating a transcontinental bridge for discovery. Together, we will not only expand the boundaries of AI science, but also ensure that these breakthroughs are pursued responsibly and applied where they matter most — improving human health, enabling intelligent robotics, and driving sustainable AI at scale,” says Eric Xing, president and university professor at MBZUAI.
Each institution has appointed an academic director to oversee the program on its campus. At MIT, Philip Isola, the Class of 1948 Career Development Professor in the Department of Electrical Engineering and Computer Science, will serve as program lead. At MBZUAI, Le Song, professor of machine learning, will take on the role.
Supported by MBZUAI — the first university dedicated entirely to advancing science through AI, and based in Abu Dhabi, U.A.E. — the collaboration will fund a number of joint research projects per year. The findings will be openly publishable, and each project will be led by a principal investigator from MIT and one from MBZUAI, with project selections made by a steering committee composed of representatives from both institutions.
MIT associate professor of physics Riccardo Comin has been selected as 2025 Experimental Physics Investigator by the Gordon and Betty Moore Foundation. Two MIT physics alumni — Gyu-Boong Jo PhD ’10 of Rice University, and Ben Jones PhD ’15 of the University of Texas at Arlington — were also among this year’s cohort of 22 honorees.
The prestigious Experimental Physics Investigators (EPI) Initiative recognizes mid-career scientists advancing the frontiers of experimental physics. Each award provides $1.3 million over five years to accelerate breakthroughs and strengthen the experimental physics community.
At MIT, Comin investigates magnetoelectric multiferroics by engineering interfaces between two-dimensional materials and three-dimensional oxide thin films. His research aims to overcome long-standing limitations in spin-charge coupling by moving beyond epitaxial constraints, enabling new interfacial phases and coupling mechanisms. In these systems, Comin’s team explores the coexistence and proximity of magnetic and ferroelectric order, with a focus on achieving strong magnetoelectric coupling. This approach opens new pathways for designing tunable multiferroic systems unconstrained by traditional synthesis methods.
Comin’s research expands the frontier of multiferroics by demonstrating stacking-controlled magnetoelectric coupling at 2D–3D interfaces. This approach enables exploration of fundamental physics in a versatile materials platform and opens new possibilities for spintronics, sensing, and data storage. By removing constraints of epitaxial growth, Comin’s work lays the foundation for microelectronic and spintronic devices with novel functionalities driven by interfacial control of spin and polarization.
Comin’s project, Interfacial MAGnetoElectrics (I-MAGinE), aims to study a new class of artificial magnetoelectric multiferroics at the interfaces between ferroic materials from 2D van der Waals systems and 3D oxide thin films. The team aims to identify and understand novel magnetoelectric effects to demonstrate the viability of stacking-controlled interfacial magnetoelectric coupling. This research could lead to significant contributions in multiferroics, and could pave the way for innovative, energy-efficient storage devices.
“This research has the potential to make significant contributions to the field of multiferroics by demonstrating the viability of stacking-controlled interfacial magnetoelectric coupling,” according to Comin’s proposal. “The findings could pave the way for future applications in spintronics, data storage, and sensing. It offers a significant opportunity to explore fundamental physics questions in a novel materials platform, while laying the ground for future technological applications, including microelectronic and spintronic devices with new functionalities.”
Comin’s group has extensive experience in researching 2D and 3D ferroic materials and electronically ordered oxide thin films, as well as ultrathin van der Waals magnets, ferroelectrics, and multiferroics. Their lab is equipped with state-of-the-art tools for material synthesis, including bulk crystal growth of van der Waals materials and pulsed laser deposition targets, along with comprehensive fabrication and characterization capabilities. Their expertise in magneto-optical probes and advanced magnetic X-ray techniques promises to enable in-depth studies of electronic and magnetic structures, specifically spin-charge coupling, in order to contribute significantly to understanding spin-charge coupling in magnetochiral materials.
The coexistence of ferroelectricity and ferromagnetism in a single material, known as multiferroicity, is rare, and strong spin-charge coupling is even rarer due to fundamental chemical and electronic structure incompatibilities.
The few known bulk multiferroics with strong magnetoelectric coupling generally rely on inversion symmetry-breaking spin arrangements, which only emerge at low temperatures, limiting practical applications. While interfacial magnetoelectric multiferroics offer an alternative, achieving efficient spin-charge coupling often requires stringent conditions like epitaxial growth and lattice matching, which limit material combinations. This research proposes to overcome these limitations by using non-epitaxial interfaces of 2D van der Waals materials and 3D oxide thin films.
Unique features of this approach include leveraging the versatility of 2D ferroics for seamless transfer onto any substrate, eliminating lattice matching requirements, and exploring new classes of interfacial magnetoelectric effects unconstrained by traditional thin-film synthesis limitations.
Launched in 2018, the Moore Foundation’s EPI Initiative cultivates collaborative research environments and provides research support to promote the discovery of new ideas and emphasize community building.
“We have seen numerous new connections form and new research directions pursued by both individuals and groups based on conversations at these gatherings,” says Catherine Mader, program officer for the initiative.
The Gordon and Betty Moore Foundation was established to create positive outcomes for future generations. In pursuit of that vision, it advances scientific discovery, environmental conservation, and the special character of the San Francisco Bay Area.
How to reduce greenhouse gas emissions from ammonia productionProposed system would combine two kinds of plants, creating greater efficiency and lowering costs while curbing climate-changing emissions.Ammonia is one of the most widely produced chemicals in the world, used mostly as fertilizer, but also for the production of some plastics, textiles, and other applications. Its production, through processes that require high heat and pressure, accounts for up to 20 percent of all the greenhouse gases from the entire chemical industry, so efforts have been underway worldwide to find ways to reduce those emissions.
Now, researchers at MIT have come up with a clever way of combining two different methods of producing the compound that minimizes waste products, that, when combined with some other simple upgrades, could reduce the greenhouse emissions from production by as much as 63 percent, compared to the leading “low-emissions” approach being used today.
The new approach is described in the journal Energy & Fuels, in a paper by MIT Energy Initiative (MITEI) Director William H. Green, graduate student Sayandeep Biswas, MITEI Director of Research Randall Field, and two others.
“Ammonia has the most carbon dioxide emissions of any kind of chemical,” says Green, who is the Hoyt C. Hottel Professor in Chemical Engineering. “It’s a very important chemical,” he says, because its use as a fertilizer is crucial to being able to feed the world’s population.
Until late in the 19th century, the most widely used source of nitrogen fertilizer was mined deposits of bat or bird guano, mostly from Chile, but that source was beginning to run out, and there were predictions that the world would soon be running short of food to sustain the population. But then a new chemical process, called the Haber-Bosch process after its inventors, made it possible to make ammonia out of nitrogen from the air and hydrogen, which was mostly derived from methane. But both the burning of fossil fuels to provide the needed heat and the use of methane to make the hydrogen led to massive climate-warming emissions from the process.
To address this, two newer variations of ammonia production have been developed: so-called “blue ammonia,” where the greenhouse gases are captured right at the factory and then sequestered deep underground, and “green ammonia,” produced by a different chemical pathway, using electricity instead of fossil fuels to hydrolyze water to make hydrogen.
Blue ammonia is already beginning to be used, with a few plants operating now in Louisiana, Green says, and the ammonia mostly being shipped to Japan, “so that’s already kind of commercial.” Other parts of the world are starting to use green ammonia, especially in places that have lots of hydropower, solar, or wind to provide inexpensive electricity, including a giant plant now under construction in Saudi Arabia.
But in most places, both blue and green ammonia are still more expensive than the traditional fossil-fuel-based version, so many teams around the world have been working on ways to cut these costs as much as possible so that the difference is small enough to be made up through tax subsidies or other incentives.
The problem is growing, because as the population grows, and as wealth increases, there will be ever-increasing demands for nitrogen fertilizer. At the same time, ammonia is a promising substitute fuel to power hard-to-decarbonize transportation such as cargo ships and heavy trucks, which could lead to even greater needs for the chemical.
“It definitely works” as a transportation fuel, by powering fuel cells that have been demonstrated for use by everything from drones to barges and tugboats and trucks, Green says. “People think that the most likely market of that type would be for shipping,” he says, “because the downside of ammonia is it’s toxic and it’s smelly, and that makes it slightly dangerous to handle and to ship around.” So its best uses may be where it’s used in high volume and in relatively remote locations, like the high seas. In fact, the International Maritime Organization will soon be voting on new rules that might give a strong boost to the ammonia alternative for shipping.
The key to the new proposed system is to combine the two existing approaches in one facility, with a blue ammonia factory next to a green ammonia factory. The process of generating hydrogen for the green ammonia plant leaves a lot of leftover oxygen that just gets vented to the air. Blue ammonia, on the other hand, uses a process called autothermal reforming that requires a source of pure oxygen, so if there’s a green ammonia plant next door, it can use that excess oxygen.
“Putting them next to each other turns out to have significant economic value,” Green says. This synergy could help hybrid “blue-green ammonia” facilities serve as an important bridge toward a future where eventually green ammonia, the cleanest version, could finally dominate. But that future is likely decades away, Green says, so having the combined plants could be an important step along the way.
“It might be a really long time before [green ammonia] is actually attractive” economically, he says. “Right now, it’s nowhere close, except in very special situations.” But the combined plants “could be a really appealing concept, and maybe a good way to start the industry,” because so far only small, standalone demonstration plants of the green process are being built.
“If green or blue ammonia is going to become the new way of making ammonia, you need to find ways to make it relatively affordable in a lot of countries, with whatever resources they’ve got,” he says. This new proposed combination, he says, “looks like a really good idea that can help push things along. Ultimately, there’s got to be a lot of green ammonia plants in a lot of places,” and starting out with the combined plants, which could be more affordable now, could help to make that happen. The team has filed for a patent on the process.
Although the team did a detailed study of both the technology and the economics that show the system has great promise, Green points out that “no one has ever built one. We did the analysis, it looks good, but surely when people build the first one, they’ll find funny little things that need some attention,” such as details of how to start up or shut down the process. “I would say there’s plenty of additional work to do to make it a real industry.” But the results of this study, which shows the costs to be much more affordable than existing blue or green plants in isolation, “definitely encourages the possibility of people making the big investments that would be needed to really make this industry feasible.”
This proposed integration of the two methods “improves efficiency, reduces greenhouse gas emissions, and lowers overall cost,” says Kevin van Geem, a professor in the Center for Sustainable Chemistry at Ghent University, who was not associated with this research. “The analysis is rigorous, with validated process models, transparent assumptions, and comparisons to literature benchmarks. By combining techno-economic analysis with emissions accounting, the work provides a credible and balanced view of the trade-offs.”
He adds that, “given the scale of global ammonia production, such a reduction could have a highly impactful effect on decarbonizing one of the most emissions-intensive chemical industries.”
The research team also included MIT postdoc Angiras Menon and MITEI research lead Guiyan Zang. The work was supported by IHI Japan through the MIT Energy Initiative and the Martin Family Society of Fellows for Sustainability.
Using generative AI to diversify virtual training grounds for robotsNew tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of this versatility? Billions, or even trillions, of textual data points across the internet.
Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics), or tediously handcrafting each digital environment from scratch.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Toyota Research Institute may have found a way to create the diverse, realistic training grounds robots need. Their “steerable scene generation” approach creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world interactions and scenarios. Trained on over 44 million 3D rooms filled with models of objects such as tables and plates, the tool places existing assets in new scenes, then refines each one into a physically accurate, lifelike environment.
Steerable scene generation creates these 3D worlds by “steering” a diffusion model — an AI system that generates a visual from random noise — toward a scene you’d find in everyday life. The researchers used this generative system to “in-paint” an environment, filling in particular elements throughout the scene. You can imagine a blank canvas suddenly turning into a kitchen scattered with 3D objects, which are gradually rearranged into a scene that imitates real-world physics. For example, the system ensures that a fork doesn’t pass through a bowl on a table — a common glitch in 3D graphics known as “clipping,” where models overlap or intersect.
How exactly steerable scene generation guides its creation toward realism, however, depends on the strategy you choose. Its main strategy is “Monte Carlo tree search” (MCTS), where the model creates a series of alternative scenes, filling them out in different ways toward a particular objective (like making a scene more physically realistic, or including as many edible items as possible). It’s used by the AI program AlphaGo to beat human opponents in Go (a game similar to chess), as the system considers potential sequences of moves before choosing the most advantageous one.
“We are the first to apply MCTS to scene generation by framing the scene generation task as a sequential decision-making process,” says MIT Department of Electrical Engineering and Computer Science (EECS) PhD student Nicholas Pfaff, who is a CSAIL researcher and a lead author on a paper presenting the work. “We keep building on top of partial scenes to produce better or more desired scenes over time. As a result, MCTS creates scenes that are more complex than what the diffusion model was trained on.”
In one particularly telling experiment, MCTS added the maximum number of objects to a simple restaurant scene. It featured as many as 34 items on a table, including massive stacks of dim sum dishes, after training on scenes with only 17 objects on average.
Steerable scene generation also allows you to generate diverse training scenarios via reinforcement learning — essentially, teaching a diffusion model to fulfill an objective by trial-and-error. After you train on the initial data, your system undergoes a second training stage, where you outline a reward (basically, a desired outcome with a score indicating how close you are to that goal). The model automatically learns to create scenes with higher scores, often producing scenarios that are quite different from those it was trained on.
Users can also prompt the system directly by typing in specific visual descriptions (like “a kitchen with four apples and a bowl on the table”). Then, steerable scene generation can bring your requests to life with precision. For example, the tool accurately followed users’ prompts at rates of 98 percent when building scenes of pantry shelves, and 86 percent for messy breakfast tables. Both marks are at least a 10 percent improvement over comparable methods like “MiDiffusion” and “DiffuScene.”
The system can also complete specific scenes via prompting or light directions (like “come up with a different scene arrangement using the same objects”). You could ask it to place apples on several plates on a kitchen table, for instance, or put board games and books on a shelf. It’s essentially “filling in the blank” by slotting items in empty spaces, but preserving the rest of a scene.
According to the researchers, the strength of their project lies in its ability to create many scenes that roboticists can actually use. “A key insight from our findings is that it’s OK for the scenes we pre-trained on to not exactly resemble the scenes that we actually want,” says Pfaff. “Using our steering methods, we can move beyond that broad distribution and sample from a ‘better’ one. In other words, generating the diverse, realistic, and task-aligned scenes that we actually want to train our robots in.”
Such vast scenes became the testing grounds where they could record a virtual robot interacting with different items. The machine carefully placed forks and knives into a cutlery holder, for instance, and rearranged bread onto plates in various 3D settings. Each simulation appeared fluid and realistic, resembling the real-world, adaptable robots steerable scene generation could help train, one day.
While the system could be an encouraging path forward in generating lots of diverse training data for robots, the researchers say their work is more of a proof of concept. In the future, they’d like to use generative AI to create entirely new objects and scenes, instead of using a fixed library of assets. They also plan to incorporate articulated objects that the robot could open or twist (like cabinets or jars filled with food) to make the scenes even more interactive.
To make their virtual environments even more realistic, Pfaff and his colleagues may incorporate real-world objects by using a library of objects and scenes pulled from images on the internet and using their previous work on “Scalable Real2Sim.” By expanding how diverse and lifelike AI-constructed robot testing grounds can be, the team hopes to build a community of users that’ll create lots of data, which could then be used as a massive dataset to teach dexterous robots different skills.
“Today, creating realistic scenes for simulation can be quite a challenging endeavor; procedural generation can readily produce a large number of scenes, but they likely won’t be representative of the environments the robot would encounter in the real world. Manually creating bespoke scenes is both time-consuming and expensive,” says Jeremy Binagia, an applied scientist at Amazon Robotics who wasn’t involved in the paper. “Steerable scene generation offers a better approach: train a generative model on a large collection of pre-existing scenes and adapt it (using a strategy such as reinforcement learning) to specific downstream applications. Compared to previous works that leverage an off-the-shelf vision-language model or focus just on arranging objects in a 2D grid, this approach guarantees physical feasibility and considers full 3D translation and rotation, enabling the generation of much more interesting scenes.”
“Steerable scene generation with post training and inference-time search provides a novel and efficient framework for automating scene generation at scale,” says Toyota Research Institute roboticist Rick Cory SM ’08, PhD ’10, who also wasn’t involved in the paper. “Moreover, it can generate ‘never-before-seen’ scenes that are deemed important for downstream tasks. In the future, combining this framework with vast internet data could unlock an important milestone towards efficient training of robots for deployment in the real world.”
Pfaff wrote the paper with senior author Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT; a senior vice president of large behavior models at the Toyota Research Institute; and CSAIL principal investigator. Other authors were Toyota Research Institute robotics researcher Hongkai Dai SM ’12, PhD ’16; team lead and Senior Research Scientist Sergey Zakharov; and Carnegie Mellon University PhD student Shun Iwase. Their work was supported, in part, by Amazon and the Toyota Research Institute. The researchers presented their work at the Conference on Robot Learning (CoRL) in September.
Every time you check the time on your phone, make an online transaction, or use a navigation app, you are depending on the precision of atomic clocks.
An atomic clock keeps time by relying on the “ticks” of atoms as they naturally oscillate at rock-steady frequencies. Today’s atomic clocks operate by tracking cesium atoms, which tick over 10 billion times per second. Each of those ticks is precisely tracked using lasers that oscillate in sync, at microwave frequencies.
Scientists are developing next-generation atomic clocks that rely on even faster-ticking atoms such as ytterbium, which can be tracked with lasers at higher, optical frequencies. If they can be kept stable, optical atomic clocks could track even finer intervals of time, up to 100 trillion times per second.
Now, MIT physicists have found a way to improve the stability of optical atomic clocks, by reducing “quantum noise” — a fundamental measurement limitation due to the effects of quantum mechanics, which obscures the atoms’ pure oscillations. In addition, the team discovered that an effect of a clock’s laser on the atoms, previously considered irrelevant, can be used to further stabilize the laser.
The researchers developed a method to harness a laser-induced “global phase” in ytterbium atoms, and have boosted this effect with a quantum-amplification technique. The new approach doubles the precision of an optical atomic clock, enabling it to discern twice as many ticks per second compared to the same setup without the new method. What’s more, they anticipate that the precision of the method should increase steadily with the number of atoms in an atomic clock.
The researchers detail the method, which they call global phase spectroscopy, in a study appearing today in the journal Nature. They envision that the clock-stabilizing technique could one day enable portable optical atomic clocks that can be transported to various locations to measure all manner of phenomena.
“With these clocks, people are trying to detect dark matter and dark energy, and test whether there really are just four fundamental forces, and even to see if these clocks can predict earthquakes,” says study author Vladan Vuletić, the Lester Wolfe Professor of Physics at MIT. “We think our method can help make these clocks transportable and deployable to where they’re needed.”
The paper’s co-authors are Leon Zaporski, Qi Liu, Gustavo Velez, Matthew Radzihovsky, Zeyang Li, Simone Colombo, and Edwin Pedrozo-Peñafiel, who are members of the MIT-Harvard Center for Ultracold Atoms and the MIT Research Laboratory of Electronics.
Ticking time
In 2020, Vuletić and his colleagues demonstrated that an atomic clock could be made more precise by quantumly entangling the clock’s atoms. Quantum entanglement is a phenomenon by which particles can be made to behave in a collective, highly correlated manner. When atoms are quantumly entangled, they redistribute any noise, or uncertainty in measuring the atoms’ oscillations, in a way that reveals a clearer, more measurable “tick.”
In their previous work, the team induced quantum entanglement among several hundred ytterbium atoms that they first cooled and trapped in a cavity formed by two curved mirrors. They sent a laser into the cavity, which bounced thousands of times between the mirrors, interacting with the atoms and causing the ensemble to entangle. They were able to show that quantum entanglement could improve the precision of existing atomic clocks by essentially reducing the noise, or uncertainty between the laser’s and atoms’ tick rates.
At the time, however, they were limited by the ticking instability of the clock’s laser. In 2022, the same team derived a way to further amplify the difference in laser versus atom tick rates with “time reversal” — a trick that relies on entangling and de-entangling the atoms to boost the signal acquired in between.
However, in that work the team was still using traditional microwaves, which oscillate at much lower frequencies than the optical frequency standards ytterbium atoms can provide. It was as if they had painstakingly lifted a film of dust off a painting, only to then photograph it with a low-resolution camera.
“When you have atoms that tick 100 trillion times per second, that’s 10,000 times faster than the frequency of microwaves,” Vuletić says. “We didn’t know at the time how to apply these methods to higher-frequency optical clocks that are much harder to keep stable.”
About phase
In their new study, the team has found a way to apply their previously developed approach of time reversal to optical atomic clocks. They then sent in a laser that oscillates near the optical frequency of the entangled atoms.
“The laser ultimately inherits the ticking of the atoms,” says first author Zaporski. “But in order for this inheritance to hold for a long time, the laser has to be quite stable.”
The researchers found they were able to improve the stability of an optical atomic clock by taking advantage of a phenomenon that scientists had assumed was inconsequential to the operation. They realized that when light is sent through entangled atoms, the interaction can cause the atoms to jump up in energy, then settle back down into their original energy state and still carry the memory about their round trip.
“One might think we’ve done nothing,” Vuletić says. “You get this global phase of the atoms, which is usually considered irrelevant. But this global phase contains information about the laser frequency.”
In other words, they realized that the laser was inducing a measurable change in the atoms, despite bringing them back to the original energy state, and that the magnitude of this change depends on the laser’s frequency.
“Ultimately, we are looking for the difference of laser frequency and the atomic transition frequency,” explains co-author Liu. “When that difference is small, it gets drowned by quantum noise. Our method amplifies this difference above this quantum noise.”
In their experiments, the team applied this new approach and found that through entanglement they were able to double the precision of their optical atomic clock.
“We saw that we can now resolve nearly twice as small a difference in the optical frequency or, the clock ticking frequency, without running into the quantum noise limit,” Zaporski says. “Although it’s a hard problem in general to run atomic clocks, the technical benefits of our method it will make it easier, and we think this can enable stable, transportable atomic clocks.”
This research was supported, in part, by the U.S. Office of Naval Research, the National Science Foundation, the U.S. Defense Advanced Research Projects Agency, the U.S. Department of Energy, the U.S. Office of Science, the National Quantum Information Science Research Centers, and the Quantum Systems Accelerator.
Uncovering new physics in metals manufacturingMIT researchers discovered a hidden atomic order that persists in metals even after extreme processing.For decades, it’s been known that subtle chemical patterns exist in metal alloys, but researchers thought they were too minor to matter — or that they got erased during manufacturing. However, recent studies have shown that in laboratory settings, these patterns can change a metal’s properties, including its mechanical strength, durability, heat capacity, radiation tolerance, and more.
Now, researchers at MIT have found that these chemical patterns also exist in conventionally manufactured metals. The surprising finding revealed a new physical phenomenon that explains the persistent patterns.
In a paper published in Nature Communications today, the researchers describe how they tracked the patterns and discovered the physics that explains them. The authors also developed a simple model to predict chemical patterns in metals, and they show how engineers could use the model to tune the effect of such patterns on metallic properties, for use in aerospace, semiconductors, nuclear reactors, and more.
“The conclusion is: You can never completely randomize the atoms in a metal. It doesn’t matter how you process it,” says Rodrigo Freitas, the TDK Assistant Professor in the Department of Materials Science and Engineering. “This is the first paper showing these non-equilibrium states that are retained in the metal. Right now, this chemical order is not something we’re controlling for or paying attention to when we manufacture metals.”
For Freitas, an early-career researcher, the findings offer vindication for exploring a crowded field that he says few believed would lead to unique or broadly impactful results. He credits the U.S. Air Force Office of Scientific Research, which supported the work through their Young Investigator Program. He also credits the collaborative effort that enabled the paper, which features three MIT PhD students as co-first authors: Mahmudul Islam, Yifan Cao, and Killian Sheriff.
“There was the question of whether I should even be tackling this specific problem because people have been working on it for a long time,” Freitas says. “But the more I learned about it, the more I saw researchers were thinking about this in idealized laboratory scenarios. We wanted to perform simulations that were as realistic as possible to reproduce these manufacturing processes with high fidelity. My favorite part of this project is how non-intuitive the findings are. The fact that you cannot completely mix something together, people didn’t see that coming.”
From surprises to theories
Freitas’ research team began with a practical question: How fast do chemical elements mix during metal processing? Conventional wisdom held that there’s a point where the chemical composition of metals becomes completely uniform from mixing during manufacturing. By finding that point, the researchers thought they could develop a simple way to design alloys with different levels of atomic order, also known as short-range order.
The researchers used machine-learning techniques to track millions of atoms as they moved and rearranged themselves under conditions that mimicked metal processing.
“The first thing we did was to deform a piece of metal,” Freitas explains. “That’s a common step during manufacturing: You roll the metal and deform it and heat it up again and deform it a little more, so it develops the structure you want. We did that and we tracked chemical order. The thought was as you deform the material, its chemical bonds are broken and that randomizes the system. These violent manufacturing processes essentially shuffle the atoms.”
The researchers hit a snag during the mixing process: The alloys never reached a fully random state. That was a surprise, because no known physical mechanism could explain the result.
“It pointed to a new piece of physics in metals,” the researchers write in the paper. “It was one of those cases where applied research led to a fundamental discovery.”
To uncover the new physics, the researchers developed computational tools, including high-fidelity machine-learning models, to capture atomic interactions, along with new statistical methods that quantify how chemical order changes over time. They then applied these tools in large-scale molecular dynamics simulations to track how atoms rearrange during processing.
The researchers found some standard chemical arrangements in their processed metals, but at higher temperatures than would normally be expected. Even more surprisingly, they found completely new chemical patterns never seen outside of manufacturing processes. This was the first time such patterns were observed. The researchers referred to the patterns as “far-from-equilibrium states.”
The researchers also built a simple model that reproduced key features of the simulations. The model explains how the chemical patterns arise from defects known as dislocations, which are like three-dimensional scribbles within a metal. As the metal is deformed, those scribbles warp, shuffling nearby atoms along the way. Previously, researchers believed that shuffling completely erased order in the metals, but they found that dislocations favor some atomic swaps over others, resulting not in randomness but in subtle patterns that explain their findings.
“These defects have chemical preferences that guide how they move,” Freitas says. “They look for low energy pathways, so given a choice between breaking chemical bonds, they tend to break the weakest bonds, and it’s not completely random. This is very exciting because it’s a non-equilibrium state: It’s not something you’d see naturally occurring in materials. It’s the same way our bodies live in non-equilibrium. The temperature outside is always hotter or colder than our bodies, and we’re maintaining that steady state equilibrium to stay alive. That’s why these states exist in metal: the balance between an internal push toward disorder plus this ordering tendency of breaking certain bonds that are always weaker than others.”
Applying a new theory
The researchers are now exploring how these chemical patterns develop across a wide range of manufacturing conditions. The result is a map that links various metal processing steps to different chemical patterns in metal.
To date, this chemical order and the properties they tune have been largely considered an academic subject. With this map, the researchers hope engineers can begin thinking of these patterns as levers in design that can be pulled during production to get new properties.
“Researchers have been looking at the ways these atomic arrangements change metallic properties — a big one is catalysis,” Freitas says of the process that drives chemical reactions. “Electrochemistry happens at the surface of the metal, and it’s very sensitive to local atomic arrangements. And there have been other properties that you wouldn't think would be influenced by these factors. Radiation damage is another big one. That affects these materials’ performance in nuclear reactors.”
Researchers have already told Freitas the paper could help explain other surprise findings about metallic properties, and he’s excited for the field to move from fundamental research into chemical order to more applied work.
“You can think of areas where you need very optimized alloys like aerospace,” Freitas says. “They care about very specific compositions. Advanced manufacturing now makes it possible to combine metals that normally wouldn’t mix through deformation. Understanding how atoms actually shuffle and mix in those processes is crucial, because it’s the key to gaining strength while still keeping the low density. So, this could be a huge deal for them.”
This work was supported, in part, by the U.S. Air Force Office of Scientific Research, MathWorks, and the MIT-Portugal Program.
Engineered “natural killer” cells could help fight cancerA new study identifies genetic modifications that make these immune cells, known as CAR-NK cells, more effective at destroying cancer cells.One of the newest weapons that scientists have developed against cancer is a type of engineered immune cell known as CAR-NK (natural killer) cells. Similar to CAR-T cells, these cells can be programmed to attack cancer cells.
MIT and Harvard Medical School researchers have now come up with a new way to engineer CAR-NK cells that makes them much less likely to be rejected by the patient’s immune system, which is a common drawback of this type of treatment.
The new advance may also make it easier to develop “off-the-shelf” CAR-NK cells that could be given to patients as soon as they are diagnosed. Traditional approaches to engineering CAR-NK or CAR-T cells usually take several weeks.
“This enables us to do one-step engineering of CAR-NK cells that can avoid rejection by host T cells and other immune cells. And, they kill cancer cells better and they’re safer,” says Jianzhu Chen, an MIT professor of biology, a member of the Koch Institute for Integrative Cancer Research,and one of the senior authors of the study.
In a study of mice with humanized immune systems, the researchers showed that these CAR-NK cells could destroy most cancer cells while evading the host immune system.
Rizwan Romee, an associate professor of medicine at Harvard Medical School and Dana-Farber Cancer Institute, is also a senior author of the paper, which appears today in Nature Communications. The paper’s lead author is Fuguo Liu, a postdoc at the Koch Institute and a research fellow at Dana-Farber.
Evading the immune system
NK cells are a critical part of the body’s natural immune defenses, and their primary responsibility is to locate and kill cancer cells and virus-infected cells. One of their cell-killing strategies, also used by T cells, is a process called degranulation. Through this process, immune cells release a protein called perforin, which can poke holes in another cell to induce cell death.
To create CAR-NK cells to treat cancer patients, doctors first take a blood sample from the patient. NK cells are isolated from the sample and engineered to express a protein called a chimeric antigen receptor (CAR), which can be designed to target specific proteins found on cancer cells.
Then, the cells spend several weeks proliferating until there are enough to transfuse back into the patient. A similar approach is also used to create CAR-T cells. Several CAR-T cell therapies have been approved to treat blood cancers such as lymphoma and leukemia, but CAR-NK treatments are still in clinical trials.
Because it takes so long to grow a population of engineered cells that can be infused into the patient, and those cells may not be as viable as cells that came from a healthy person, researchers are exploring an alternative approach: using NK cells from a healthy donor.
Such cells could be grown in large quantities and would be ready whenever they were needed. However, the drawback to these cells is that the recipient’s immune system may see them as foreign and attack them before they can start killing cancer cells.
In the new study, the MIT team set out to find a way to help NK cells “hide” from a patient’s immune system. Through studies of immune cell interactions, they showed that NK cells could evade a host T-cell response if they did not carry surface proteins called HLA class 1 proteins. These proteins, usually expressed on NK cell surfaces, can trigger T cells to attack if the immune system doesn’t recognize them as “self.”
To take advantage of this, the researchers engineered the cells to express a sequence of siRNA (short interfering RNA) that interferes with the genes for HLA class 1. They also delivered the CAR gene, as well as the gene for either PD-L1 or single-chain HLA-E (SCE). PD-L1 and SCE are proteins that make NK cells more effective by turning up genes that are involved in killing cancer cells.
All of these genes can be carried on a single piece of DNA, known as a construct, making it simple to transform donor NK cells into immune-evasive CAR-NK cells. The researchers used this construct to create CAR-NK cells targeting a protein called CD-19, which is often found on cancerous B cells in lymphoma patients.
NK cells unleashed
The researchers tested these CAR-NK cells in mice with a human-like immune system. These mice were also injected with lymphoma cells.
Mice that received CAR-NK cells with the new construct maintained the NK cell population for at least three weeks, and the NK cells were able to nearly eliminate cancer in those mice. In mice that received either NK cells with no genetic modifications or NK cells with only the CAR gene, the host immune cells attacked the donor NK cells. In these mice, the NK cells died out within two weeks, and the cancer spread unchecked.
The researchers also found that these engineered CAR-NK cells were much less likely to induce cytokine release syndrome — a common side effect of immunotherapy treatments, which can cause life-threatening complications.
Because of CAR-NK cells’ potentially better safety profile, Chen anticipates that they could eventually be used in place of CAR-T cells. For any CAR-NK cells that are now in development to target lymphoma or other types of cancer, it should be possible to adapt them by adding the construct developed in this study, he says.
The researchers now hope to run a clinical trial of this approach, working with colleagues at Dana-Farber. They are also working with a local biotech company to test CAR-NK cells to treat lupus, an autoimmune disorder that causes the immune system to attack healthy tissues and organs.
The research was funded, in part, by Skyline Therapeutics, the Koch Institute Frontier Research Program through the Kathy and Curt Marble Cancer Research Fund and the Elisa Rah (2004, 2006) Memorial Fund, the Claudia Adams Barr Foundation, and the Koch Institute Support (core) Grant from the National Cancer Institute.
Laurent Demanet appointed co-director of MIT Center for Computational Science and EngineeringApplied mathematics professor will join fellow co-director Nicolas Hadjiconstantinou in leading the cross-cutting center.Laurent Demanet, MIT professor of applied mathematics, has been appointed co-director of the MIT Center for Computational Science and Engineering (CCSE), effective Sept. 1.
Demanet, who holds a joint appointment in the departments of Mathematics and Earth, Atmospheric and Planetary Sciences — where he previously served as director of the Earth Resources Laboratory — succeeds Youssef Marzouk, who is now serving as the associate dean of the MIT Schwarzman College of Computing.
Joining co-director Nicolas Hadjiconstantinou, the Quentin Berg (1937) Professor of Mechanical Engineering, Demanet will help lead CCSE, supporting students, faculty, and researchers while fostering a vibrant community of innovation and discovery in computational science and engineering (CSE).
“Laurent’s ability to translate concepts of computational science and engineering into understandable, real-world applications is an invaluable asset to CCSE. His interdisciplinary experience is a benefit to the visibility and impact of CSE research and education. I look forward to working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.
“I’m pleased to welcome Laurent into his new role as co-director of CCSE. His work greatly supports the cross-cutting methodology at the heart of the computational science and engineering community. I’m excited for CCSE to have a co-director from the School of Science, and eager to see the center continue to broaden its connections across MIT,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing, department head of Electrical Engineering and Computer Science, and MathWorks Professor.
Established in 2008, CCSE was incorporated into the MIT Schwarzman College of Computing as one of its core academic units in January 2020. An interdisciplinary research and education center dedicated to pioneering applications of computation, CCSE houses faculty, researchers, and students from a range of MIT schools, such as the schools of Engineering, Science, Architecture and Planning, and the MIT Sloan School of Management, as well as other units of the college.
“I look forward to working with Nicolas and the college leadership on raising the profile of CCSE on campus and globally. We will be pursuing a set of initiatives that span from enhancing the visibility of our research and strengthening our CSE PhD program, to expanding professional education offerings and deepening engagement with our alumni and with industry,” says Demanet.
Demanet’s research lies at the intersection of applied mathematics and scientific computing to visualize the structures beneath Earth’s surface. He also has a strong interest in scientific computing, machine learning, inverse problems, and wave propagation. Through his position as principal investigator of the Imaging and Computing Group, Demanet and his students aim to answer fundamental questions in computational seismic imaging to increase the quality and accuracy of mapping and the projection of changes in Earth’s geological structures. The implications of his work are rooted in environmental monitoring, water resources and geothermal energy, and the understanding of seismic hazards, among others.
He joined the MIT faculty in 2009. He received an Alfred P. Sloan Research Fellowship and the U.S. Air Force Young Investigator Award in 2011, and a CAREER award from the National Science Foundation in 2012. He also held the Class of 1954 Career Development Professorship from 2013 to 2016. Prior to coming to MIT, Demanet held the Szegö Assistant Professorship at Stanford University. He completed his undergraduate studies in mathematical engineering and theoretical physics at Universite de Louvain in Belgium, and earned a PhD in applied and computational mathematics at Caltech, where he was awarded the William P. Carey Prize for best dissertation in the mathematical sciences.
Fighting for the health of the planet with AIAssistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.
Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the MIT Laboratory for Information and Decision Systems — was struck by the disparities in how people live.
“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says. “From a young age, I knew that I definitely wanted to address that issue.”
That motivation was further stoked by a high school biology teacher, who focused his class on climate and sustainability.
“We learned that climate change, this huge, important issue, would exacerbate inequity,” Donti says. “That really stuck with me and put a fire in my belly.”
So, when Donti enrolled at Harvey Mudd College, she thought she would direct her energy toward the study of chemistry or materials science to create next-generation solar panels.
Those plans, however, were jilted. Donti “fell in love” with computer science, and then discovered work by researchers in the United Kingdom who were arguing that artificial intelligence and machine learning would be essential to help integrate renewables into power grids.
“It was the first time I’d seen those two interests brought together,” she says. “I got hooked and have been working on that topic ever since.”
Pursuing a PhD at Carnegie Mellon University, Donti was able to design her degree to include computer science and public policy. In her research, she explored the need for fundamental algorithms and tools that could manage, at scale, power grids relying heavily on renewables.
“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says. “But I wanted to make sure that the way I was doing the work was grounded both in the actual energy systems domain and working with people in that domain” to provide what was actually needed.
While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI. Her objective, she says, was to help the community of people involved in climate and sustainability — “be they computer scientists, academics, practitioners, or policymakers” — to come together and access resources, connection, and education “to help them along that journey.”
“In the climate space,” she says, “you need experts in particular climate change-related sectors, experts in different technical and social science tool kits, problem owners, affected users, policymakers who know the regulations — all of those — to have on-the-ground scalable impact.”
When Donti came to MIT in September 2023, it was not surprising that she was drawn by its initiatives directing the application of computer science toward society’s biggest problems, especially the current threat to the health of the planet.
“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says. “Technology is not just one-and-done and monetizable in the context of a year.”
Her work uses deep learning models to incorporate the physics and hard constraints of electric power systems that employ renewables for better forecasting, optimization, and control.
“Machine learning is already really widely used for things like solar power forecasting, which is a prerequisite to managing and balancing power grids,” she says. “My focus is, how do you improve the algorithms for actually balancing power grids in the face of a range of time-varying renewables?”
Among Donti’s breakthroughs is a promising solution for power grid operators to be able to optimize for cost, taking into account the actual physical realities of the grid, rather than relying on approximations. While the solution is not yet deployed, it appears to work 10 times faster, and far more cheaply, than previous technologies, and has attracted the attention of grid operators.
Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization. In general, much data related to the systems is private, either because it is proprietary or because of security concerns. Donti and her research group are working to create synthetic data and benchmarks that, Donti says, “can help to expose some of the underlying problems” in making power systems more efficient.
“The question is,” Donti says, “can we bring our datasets to a point such that they are just hard enough to drive progress?”
For her efforts, Donti has been awarded the U.S. Department of Energy Computational Science Graduate Fellowship and the NSF Graduate Research Fellowship. She was recognized as part of MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50.”
Next spring, Donti will co-teach a class called AI for Climate Action with Sara Beery, EECS assistant professor, whose focus is AI for biodiversity and ecosystems, and Abigail Bodner, an assistant professor in Earth, Atmospheric and Planetary Sciences, holding an MIT Schwarzman College of Computing shared position with EECS.
“We’re all super-excited about it,” Donti says.
Coming to MIT, Donti says, “I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society.”
New prediction model could improve the reliability of fusion power plantsThe approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and limitless fusion energy.
Today, there are a number of experimental tokamaks in operation around the world, with more underway. Most are small-scale research machines built to investigate how the devices can spin up plasma and harness its energy. One of the challenges that tokamaks face is how to safely and reliably turn off a plasma current that is circulating at speeds of up to 100 kilometers per second, at temperatures of over 100 million degrees Celsius.
Such “rampdowns” are necessary when a plasma becomes unstable. To prevent the plasma from further disrupting and potentially damaging the device’s interior, operators ramp down the plasma current. But occasionally the rampdown itself can destabilize the plasma. In some machines, rampdowns have caused scrapes and scarring to the tokamak’s interior — minor damage that still requires considerable time and resources to repair.
Now, scientists at MIT have developed a method to predict how plasma in a tokamak will behave during a rampdown. The team combined machine-learning tools with a physics-based model of plasma dynamics to simulate a plasma’s behavior and any instabilities that may arise as the plasma is ramped down and turned off. The researchers trained and tested the new model on plasma data from an experimental tokamak in Switzerland. They found the method quickly learned how plasma would evolve as it was tuned down in different ways. What’s more, the method achieved a high level of accuracy using a relatively small amount of data. This training efficiency is promising, given that each experimental run of a tokamak is expensive and quality data is limited as a result.
The new model, which the team highlights this week in an open-access Nature Communications paper, could improve the safety and reliability of future fusion power plants.
“For fusion to be a useful energy source it’s going to have to be reliable,” says lead author Allen Wang, a graduate student in aeronautics and astronautics and a member of the Disruption Group at MIT’s Plasma Science and Fusion Center (PSFC). “To be reliable, we need to get good at managing our plasmas.”
The study’s MIT co-authors include PSFC Principal Research Scientist and Disruptions Group leader Cristina Rea, and members of the Laboratory for Information and Decision Systems (LIDS) Oswin So, Charles Dawson, and Professor Chuchu Fan, along with Mark (Dan) Boyer of Commonwealth Fusion Systems and collaborators from the Swiss Plasma Center in Switzerland.
“A delicate balance”
Tokamaks are experimental fusion devices that were first built in the Soviet Union in the 1950s. The device gets its name from a Russian acronym that translates to a “toroidal chamber with magnetic coils.” Just as its name describes, a tokamak is toroidal, or donut-shaped, and uses powerful magnets to contain and spin up a gas to temperatures and energies high enough that atoms in the resulting plasma can fuse and release energy.
Today, tokamak experiments are relatively low-energy in scale, with few approaching the size and output needed to generate safe, reliable, usable energy. Disruptions in experimental, low-energy tokamaks are generally not an issue. But as fusion machines scale up to grid-scale dimensions, controlling much higher-energy plasmas at all phases will be paramount to maintaining a machine’s safe and efficient operation.
“Uncontrolled plasma terminations, even during rampdown, can generate intense heat fluxes damaging the internal walls,” Wang notes. “Quite often, especially with the high-performance plasmas, rampdowns actually can push the plasma closer to some instability limits. So, it’s a delicate balance. And there’s a lot of focus now on how to manage instabilities so that we can routinely and reliably take these plasmas and safely power them down. And there are relatively few studies done on how to do that well.”
Bringing down the pulse
Wang and his colleagues developed a model to predict how a plasma will behave during tokamak rampdown. While they could have simply applied machine-learning tools such as a neural network to learn signs of instabilities in plasma data, “you would need an ungodly amount of data” for such tools to discern the very subtle and ephemeral changes in extremely high-temperature, high-energy plasmas, Wang says.
Instead, the researchers paired a neural network with an existing model that simulates plasma dynamics according to the fundamental rules of physics. With this combination of machine learning and a physics-based plasma simulation, the team found that only a couple hundred pulses at low performance, and a small handful of pulses at high performance, were sufficient to train and validate the new model.
The data they used for the new study came from the TCV, the Swiss “variable configuration tokamak” operated by the Swiss Plasma Center at EPFL (the Swiss Federal Institute of Technology Lausanne). The TCV is a small experimental fusion experimental device that is used for research purposes, often as test bed for next-generation device solutions. Wang used the data from several hundred TCV plasma pulses that included properties of the plasma such as its temperature and energies during each pulse’s ramp-up, run, and ramp-down. He trained the new model on this data, then tested it and found it was able to accurately predict the plasma’s evolution given the initial conditions of a particular tokamak run.
The researchers also developed an algorithm to translate the model’s predictions into practical “trajectories,” or plasma-managing instructions that a tokamak controller can automatically carry out to for instance adjust the magnets or temperature maintain the plasma’s stability. They implemented the algorithm on several TCV runs and found that it produced trajectories that safely ramped down a plasma pulse, in some cases faster and without disruptions compared to runs without the new method.
“At some point the plasma will always go away, but we call it a disruption when the plasma goes away at high energy. Here, we ramped the energy down to nothing,” Wang notes. “We did it a number of times. And we did things much better across the board. So, we had statistical confidence that we made things better.”
The work was supported in part by Commonwealth Fusion Systems (CFS), an MIT spinout that intends to build the world’s first compact, grid-scale fusion power plant. The company is developing a demo tokamak, SPARC, designed to produce net-energy plasma, meaning that it should generate more energy than it takes to heat up the plasma. Wang and his colleagues are working with CFS on ways that the new prediction model and tools like it can better predict plasma behavior and prevent costly disruptions to enable safe and reliable fusion power.
“We’re trying to tackle the science questions to make fusion routinely useful,” Wang says. “What we’ve done here is the start of what is still a long journey. But I think we’ve made some nice progress.”
Additional support for the research came from the framework of the EUROfusion Consortium, via the Euratom Research and Training Program and funded by the Swiss State Secretariat for Education, Research, and Innovation.
Printable aluminum alloy sets strength records, may enable lighter aircraft partsIncorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.MIT engineers have developed a printable aluminum alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminum.
The new printable metal is made from a mix of aluminum and other elements that the team identified using a combination of simulations and machine learning, which significantly pruned the number of possible combinations of materials to search through. While traditional methods would require simulating over 1 million possible combinations of materials, the team’s new machine learning-based approach needed only to evaluate 40 possible compositions before identifying an ideal mix for a high-strength, printable aluminum alloy.
When they printed the alloy and tested the resulting material, the team confirmed that, as predicted, the aluminum alloy was as strong as the strongest aluminum alloys that are manufactured today using traditional casting methods.
The researchers envision that the new printable aluminum could be made into stronger, more lightweight and temperature-resistant products, such as fan blades in jet engines. Fan blades are traditionally cast from titanium — a material that is more than 50 percent heavier and up to 10 times costlier than aluminum — or made from advanced composites.
“If we can use lighter, high-strength material, this would save a considerable amount of energy for the transportation industry,” says Mohadeseh Taheri-Mousavi, who led the work as a postdoc at MIT and is now an assistant professor at Carnegie Mellon University.
“Because 3D printing can produce complex geometries, save material, and enable unique designs, we see this printable alloy as something that could also be used in advanced vacuum pumps, high-end automobiles, and cooling devices for data centers,” adds John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering at MIT.
Hart and Taheri-Mousavi provide details on the new printable aluminum design in a paper published in the journal Advanced Materials. The paper’s MIT co-authors include Michael Xu, Clay Houser, Shaolou Wei, James LeBeau, and Greg Olson, along with Florian Hengsbach and Mirko Schaper of Paderborn University in Germany, and Zhaoxuan Ge and Benjamin Glaser of Carnegie Mellon University.
Micro-sizing
The new work grew out of an MIT class that Taheri-Mousavi took in 2020, which was taught by Greg Olson, professor of the practice in the Department of Materials Science and Engineering. As part of the class, students learned to use computational simulations to design high-performance alloys. Alloys are materials that are made from a mix of different elements, the combination of which imparts exceptional strength and other unique properties to the material as a whole.
Olson challenged the class to design an aluminum alloy that would be stronger than the strongest printable aluminum alloy designed to date. As with most materials, the strength of aluminum depends in large part on its microstructure: The smaller and more densely packed its microscopic constituents, or “precipitates,” the stronger the alloy would be.
With this in mind, the class used computer simulations to methodically combine aluminum with various types and concentrations of elements, to simulate and predict the resulting alloy’s strength. However, the exercise failed to produce a stronger result. At the end of the class, Taheri-Mousavi wondered: Could machine learning do better?
“At some point, there are a lot of things that contribute nonlinearly to a material’s properties, and you are lost,” Taheri-Mousavi says. “With machine-learning tools, they can point you to where you need to focus, and tell you for example, these two elements are controlling this feature. It lets you explore the design space more efficiently.”
Layer by layer
In the new study, Taheri-Mousavi continued where Olson’s class left off, this time looking to identify a stronger recipe for aluminum alloy. This time, she used machine-learning techniques designed to efficiently comb through data such as the properties of elements, to identify key connections and correlations that should lead to a more desirable outcome or product.
She found that, using just 40 compositions mixing aluminum with different elements, their machine-learning approach quickly homed in on a recipe for an aluminum alloy with higher volume fraction of small precipitates, and therefore higher strength, than what the previous studies identified. The alloy’s strength was even higher than what they could identify after simulating over 1 million possibilities without using machine learning.
To physically produce this new strong, small-precipitate alloy, the team realized 3D printing would be the way to go instead of traditional metal casting, in which molten liquid aluminum is poured into a mold and is left to cool and harden. The longer this cooling time is, the more likely the individual precipitate is to grow.
The researchers showed that 3D printing, broadly also known as additive manufacturing, can be a faster way to cool and solidify the aluminum alloy. Specifically, they considered laser bed powder fusion (LBPF) — a technique by which a powder is deposited, layer by layer, on a surface in a desired pattern and then quickly melted by a laser that traces over the pattern. The melted pattern is thin enough that it solidfies quickly before another layer is deposited and similarly “printed.” The team found that LBPF’s inherently rapid cooling and solidification enabled the small-precipitate, high-strength aluminum alloy that their machine learning method predicted.
“Sometimes we have to think about how to get a material to be compatible with 3D printing,” says study co-author John Hart. “Here, 3D printing opens a new door because of the unique characteristics of the process — particularly, the fast cooling rate. Very rapid freezing of the alloy after it’s melted by the laser creates this special set of properties.”
Putting their idea into practice, the researchers ordered a formulation of printable powder, based on their new aluminum alloy recipe. They sent the powder — a mix of aluminum and five other elements — to collaborators in Germany, who printed small samples of the alloy using their in-house LPBF system. The samples were then sent to MIT where the team ran multiple tests to measure the alloy’s strength and image the samples’ microstructure.
Their results confirmed the predictions made by their initial machine learning search: The printed alloy was five times stronger than a casted counterpart and 50 percent stronger than alloys designed using conventional simulations without machine learning. The new alloy’s microstructure also consisted of a higher volume fraction of small precipitates, and was stable at high temperatures of up to 400 degrees Celsius — a very high temperature for aluminum alloys.
The researchers are applying similar machine-learning techniques to further optimize other properties of the alloy.
“Our methodology opens new doors for anyone who wants to do 3D printing alloy design,” Taheri-Mousavi says. “My dream is that one day, passengers looking out their airplane window will see fan blades of engines made from our aluminum alloys.”
This work was carried out, in part, using MIT.nano’s characterization facilities.
Study sheds light on musicians’ enhanced attentionBrain imaging suggests people with musical training may be better than others at filtering out distracting sounds.In a world full of competing sounds, we often have to filter out a lot of noise to hear what’s most important. This critical skill may come more easily for people with musical training, according to scientists at MIT’s McGovern Institute for Brain Research, who used brain imaging to follow what happens when people try to focus their attention on certain sounds.
When Cassia Low Manting, a recent MIT postdoc working in the labs of MIT Professor and McGovern Institute PI John Gabrieli and former McGovern Institute PI Dimitrios Pantazis, asked people to focus on a particular melody while another melody played at the same time, individuals with musical backgrounds were, unsurprisingly, better able to follow the target tune. An analysis of study participants’ brain activity suggests this advantage arises because musical training sharpens neural mechanisms that amplify the sounds they want to listen to while turning down distractions.
“People can hear, understand, and prioritize multiple sounds around them that flow on a moment-to-moment basis,” explains Gabrieli, who is the Grover Hermann Professor of Health Sciences and Technology at MIT. “This study reveals the specific brain mechanisms that successfully process simultaneous sounds on a moment-to-moment basis and promote attention to the most important sounds. It also shows how musical training alters that processing in the mind and brain, offering insight into how experience shapes the way we listen and pay attention.”
The research team, which also included senior author Daniel Lundqvist at the Karolinska Institute in Sweden, reported their open-access findings Sept. 17 in the journal Science Advances. Manting, who is now at the Karolinska Institute, notes that the research is part of an ongoing collaboration between the two institutions.
Overcoming challenges
Participants in the study had vastly difference backgrounds when it came to music. Some were professional musicians with deep training and experience, while others struggled to differentiate between the two tunes they were played, despite each one’s distinct pitch. This disparity allowed the researchers to explore how the brain’s capacity for attention might change with experience. “Musicians are very fun to study because their brains have been morphed in ways based on their training,” Manting says. “It’s a nice model to study these training effects.”
Still, the researchers had significant challenges to overcome. It has been hard to study how the brain manages auditory attention, because when researchers use neuroimaging to monitor brain activity, they see the brain’s response to all sounds: those that the listener cares most about, as well as those the listener is trying to ignore. It is usually difficult to figure out which brain signals were triggered by which sounds.
Manting and her colleagues overcame this challenge with a method called frequency tagging. Rather than playing the melodies in their experiments at a constant volume, the volume of each melody oscillated, rising and falling with a particular frequency. Each melody had its own frequency, creating detectable patterns in the brain signals that responded to it. “When you play these two sounds simultaneously to the subject and you record the brain signal, you can say, this 39-Hertz activity corresponds to the lower-pitch sound and the 43-Hertz activity corresponds specifically to the higher-pitch sound,” Manting explains. “It is very clean and very clear.”
When they paired frequency tagging with magnetoencephalography, a noninvasive method of monitoring brain activity, the team was able to track how their study participants’ brains responded to each of two melodies during their experiments. While the two tunes played, subjects were instructed to follow either the higher-pitched or the lower-pitched melody. When the music stopped, they were asked about the final notes of the target tune: did they rise or did they fall? The researchers could make this task harder by making the two tunes closer together in pitch, as well as by altering the timing of the notes.
Manting used a survey that asked about musical experience to score each participant’s musicality, and this measure had an obvious effect on task performance: The more musical a person was, the more successful they were at following the tune they had been asked to track.
To look for differences in brain activity that might explain this, the research team developed a new machine-learning approach to analyze their data. They used it to tease apart what was happening in the brain as participants focused on the target tune — even, in some cases, when the notes of the distracting tune played at the exact same time.
Top-down versus bottom-up attention
What they found was a clear separation of brain activity associated with two kinds of attention, known as top-down and bottom-up attention. Manting explains that top-down attention is goal-oriented, involving a conscious focus — the kind of attention listeners called on as they followed the target tune. Bottom-up attention, on the other hand, is triggered by the nature of the sound itself. A fire alarm would be expected to trigger this kind of attention, both with its volume and its suddenness. The distracting tune in the team’s experiments triggered activity associated with bottom-up attention — but more so in some people than in others.
“The more musical someone is, the better they are at focusing their top-down selective attention, and the less the effect of bottom-up attention is,” Manting explains.
Manting expects that musicians use their heightened capacity for top-down attention in other situations, as well. For example, they might be better than others at following a conversation in a room filled with background chatter. “I would put my bet on it that there is a high chance that they will be great at zooming into sounds,” she says.
She wonders, however, if one kind of distraction might actually be harder for a musician to filter out: the sound of their own instrument. Manting herself plays both the piano and the Chinese harp, and she says hearing those instruments is “like someone calling my name.” It’s one of many questions about how musical training affects cognition that she plans to explore in her future work.
Matthew Shoulders named head of the Department of ChemistryA leading researcher in protein folding biochemistry and next-generation protein engineering techniques will advance chemistry research and education.Matthew D. Shoulders, the Class of 1942 Professor of Chemistry, a MacVicar Faculty Fellow, and an associate member of the Broad Institute of MIT and Harvard, has been named head of the MIT Department of Chemistry, effective Jan. 16, 2026.
“Matt has made pioneering contributions to the chemistry research community through his research on mechanisms of proteostasis and his development of next-generation techniques to address challenges in biomedicine and agriculture,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “He is also a dedicated educator, beloved by undergraduates and graduates alike. I know the department will be in good hands as we double down on our commitment to world-leading research and education in the face of financial headwinds.”
Shoulders succeeds Troy Van Voorhis, the Robert T. Haslam and Bradley Dewey Professor of Chemistry, who has been at the helm since October 2019.
“I am tremendously grateful to Troy for his leadership the past six years, building a fantastic community here in our department. We face challenges, but also many exciting opportunities, as a department in the years to come,” says Shoulders. “One thing is certain: Chemistry innovations are critical to solving pressing global challenges. Through the research that we do and the scientists we train, our department has a huge role to play in shaping the future.”
Shoulders studies how cells fold proteins, and he develops and applies novel protein engineering techniques to challenges in biotechnology. His work across chemistry and biochemistry fields including proteostasis, extracellular matrix biology, virology, evolution, and synthetic biology is yielding not just important insights into topics like how cells build healthy tissues and how proteins evolve, but also influencing approaches to disease therapy and biotechnology development.
“Matt is an outstanding researcher whose work touches on fundamental questions about how the cell machinery directs the synthesis and folding of proteins. His discoveries about how that machinery breaks down as a result of mutations or in response to stress has a fundamental impact on how we think about and treat human diseases,” says Van Voorhis.
In one part of Matt's current research program, he is studying how protein folding systems in cells — known as chaperones — shape the evolution of their clients. Amongst other discoveries, his lab has shown that viral pathogens hijack human chaperones to enable their rapid evolution and escape from host immunity. In related recent work, they have discovered that these same chaperones can promote access to malignancy-driving mutations in tumors. Beyond fundamental insights into evolutionary biology, these findings hold potential to open new therapeutic strategies to target cancer and viral infections.
“Matt’s ability to see both the details and the big picture makes him an outstanding researcher and a natural leader for the department,” says Timothy Swager, the John D. MacArthur Professor of Chemistry. “MIT Chemistry can only benefit from his dedication to understanding and addressing the parts and the whole.”
Shoulders also leads a food security project through the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Shoulders, along with MIT Research Scientist Robbie Wilson, assembled an interdisciplinary team based at MIT to enhance climate resilience in agriculture by improving one of the most inefficient aspects of photosynthesis, the carbon dioxide-fixing plant enzyme RuBisCO. J-WAFS funded this high-risk, high-reward MIT Grand Challenge project in 2023, and it has received further support from federal research agencies and the Grantham Foundation for the Protection of the Environment.
“Our collaborative team of biochemists and synthetic biologists, computational biologists, and chemists is deeply integrated with plant biologists, creating a robust feedback loop for enzyme engineering,” Shoulders says. “Together, this team is making a concerted effort using state-of-the-art techniques to engineer crop RuBisCO with an eye to helping make meaningful gains in securing a stable crop supply, hopefully with accompanying improvements in both food and water security.”
In addition to his research contributions, Shoulders has taught multiple classes for Course V, including 5.54 (Advances in Chemical Biology) and 5.111 (Principles of Chemical Science), along with a number of other key chemistry classes. His contributions to a 5.111 “bootcamp” through the MITx platform served to address gaps in the classroom curriculum by providing online tools to help undergraduate students better grasp the material in the chemistry General Institute Requirement (GIR). His development of Guided Learning Demonstrations to support first-year chemistry courses at MIT has helped bring the lab to the GIR, and also contributed to the popularity of 5.111 courses offered regularly via MITx.
“I have had the pleasure of teaching with Matt on several occasions, and he is a fantastic educator. He is an innovator both inside and outside the classroom and has an unwavering commitment to his students’ success,” says Van Voorhis of Shoulders, who was named a 2022 MacVicar Faculty Fellow, and who received a Committed to Caring award through the Office of Graduate Education.
Shoulders also founded the MIT Homeschool Internship Program for Science and Technology, which brings high school students to campus for paid summer research experiences in labs across the Institute.
He is a founding member of the Department of Chemistry’s Quality of Life Committee and chair for the last six years, helping to improve all aspects of opportunity, professional development, and experience in the department: “countless changes that have helped make MIT a better place for all,” as Van Voorhis notes, including creating a peer mentoring program for graduate students and establishing universal graduate student exit interviews to collect data for department-wide assessment and improvement.
At the Institute level, Shoulders has served on the Committee on Graduate Programs, Committee on Sexual Misconduct Prevention and Response (in which he co-chaired the provost's working group on the Faculty and Staff Sexual Misconduct Survey), and the Committee on Assessment of Biohazards and Embryonic Stem Cell Research Oversight, among other roles.
Shoulders graduated summa cum laude from Virginia Tech in 2004, earning a BS in chemistry with a minor in biochemistry. He earned a PhD in chemistry at the University of Wisconsin at Madison in 2009 under Professor Ronald Raines. Following an American Cancer Society Postdoctoral Fellowship at Scripps Research Institute, working with professors Jeffery Kelly and Luke Wiseman, Shoulders joined the MIT Department of Chemistry faculty as an assistant professor in 2012. Shoulders also serves as an associate member of the Broad Institute and an investigator at the Center for Musculoskeletal Research at Massachusetts General Hospital.
Among his many awards, Shoulders has received a NIH Director's New Innovator Award under the NIH High-Risk, High-Reward Research Program; an NSF CAREER Award; an American Cancer Society Research Scholar Award; the Camille Dreyfus Teacher-Scholar Award; and most recently the Ono Pharma Foundation Breakthrough Science Award.
Report: Sustainability in supply chains is still a firm-level priorityAnalysis from MIT’s Center for Transportation and Logistics finds companies are still acting to reduce emissions, but often lag in measurement techniques.Corporations are actively seeking sustainability advances in their supply chains — but many need to improve the business metrics they use in this area to realize more progress, according to a new report by MIT researchers.
During a time of shifting policies globally and continued economic uncertainty, the survey-based report finds 85 percent of companies say they are continuing supply chain sustainability practices at the same level as in recent years, or are increasing those efforts.
“What we found is strong evidence that sustainability still matters,” says Josué Velázquez Martínez, a research scientist and director of the MIT Sustainable Supply Chain Lab, which helped produce the report. “There are many things that remain to be done to accomplish those goals, but there’s a strong willingness from companies in all parts of the world to do something about sustainability.”
The new analysis, titled “Sustainability Still Matters,” was released today. It is the sixth annual report on the subject prepared by the MIT Sustainable Supply Chain Lab, which is part of MIT’s Center for Transportation and Logistics. The Council of Supply Chain Management Professionals collaborated on the project as well.
The report is based on a global survey, with responses from 1,203 professionals in 97 countries. This year, the report analyzes three issues in depth, including regulations and the role they play in corporate approaches to supply chain management. A second core topic is management and mitigation of what industry professionals call “Scope 3” emissions, which are those not from a firm itself, but from a firm’s supply chain. And a third issue of focus is the future of freight transportation, which by itself accounts for a substantial portion of supply chain emissions.
Broadly, the survey finds that for European-based firms, the principal driver of action in this area remains government mandates, such as the Corporate Sustainability Reporting Directive, which requires companies to publish regular reports on their environmental impact and the risks to society involved. In North America, firm leadership and investor priorities are more likely to be decisive factors in shaping a company’s efforts.
“In Europe the pressure primarily comes more from regulation, but in the U.S. it comes more from investors, or from competitors,” Velázquez Martínez says.
The survey responses on Scope 3 emissions reveal a number of opportunities for improvement. In business and sustainability terms, Scope 1 greenhouse gas emissions are those a firm produces directly. Scope 2 emissions are the energy it has purchased. And Scope 3 emissions are those produced across a firm’s value chain, including the supply chain activities involved in producing, transporting, using, and disposing of its products.
The report reveals that about 40 percent of firms keep close track of Scope 1 and 2 emissions, but far fewer tabulate Scope 3 on equivalent terms. And yet Scope 3 may account for roughly 75 percent of total firm emissions, on aggregate. About 70 percent of firms in the survey say they do not have enough data from suppliers to accurately tabulate the total greenhouse gas and climate impact of their supply chains.
Certainly it can be hard to calculate the total emissions when a supply chain has many layers, including smaller suppliers lacking data capacity. But firms can upgrade their analytics in this area, too. For instance, 50 percent of North American firms are still using spreadsheets to tabulate emissions data, often making rough estimates that correlate emissions to simple economic activity. An alternative is life cycle assessment software that provides more sophisticated estimates of a product’s emissions, from the extraction of its materials to its post-use disposal. By contrast, only 32 percent of European firms are still using spreadsheets rather than life cycle assessment tools.
“You get what you measure,” Velázquez Martínez says. “If you measure poorly, you’re going to get poor decisions that most likely won’t drive the reductions you’re expecting. So we pay a lot of attention to that particular issue, which is decisive to defining an action plan. Firms pay a lot of attention to metrics in their financials, but in sustainability they’re often using simplistic measurements.”
When it comes to transportation, meanwhile, the report shows that firms are still grappling with the best ways to reduce emissions. Some see biofuels as the best short-term alternative to fossil fuels; others are investing in electric vehicles; some are waiting for hydrogen-powered vehicles to gain traction. Supply chains, after all, frequently involve long-haul trips. For firms, as for individual consumers, electric vehicles are more practical with a larger infrastructure of charging stations. There are advances on that front but more work to do as well.
That said, “Transportation has made a lot of progress in general,” Velázquez Martínez says, noting the increased acceptance of new modes of vehicle power in general.
Even as new technologies loom on the horizon, though, supply chain sustainability is not wholly depend on their introduction. One factor continuing to propel sustainability in supply chains is the incentives companies have to lower costs. In a competitive business environment, spending less on fossil fuels usually means savings. And firms can often find ways to alter their logistics to consume and spend less.
“Along with new technologies, there is another side of supply chain sustainability that is related to better use of the current infrastructure,” Velázquez Martínez observes. “There is always a need to revise traditional ways of operating to find opportunities for more efficiency.”
Chemists create red fluorescent dyes that may enable clearer biomedical imagingThe new dyes are based on boron-containing molecules that were previously too unstable for practical use.MIT chemists have designed a new type of fluorescent molecule that they hope could be used for applications such as generating clearer images of tumors.
The new dye is based on a borenium ion — a positively charged form of boron that can emit light in the red to near-infrared range. Until recently, these ions have been too unstable to be used for imaging or other biomedical applications.
In a study appearing today in Nature Chemistry, the researchers showed that they could stabilize borenium ions by attaching them to a ligand. This approach allowed them to create borenium-containing films, powders, and crystals, all of which emit and absorb light in the red and near-infrared range.
That is important because near-IR light is easier to see when imaging structures deep within tissues, which could allow for clearer images of tumors and other structures in the body.
“One of the reasons why we focus on red to near-IR is because those types of dyes penetrate the body and tissue much better than light in the UV and visible range. Stability and brightness of those red dyes are the challenges that we tried to overcome in this study,” says Robert Gilliard, the Novartis Professor of Chemistry at MIT and the senior author of the study.
MIT research scientist Chun-Lin Deng is the lead author of the paper. Other authors include Bi Youan (Eric) Tra PhD ’25, former visiting graduate student Xibao Zhang, and graduate student Chonghe Zhang.
Stabilized borenium
Most fluorescent imaging relies on dyes that emit blue or green light. Those imaging agents work well in cells, but they are not as useful in tissue because low levels of blue and green fluorescence produced by the body interfere with the signal. Blue and green light also scatters in tissue, limiting how deeply it can penetrate.
Imaging agents that emit red fluorescence can produce clearer images, but most red dyes are inherently unstable and don’t produce a bright signal, because of their low quantum yields (the ratio of fluorescent photons emitted per photon of light is absorbed). For many red dyes, the quantum yield is only about 1 percent.
Among the molecules that can emit near-infrared light are borenium cations —positively charged ions containing an atom of boron attached to three other atoms.
When these molecules were first discovered in the mid-1980s, they were considered “laboratory curiosities,” Gilliard says. These molecules were so unstable that they had to be handled in a sealed container called a glovebox to protect them from exposure to air, which can lead them to break down.
Later, chemists realized they could make these ions more stable by attaching them to molecules called ligands. Working with these more stable ions, Gillliard’s lab discovered in 2019 that they had some unusual properties: Namely, they could respond to changes in temperature by emitting different colors of light.
However, at that point, “there was a substantial problem in that they were still too reactive to be handled in open air,” Gilliard says.
His lab began working on new ways to further stabilize them using ligands known as carbodicarbenes (CDCs), which they reported in a 2022 study. Due to this stabilization, the compounds can now be studied and handled without using a glovebox. They are also resistant to being broken down by light, unlike many previous borenium-based compounds.
In the new study, Gilliard began experimenting with the anions (negatively charged ions) that are a part of the CDC-borenium compounds. Interactions between these anions and the borenium cation generate a phenomenon known as exciton coupling, the researchers discovered. This coupling, they found, shifted the molecules’ emission and absorption properties toward the infrared end of the color spectrum. These molecules also generated a high quantum yield, allowing them to shine more brightly.
“Not only are we in the correct region, but the efficiency of the molecules is also very suitable,” Gilliard says. “We’re up to percentages in the thirties for the quantum yields in the red region, which is considered to be high for that region of the electromagnetic spectrum.”
Potential applications
The researchers also showed that they could convert their borenium-containing compounds into several different states, including solid crystals, films, powders, and colloidal suspensions.
For biomedical imaging, Gilliard envisions that these borenium-containing materials could be encapsulated in polymers, allowing them to be injected into the body to use as an imaging dye. As a first step, his lab plans to work with researchers in the chemistry department at MIT and at the Broad Institute of MIT and Harvard to explore the potential of imaging these materials within cells.
Because of their temperature responsiveness, these materials could also be deployed as temperature sensors, for example, to monitor whether drugs or vaccines have been exposed to temperatures that are too high or low during shipping.
“For any type of application where temperature tracking is important, these types of ‘molecular thermometers’ can be very useful,” Gilliard says.
If incorporated into thin films, these molecules could also be useful as organic light-emitting diodes (OLEDs), particularly in new types of materials such as flexible screens, Gilliard says.
“The very high quantum yields achieved in the near-IR, combined with the excellent environmental stability, make this class of compounds extremely interesting for biological applications,” says Frieder Jaekle, a professor of chemistry at Rutgers University, who was not involved in the study. “Besides the obvious utility in bioimaging, the strong and tunable near-IR emission also makes these new fluorophores very appealing as smart materials for anticounterfeiting, sensors, switches, and advanced optoelectronic devices.”
In addition to exploring possible applications for these dyes, the researchers are now working on extending their color emission further into the near-infrared region, which they hope to achieve by incorporating additional boron atoms. Those extra boron atoms could make the molecules less stable, so the researchers are also working on new types of carbodicarbenes to help stabilize them.
The research was funded by the Arnold and Mabel Beckman Foundation and the National Institutes of Health.
AI maps how a new antibiotic targets gut bacteriaMIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact. Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.
“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work, assistant professor of biochemistry and biomedical sciences at McMaster, and research affiliate at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health. “The problem isn’t finding molecules that kill bacteria in a dish — we’ve been able to do that for a long time. A major hurdle is figuring out what those molecules actually do inside bacteria. Without that detailed understanding, you can’t develop these early-stage antibiotics into safe and effective therapies for patients.”
Enterololin is a stride toward precision antibiotics: treatments designed to knock out only the bacteria causing trouble. In mouse models of Crohn’s-like inflammation, the drug zeroed in on Escherichia coli, a gut-dwelling bacterium that can worsen flares, while leaving most other microbial residents untouched. Mice given enterololin recovered faster and maintained a healthier microbiome than those treated with vancomycin, a common antibiotic.
Pinning down a drug’s mechanism of action, the molecular target it binds inside bacterial cells, normally requires years of painstaking experiments. Stokes’ lab discovered enterololin using a high-throughput screening approach, but determining its target would have been the bottleneck. Here, the team turned to DiffDock, a generative AI model developed at CSAIL by MIT PhD student Gabriele Corso and MIT Professor Regina Barzilay.
DiffDock was designed to predict how small molecules fit into the binding pockets of proteins, a notoriously difficult problem in structural biology. Traditional docking algorithms search through possible orientations using scoring rules, often producing noisy results. DiffDock instead frames docking as a probabilistic reasoning problem: a diffusion model iteratively refines guesses until it converges on the most likely binding mode.
“In just a couple of minutes, the model predicted that enterololin binds to a protein complex called LolCDE, which is essential for transporting lipoproteins in certain bacteria,” says Barzilay, who also co-leads the Jameel Clinic. “That was a very concrete lead — one that could guide experiments, rather than replace them.”
Stokes’ group then put that prediction to the test. Using DiffDock predictions as an experimental GPS, they first evolved enterololin-resistant mutants of E. coli in the lab, which revealed that changes in the mutant’s DNA mapped to lolCDE, precisely where DiffDock had predicted enterololin to bind. They also performed RNA sequencing to see which bacterial genes switched on or off when exposed to the drug, as well as used CRISPR to selectively knock down expression of the expected target. These laboratory experiments all revealed disruptions in pathways tied to lipoprotein transport, exactly what DiffDock had predicted.
“When you see the computational model and the wet-lab data pointing to the same mechanism, that’s when you start to believe you’ve figured something out,” says Stokes.
For Barzilay, the project highlights a shift in how AI is used in the life sciences. “A lot of AI use in drug discovery has been about searching chemical space, identifying new molecules that might be active,” she says. “What we’re showing here is that AI can also provide mechanistic explanations, which are critical for moving a molecule through the development pipeline.”
That distinction matters because mechanism-of-action studies are often a major rate-limiting step in drug development. Traditional approaches can take 18 months to two years, or more, and cost millions of dollars. In this case, the MIT–McMaster team cut the timeline to about six months, at a fraction of the cost.
Enterololin is still in the early stages of development, but translation is already underway. Stokes’ spinout company, Stoked Bio, has licensed the compound and is optimizing its properties for potential human use. Early work is also exploring derivatives of the molecule against other resistant pathogens, such as Klebsiella pneumoniae. If all goes well, clinical trials could begin within the next few years.
The researchers also see broader implications. Narrow-spectrum antibiotics have long been sought as a way to treat infections without collateral damage to the microbiome, but they have been difficult to discover and validate. AI tools like DiffDock could make that process more practical, rapidly enabling a new generation of targeted antimicrobials.
For patients with Crohn’s and other inflammatory bowel conditions, the prospect of a drug that reduces symptoms without destabilizing the microbiome could mean a meaningful improvement in quality of life. And in the bigger picture, precision antibiotics may help tackle the growing threat of antimicrobial resistance.
“What excites me is not just this compound, but the idea that we can start thinking about the mechanism of action elucidation as something we can do more quickly, with the right combination of AI, human intuition, and laboratory experiments,” says Stokes. “That has the potential to change how we approach drug discovery for many diseases, not just Crohn’s.”
“One of the greatest challenges to our health is the increase of antimicrobial-resistant bacteria that evade even our best antibiotics,” adds Yves Brun, professor at the University of Montreal and distinguished professor emeritus at Indiana University Bloomington, who wasn’t involved in the paper. “AI is becoming an important tool in our fight against these bacteria. This study uses a powerful and elegant combination of AI methods to determine the mechanism of action of a new antibiotic candidate, an important step in its potential development as a therapeutic.”
Corso, Barzilay, and Stokes wrote the paper with McMaster researchers Denise B. Catacutan, Vian Tran, Jeremie Alexander, Yeganeh Yousefi, Megan Tu, Stewart McLellan, and Dominique Tertigas, and professors Jakob Magolan, Michael Surette, Eric Brown, and Brian Coombes. Their research was supported, in part, by the Weston Family Foundation; the David Braley Centre for Antibiotic Discovery; the Canadian Institutes of Health Research; the Natural Sciences and Engineering Research Council of Canada; M. and M. Heersink; Canadian Institutes for Health Research; Ontario Graduate Scholarship Award; the Jameel Clinic; and the U.S. Defense Threat Reduction Agency Discovery of Medical Countermeasures Against New and Emerging Threats program.
The researchers posted sequencing data in public repositories and released the DiffDock-L code openly on GitHub.
Secretary of Energy Chris Wright ’85 visits MITPanel discussions focused on innovation in many forms of energy, then a tour of campus featured student research.U.S. Secretary of Energy Chris Wright ’85 visited MIT on Monday, meeting Institute leaders, discussing energy innovation at a campus forum, viewing poster presentations from researchers supported through the MIT-GE Vernova Energy and Climate Alliance, and watching energy research demos in the lab where he used to work as a student.
“I’ve always been in energy because I think it’s just far and away the world’s most important industry,” Wright said at the forum, which included a panel discussion with business leaders and a fireside chat with MIT Professor Ernest Moniz, who was the U.S. secretary of energy from 2013 to 2017. Wright added: “Not only is it by far the world’s most important industry, because it enables all the others, but it’s also a booming time right now. … It is an awesomely exciting time to be in energy.”
Wright was greeted on campus by MIT President Sally Kornbluth, who also gave introductory remarks at the forum, held in MIT’s Samberg Center. While the Institute has added many research facilities and buildings since Wright was a student, Kornbluth observed, the core MIT ethos remains the same.
“MIT is still MIT,” Kornbluth said. “It’s a community that rewards merit, boldness, and scientific rigor. And it’s a magnet for people with a drive to solve hard problems that matter in the real world, an enthusiasm for working with industry, and an ethic of national service.”
When it comes to energy research, Kornbluth added, “MIT is developing transformational approaches to make American energy more secure, reliable, affordable, and clean — which in turn will strengthen both U.S. competitiveness and national security.”
At the event, Wright, the 17th U.S. secretary of energy, engaged in a fireside chat with Moniz, the 13th U.S. secretary of energy, the Cecil and Ida Green Professor of Physics and Engineering Systems Post-Tenure, a special advisor to the MIT president, and the founding director of the MIT Energy Initiative (MITEI). Wright began his remarks by reflecting on Kornbluth’s description of the Institute.
“Merit, boldness, and scientific rigor,” Wright said. “That is MIT … to me. That hit me hard when I got here, and frankly, it’s a good part of the reason my life has gone the way it’s gone.”
On energy topics, Wright emphasized the need for continued innovation in energy across a range of technologies, including fusion, geothermal, and more, while advocating for the benefits of vigorous market-based progress. Before becoming secretary of energy, Wright most recently served as founder and CEO of Liberty Energy. He also was the founder of Pinnacle Technologies, among other enterprises. Wright was confirmed as secretary by the U.S. Senate in February.
Asked to name promising areas of technological development, Wright focused on three particular areas of interest. Citing artificial intelligence, he noted that the interest in it was “overwhelming,” with many possible applications. Regarding fusion energy, Wright said, “We are going to see meaningful breakthroughs.” And quantum computing, he added, was going to be a “game-changer” as well.
Wright also emphasized the value of federal support for fundamental research, including projects in the national laboratories the Department of Energy oversees.
“The 17 national labs we have in this country are absolute jewels. They are gems of this country,” Wright said. He later noted, “There are things, like this foundational research, that are just an essential part of our country and an essential part of our future.”
Moniz asked Wright a range of questions in the fireside chat, while adding his own perspective at times about the many issues connected to energy abundance globally.
“Climate, energy, security, equity, affordability, have to be recognized as one conversation, and not separate conversations,” Moniz said. “That’s what’s at stake in my view.”
Wright’s appearance was part of the Energy Freedom Tour developed by the American Conservation Coalition (ACC), in coordination with the Hamm Institute for American Energy at Oklahoma State University. Later stops are planned for Stanford University and Texas A&M University.
Ann Bluntzer Pullin, executive director of the Hamm Institute, gave remarks at the forum as well, noting the importance of making students aware of the energy industry and helping to “get them excited about the impact this career can make.” She also praised MIT’s advances in the field, adding, “This is where so many ideas were born and executed that have allowed America to really thrive in this energy abundance in our country that we have [had] for so long.”
The forum also featured remarks from Roger Martella, chief corporate officer, chief sustainability officer, and head of government affairs at GE Vernova. In March, MIT and GE Vernova announced a new five-year joint program, the MIT-GE Vernova Energy and Climate Alliance, featuring research projects, education programs, and career opportunities for MIT students.
“That’s what we’re about, electrification as the lifeblood of prosperity,” Martella said, describing GE Vernova’s work. “When we’re here at MIT we feel like we’re living history every moment when we’re walking down the halls, because no institution has [contributed] to innovation and technology more, doing it every single day to advance prosperity for all people around the world.”
A panel discussion at the forum featured Wright speaking along with three MIT alumni who are active in the energy business: Carlos Araque ’01, SM ’02, CEO of Quaise Energy, a leading-edge firm in geothermal energy solutions; Bob Mumgaard SM ’15, PhD ’15, CEO of Commonwealth Fusion Systems, a leading fusion energy firm and an MIT spinout; and Milo Werner SM ’07, MBA ’07, a general partner at DCVC and expert in energy and climate investments. The panel was moderated by Chris Barnard, president of the ACC.
Mumgaard noted that Commonwealth Fusion Systems launched in 2018 with “an explicit mission, working with MIT still today, of putting fusion onto an industrial trajectory,” although there is “plenty left to do, still, at that intersection of science, technology, innovation, and business.”
Araque said he believes geothermal is “metric-by-metric” more powerful and profitable than many other forms of energy. “This is not a stop-gap,” he added. Quaise is currently developing its first power-plant-scale facility in the U.S.
Werner noted that the process of useful innovation only begins in the lab; making an advance commercially viable is the critical next step. The biggest impact “is not in the breakthrough,” she said. “It’s not in the discovery that you make in the lab. It’s actually once you’ve built a billion of them. That’s when you actually change the world.”
After the forum, Wright took a tour of multiple research centers on the MIT campus, including the MIT.nano facility, guided by Vladimir Bulović, faculty director of MIT.nano and the Fariborz Maseeh Chair in Emerging Technology.
At MIT.nano, Bulović showed Wright the Titan Krios G3i, a nearly room-size electron microscope that enables researchers to take a high-resolution look at the structure of tiny particles, with a variety of research applications. The tour also viewed one of MIT.nano’s cleanrooms, a shared fabrication facility used by both MIT researchers and users outside of MIT, including many in industry.
On a different note, in an MIT.nano hallway, Bulović showed Wright the One.MIT mosaics, which contain the names of all MIT students and employees past and present — well over 300,000 in all. First etched on a 6-inch wafer, the mosaics are a visual demonstration of the power of nanotechnology — and a searchable display, so Bulović located Wright’s name, which is printed near the chin of one of the figures on the MIT seal.
The tour ended in the basement of Building 10, in what is now the refurbished Grainger Energy Machine Facility, where Wright used to conduct research. After earning his undergraduate degree in mechanical engineering, Wright entered into graduate studies at MIT before leaving, as he recounted at the forum, to pursue business opportunities.
At the lab, Wright met with David Perreault, the Ford Foundation Professor of Engineering; Steven Leeb, the Emanuel Landsman Professor and a specialist in power systems; and Sam Coday, the Emanuel E. Landsman Career Development Chair and an assistant professor in the Department of Electrical Engineering and Computer Science. A half-dozen MIT graduate students gave Wright demos of their research projects, all involving energy-generation innovations. Wright readily engaged with all the graduate students about the technologies and the parameters of the devices, and asked the students about their own careers.
Wright was accompanied on the lab tour by MIT Provost Anantha Chandrakasan, himself an expert in developing energy-efficient systems. Chandrakasan delivered closing remarks at the forum in the Samberg Center, noting MIT’s “strong partnership with the Department of Energy” and its “long and proud history of engaging industry.”
As such, Chandrakasan said, MIT has a “role as a resource in service of the nation, so please don’t hesitate to call on us.”
MIT-affiliated physicists win McMillan Award for discovery of exotic electronic stateJiaqi Cai and Zhengguang Lu independently discovered that electrons can become fractions of themselves.Last year, MIT physicists reported in the journal Nature that electrons can become fractions of themselves in graphene, an atomically thin form of carbon. This exotic electronic state, called the fractional quantum anomalous Hall effect (FQAHE), could enable more robust forms of quantum computing.
Now two young MIT-affiliated physicists involved in the discovery of FQAHE have been named the 2025 recipients of the McMillan Award from the University of Illinois for their work. Jiaqi Cai and Zhengguang Lu won the award “for the discovery of fractional anomalous quantum hall physics in 2D moiré materials.”
Cai is currently a Pappalardo Fellow at MIT working with Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics, and collaborating with several other labs at MIT including Long Ju, the Lawrence and Sarah W. Biedenharn Career Development Associate Professor in the MIT Department of Physics. He discovered FQAHE while working in the laboratory of Professor Xiaodong Xu at the University of Washington.
Lu discovered FQAHE while working as a postdoc Ju's lab and has since become an assistant professor at Florida State University.
The two independent discoveries were made in the same year.
“The McMillan award is the highest honor that a young condensed matter physicist can receive,” says Ju. “My colleagues and I in the Condensed Matter Experiment and the Condensed Matter Theory Group are very proud of Zhengguang and Jiaqi.”
Ju and Jarillo-Herrero are both also affiliated with the Materials Research Laboratory.
In addition to a monetary prize and a plaque, Lu and Cai will give a colloquium on their work at the University of Illinois this fall.
Martin Trust Center for MIT Entrepreneurship welcomes Ana Bakshi as new executive directorBakshi will help shape and scale entrepreneurship education and platform at MIT.The Martin Trust Center for MIT Entrepreneurship announced that Ana Bakshi has been named its new executive director. Bakshi started in the role earlier this month at the start of the school year and will collaborate closely with the managing director, Ethernet Inventors Professor of the Practice Bill Aulet, to elevate the center to higher levels.
“Ana is uniquely qualified for this role through her knowledge and experience in entrepreneurship education at the highest levels, paired with her exceptional leadership and execution skills,” says Aulet. “Ana is committed to creating the highest-quality centers and institutes for entrepreneurs, first at King’s College London, where we met over 10 years ago, and then at Oxford University. This ideal skill set is compounded by her experience in leading high-growth companies, most recently as the chief operation officer in an award-winning AI startup. I’m honored and thrilled to welcome her to MIT — her knowledge and energy will greatly elevate our community, and the field as a whole.”
A rapidly changing environment creates imperative for raising the bar for entrepreneurship education
The need to raise the bar for innovation-driven entrepreneurship education is of utmost importance in today's world. The rate of change is getting faster and faster every day, especially with artificial intelligence, and is generating new problems that need to be solved, as well as exacerbating existing problems in climate, health care, manufacturing, future of work, education, and economic stratification, to name but a few. The world needs more entrepreneurs and better entrepreneurs.
Bakshi joins the Trust Center at a time when MIT is at the forefront of helping to develop people and systems that can turn challenges into opportunities using an entrepreneurial mindset, skill set, and way of operating. Bakshi’s deep experience and success will be key to unlocking this opportunity. “I am honored to be joining MIT and the Trust Center at a time when education and entrepreneurship have the chance to shape the greatest good for the greatest number,” Bakshi says. “In an era defined by both extraordinary challenges and extraordinary possibilities, the future will be built by those bold enough to try, and MIT will be at the forefront of this.”
Translating academic research into real-world impact
Bakshi has built two world-class entrepreneurship centers from the ground up. She served as the founding director at King’s College and then at Oxford. In this role, she was responsible for all aspects of these centers, including fundraising.
While at Oxford, she authored a data-driven approach to determining efficacy of outcomes for their programs, as evidenced by a 61-page study, “Universities: Drivers of Prosperity and Economic Recovery.”
As the director of the Oxford Foundry (Oxford’s cross-university entrepreneurship center), Bakshi focused on investing in ambitious founders and talent. The center was backed by global entrepreneurial leaders such as the founders of LinkedIn and Twitter, with corporate partnerships including Santander and EY, and investment funds including Oxford Science Enterprises (OSE). As of 2021, the startups supported by the Foundry and King’s College have raised over $500 million and have created nearly 3,000 jobs, spanning diverse industries including health tech, climate tech, cybersecurity, fintech, and deep tech spinouts focusing on world-class science.
In addition, she built the highly successful and economically sustainable Entrepreneurship School, Oxford’s first digital online learning platform.
Bakshi comes to MIT after having worked in the private sector as the chief operating officer (COO) in a rapidly growing artificial intelligence startup for almost two years, Quench.ai, with offices in London and New York City. She was the first C-suite employee at Quench.ai, serving as COO and now senior advisor, helping companies unlock value from their knowledge through AI.
Right place, right time, right person moving at the speed of MIT AI
Since its inception, then turbocharged in the 1940s with the creation and operation of the RadLab, and continuing to this day, entrepreneurship is at the core of MIT’s identity and mission.
"MIT has been a leader in entrepreneurship for decades. It’s now the third leg of the school, alongside teaching and research,” says Mark Gorenberg ’76, chair of the MIT Corporation. “I’m excited to have such a transformative leader as Ana join the Trust Center team, and I look forward to the impact she will have on the students and the wider academic community at MIT as we enter an exciting new phase in company building, driven by the accelerated use of AI and emerging technologies."
“In a time where we are rethinking management education, entrepreneurship as an interdisciplinary field to create impact is even more important to our future. To have such an experienced and accomplished leader in academia and the startup world, especially in AI, reinforces our commitment to be a global leader in this field,” says Richard M. Locke, John C Head III Dean at the MIT Sloan School of Management.
“MIT is a unique hub of research, innovation, and entrepreneurship, and that special mix creates massive positive impact that ripples around the world,” says Frederic Kerrest, MIT Sloan MBA ’09, co-founder of Okta, and member of the MIT Corporation. “In a rapidly changing, AI-driven world, Ana has the skills and experience to further accelerate MIT’s global leadership in entrepreneurship education to ensure that our students launch and scale the next generation of groundbreaking, innovation-driven startups.”
Prior to her time at Oxford and King’s College, Bakshi served as an elected councilor representing 6,000-plus constituents, held roles in international nongovernmental organizations, and led product execution strategy at MAHI, an award-winning family-led craft sauce startup, available in thousands of major retailers across the U.K. Bakshi sits on the advisory council for conservation charity Save the Elephants, leveraging AI-driven and scientific approaches to reduce human-wildlife conflict and protect elephant populations. Her work and impact have been featured across FT, Forbes, BBC, The Times, and The Hill. Bakshi was twice honored as a Top 50 Woman in Tech (U.K.), most recently in 2025.
“As AI changes how we learn, how we build, and how we scale, my focus will be on helping MIT expand its support for phenomenal talent — students and faculty — with the skills, ecosystem, and backing to turn knowledge into impact,” Bakshi says.
35 years of impact to date
The Trust Center was founded in 1990 by the late Professor Edward Roberts and serves all MIT students across all schools and all disciplines. It supports 60-plus courses and extensive extracurricular programming, including the delta v academic accelerator. Much of the work of the center is generated through the Disciplined Entrepreneurship methodology, which offers a proven approach to create new ventures. Over a thousand schools and other organizations across the world use Disciplined Entrepreneurship books and resources to teach entrepreneurship.
Now, with AI-powered tools like Orbit and JetPack, the Trust Center is changing the way that entrepreneurship is taught and practiced. Its mission is to produce the next generation of innovation-driven entrepreneurs while advancing the field more broadly to make it both rigorous and practical. This approach of leveraging proven evidence-based methodology, emerging technology, the ingenuity of MIT students, and responding to industry shifts is similar to how MIT established the field of chemical engineering in the 1890s. The desired result in both cases was to create a comprehensive, integrated, scalable, rigorous, and practical curriculum to create a new workforce to address the nation’s and world’s greatest challenges.
A simple formula could guide the design of faster-charging, longer-lasting batteriesMIT researchers developed a model that explains lithium intercalation rates in lithium-ion batteries.At the heart of all lithium-ion batteries is a simple reaction: Lithium ions dissolved in an electrolyte solution “intercalate” or insert themselves into a solid electrode during battery discharge. When they de-intercalate and return to the electrolyte, the battery charges.
This process happens thousands of times throughout the life of a battery. The amount of power that the battery can generate, and how quickly it can charge, depend on how fast this reaction happens. However, little is known about the exact mechanism of this reaction, or the factors that control its rate.
In a new study, MIT researchers have measured lithium intercalation rates in a variety of different battery materials and used that data to develop a new model of how the reaction is controlled. Their model suggests that lithium intercalation is governed by a process known as coupled ion-electron transfer, in which an electron is transferred to the electrode along with a lithium ion.
Insights gleaned from this model could guide the design of more powerful and faster charging lithium-ion batteries, the researchers say.
“What we hope is enabled by this work is to get the reactions to be faster and more controlled, which can speed up charging and discharging,” says Martin Bazant, the Chevron Professor of Chemical Engineering and a professor of mathematics at MIT.
The new model may also help scientists understand why tweaking electrodes and electrolytes in certain ways leads to increased energy, power, and battery life — a process that has mainly been done by trial and error.
“This is one of these papers where now we began to unify the observations of reaction rates that we see with different materials and interfaces, in one theory of coupled electron and ion transfer for intercalation, building up previous work on reaction rates,” says Yang Shao-Horn, the J.R. East Professor of Engineering at MIT and a professor of mechanical engineering, materials science and engineering, and chemistry.
Shao-Horn and Bazant are the senior authors of the paper, which appears today in Science. The paper’s lead authors are Yirui Zhang PhD ’22, who is now an assistant professor at Rice University; Dimitrios Fraggedakis PhD ’21, who is now an assistant professor at Princeton University; Tao Gao, a former MIT postdoc who is now an assistant professor at the University of Utah; and MIT graduate student Shakul Pathak.
Modeling lithium flow
For many decades, scientists have hypothesized that the rate of lithium intercalation at a lithium-ion battery electrode is determined by how quickly lithium ions can diffuse from the electrolyte into the electrode. This reaction, they believed, was governed by a model known as the Butler-Volmer equation, originally developed almost a century ago to describe the rate of charge transfer during an electrochemical reaction.
However, when researchers have tried to measure lithium intercalation rates, the measurements they obtained were not always consistent with the rates predicted by the Butler-Volmer equation. Furthermore, obtaining consistent measurements across labs has been difficult, with different research teams reporting measurements for the same reaction that varied by a factor of up to 1 billion.
In the new study, the MIT team measured lithium intercalation rates using an electrochemical technique that involves applying repeated, short bursts of voltage to an electrode. They generated these measurements for more than 50 combinations of electrolytes and electrodes, including lithium nickel manganese cobalt oxide, which is commonly used in electric vehicle batteries, and lithium cobalt oxide, which is found in the batteries that power most cell phones, laptops, and other portable electronics.
For these materials, the measured rates are much lower than has previously been reported, and they do not correspond to what would be predicted by the traditional Butler-Volmer model.
The researchers used the data to come up with an alternative theory of how lithium intercalation occurs at the surface of an electrode. This theory is based on the assumption that in order for a lithium ion to enter an electrode, an electron from the electrolyte solution must be transferred to the electrode at the same time.
“The electrochemical step is not lithium insertion, which you might think is the main thing, but it’s actually electron transfer to reduce the solid material that is hosting the lithium,” Bazant says. “Lithium is intercalated at the same time that the electron is transferred, and they facilitate one another.”
This coupled-electron ion transfer (CIET) lowers the energy barrier that must be overcome for the intercalation reaction to occur, making it more likely to happen. The mathematical framework of CIET allowed the researchers to make reaction rate predictions, which were validated by their experiments and substantially different from those made by the Butler-Volmer model.
Faster charging
In this study, the researchers also showed that they could tune intercalation rates by changing the composition of the electrolyte. For example, swapping in different anions can lower the amount of energy needed to transfer the lithium and electron, making the process more efficient.
“Tuning the intercalation kinetics by changing electrolytes offers great opportunities to enhance the reaction rates, alter electrode designs, and therefore enhance the battery power and energy,” Shao-Horn says.
Shao-Horn’s lab and their collaborators have been using automated experiments to make and test thousands of different electrolytes, which are used to develop machine-learning models to predict electrolytes with enhanced functions.
The findings could also help researchers to design batteries that would charge faster, by speeding up the lithium intercalation reaction. Another goal is reducing the side reactions that can cause battery degradation when electrons are picked off the electrode and dissolve into the electrolyte.
“If you want to do that rationally, not just by trial and error, you need some kind of theoretical framework to know what are the important material parameters that you can play with,” Bazant says. “That’s what this paper tries to provide.”
The research was funded by Shell International Exploration and Production and the Toyota Research Institute through the D3BATT Center for Data-Driven Design of Rechargeable Batteries.
Accounting for uncertainty to help engineers design complex systemsThe approach could enable autonomous vehicles, commercial aircraft, or transportation networks that are more reliable in the face of real-world unpredictability.Designing a complex electronic device like a delivery drone involves juggling many choices, such as selecting motors and batteries that minimize cost while maximizing the payload the drone can carry or the distance it can travel.
Unraveling that conundrum is no easy task, but what happens if the designers don’t know the exact specifications of each battery and motor? On top of that, the real-world performance of these components will likely be affected by unpredictable factors, like changing weather along the drone’s route.
MIT researchers developed a new framework that helps engineers design complex systems in a way that explicitly accounts for such uncertainty. The framework allows them to model the performance tradeoffs of a device with many interconnected parts, each of which could behave in unpredictable ways.
Their technique captures the likelihood of many outcomes and tradeoffs, giving designers more information than many existing approaches which, at most, can usually only model best-case and worst-case scenarios.
Ultimately, this framework could help engineers develop complex systems like autonomous vehicles, commercial aircraft, or even regional transportation networks that are more robust and reliable in the face of real-world unpredictability.
“In practice, the components in a device never behave exactly like you think they will. If someone has a sensor whose performance is uncertain, and an algorithm that is uncertain, and the design of a robot that is also uncertain, now they have a way to mix all these uncertainties together so they can come up with a better design,” says Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering at MIT, a principal investigator in the Laboratory for Information and Decision Systems (LIDS), an affiliate faculty with the Institute for Data, Systems, and Society (IDSS), and senior author of a paper on this framework.
Zardini is joined on the paper by lead author Yujun Huang, an MIT graduate student; and Marius Furter, a graduate student at the University of Zurich. The research will be presented at the IEEE Conference on Decision and Control.
Considering uncertainty
The Zardini Group studies co-design, a method for designing systems made of many interconnected components, from robots to regional transportation networks.
The co-design language breaks a complex problem into a series of boxes, each representing one component, that can be combined in different ways to maximize outcomes or minimize costs. This allows engineers to solve complex problems in a feasible amount of time.
In prior work, the researchers modeled each co-design component without considering uncertainty. For instance, the performance of each sensor the designers could choose for a drone was fixed.
But engineers often don’t know the exact performance specifications of each sensor, and even if they do, it is unlikely the senor will perfectly follow its spec sheet. At the same time, they don’t know how each sensor will behave once integrated into a complex device, or how performance will be affected by unpredictable factors like weather.
“With our method, even if you are unsure what the specifications of your sensor will be, you can still design the robot to maximize the outcome you care about,” says Furter.
To accomplish this, the researchers incorporated this notion of uncertainty into an existing framework based on category theory.
Using some mathematical tricks, they simplified the problem into a more general structure. This allows them to use the tools of category theory to solve co-design problems in a way that considers a range of uncertain outcomes.
By reformulating the problem, the researchers can capture how multiple design choices affect one another even when their individual performance is uncertain.
This approach is also simpler than many existing tools that typically require extensive domain expertise. With their plug-and-play system, one can rearrange the components in the system without violating any mathematical constraints.
And because no specific domain expertise is required, the framework could be used by a multidisciplinary team where each member designs one component of a larger system.
“Designing an entire UAV isn’t feasible for just one person, but designing a component of a UAV is. By providing the framework for how these components work together in a way that considers uncertainty, we’ve made it easier for people to evaluate the performance of the entire UAV system,” Huang says.
More detailed information
The researchers used this new approach to choose perception systems and batteries for a drone that would maximize its payload while minimizing its lifetime cost and weight.
While each perception system may offer a different detection accuracy under varying weather conditions, the designer doesn’t know exactly how its performance will fluctuate. This new system allows the designer to take these uncertainties into consideration when thinking about the drone’s overall performance.
And unlike other approaches, their framework reveals distinct advantages of each battery technology.
For instance, their results show that at lower payloads, nickel-metal hydride batteries provide the lowest expected lifetime cost. This insight would be impossible to fully capture without accounting for uncertainty, Zardini says.
While another method might only be able to show the best-case and worst-case performance scenarios of lithium polymer batteries, their framework gives the user more detailed information.
For example, it shows that if the drone’s payload is 1,750 grams, there is a 12.8 percent chance the battery design would be infeasible.
“Our system provides the tradeoffs, and then the user can reason about the design,” he adds.
In the future, the researchers want to improve the computational efficiency of their problem-solving algorithms. They also want to extend this approach to situations where a system is designed by multiple parties that are collaborative and competitive, like a transportation network in which rail companies operate using the same infrastructure.
“As the complexity of systems grow, and involves more disparate components, we need a formal framework in which to design these systems. This paper presents a way to compose large systems from modular components, understand design trade-offs, and importantly do so with a notion of uncertainty. This creates an opportunity to formalize the design of large-scale systems with learning-enabled components,” says Aaron Ames, the Bren Professor of Mechanical and Civil Engineering, Control and Dynamical Systems, and Aerospace at Caltech, who was not involved with this research.
Palladium filters could enable cheaper, more efficient generation of hydrogen fuelThe novel design allows the membranes to withstand high temperatures when separating hydrogen from gas mixtures.Palladium is one of the keys to jump-starting a hydrogen-based energy economy. The silvery metal is a natural gatekeeper against every gas except hydrogen, which it readily lets through. For its exceptional selectivity, palladium is considered one of the most effective materials at filtering gas mixtures to produce pure hydrogen.
Today, palladium-based membranes are used at commercial scale to provide pure hydrogen for semiconductor manufacturing, food processing, and fertilizer production, among other applications in which the membranes operate at modest temperatures. If palladium membranes get much hotter than around 800 kelvins, they can break down.
Now, MIT engineers have developed a new palladium membrane that remains resilient at much higher temperatures. Rather than being made as a continuous film, as most membranes are, the new design is made from palladium that is deposited as “plugs” into the pores of an underlying supporting material. At high temperatures, the snug-fitting plugs remain stable and continue separating out hydrogen, rather than degrading as a surface film would.
The thermally stable design opens opportunities for membranes to be used in hydrogen-fuel-generating technologies such as compact steam methane reforming and ammonia cracking — technologies that are designed to operate at much higher temperatures to produce hydrogen for zero-carbon-emitting fuel and electricity.
“With further work on scaling and validating performance under realistic industrial feeds, the design could represent a promising route toward practical membranes for high-temperature hydrogen production,” says Lohyun Kim PhD ’24, a former graduate student in MIT’s Department of Mechanical Engineering.
Kim and his colleagues report details of the new membrane in a study appearing today in the journal Advanced Functional Materials. The study’s co-authors are Randall Field, director of research at the MIT Energy Initiative (MITEI); former MIT chemical engineering graduate student Chun Man Chow PhD ’23; Rohit Karnik, the Jameel Professor in the Department of Mechanical Engineering at MIT and the director of the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS); and Aaron Persad, a former MIT research scientist in mechanical engineering who is now an assistant professor at the University of Maryland Eastern Shore.
Compact future
The team’s new design came out of a MITEI project related to fusion energy. Future fusion power plants, such as the one MIT spinout Commonwealth Fusion Systems is designing, will involve circulating hydrogen isotopes of deuterium and tritium at extremely high temperatures to produce energy from the isotopes’ fusing. The reactions inevitably produce other gases that will have to be separated, and the hydrogen isotopes will be recirculated into the main reactor for further fusion.
Similar issues arise in a number of other processes for producing hydrogen, where gases must be separated and recirculated back into a reactor. Concepts for such recirculating systems would require first cooling down the gas before it can pass through hydrogen-separating membranes — an expensive and energy-intensive step that would involve additional machinery and hardware.
“One of the questions we were thinking about is: Can we develop membranes which could be as close to the reactor as possible, and operate at higher temperatures, so we don’t have to pull out the gas and cool it down first?” Karnik says. “It would enable more energy-efficient, and therefore cheaper and compact, fusion systems.”
The researchers looked for ways to improve the temperature resistance of palladium membranes. Palladium is the most effective metal used today to separate hydrogen from a variety of gas mixtures. It naturally attracts hydrogen molecules (H2) to its surface, where the metal’s electrons interact with and weaken the molecule’s bonds, causing H2 to temporarily break apart into its respective atoms. The individual atoms then diffuse through the metal and join back up on the other side as pure hydrogen.
Palladium is highly effective at permeating hydrogen, and only hydrogen, from streams of various gases. But conventional membranes typically can operate at temperatures of up to 800 kelvins before the film starts to form holes or clumps up into droplets, allowing other gases to flow through.
Plugging in
Karnik, Kim and their colleagues took a different design approach. They observed that at high temperatures, palladium will start to shrink up. In engineering terms, the material is acting to reduce surface energy. To do this, palladium, and most other materials and even water, will pull apart and form droplets with the smallest surface energy. The lower the surface energy, the more stable the material can be against further heating.
This gave the team an idea: If a supporting material’s pores could be “plugged” with deposits of palladium — essentially already forming a droplet with the lowest surface energy — the tight quarters might substantially increase palladium’s heat tolerance while preserving the membrane’s selectivity for hydrogen.
To test this idea, they fabricated small chip-sized samples of membrane using a porous silica supporting layer (each pore measuring about half a micron wide), onto which they deposited a very thin layer of palladium. They applied techniques to essentially grow the palladium into the pores, and polished down the surface to remove the palladium layer and leave palladium only inside the pores.
They then placed samples in a custom-built apparatus in which they flowed hydrogen-containing gas of various mixtures and temperatures to test its separation performance. The membranes remained stable and continued to separate hydrogen from other gases even after experiencing temperatures of up to 1,000 kelvins for over 100 hours — a significant improvement over conventional film-based membranes.
“The use of palladium film membranes are generally limited to below around 800 kelvins, at which point they degrade,” Kim says. “Our plug design therefore extends palladium’s effective heat resilience by roughly at least 200 kelvins and maintains integrity far longer under extreme conditions.”
These conditions are within the range of hydrogen-generating technologies such as steam methane reforming and ammonia cracking.
Steam methane reforming is an established process that has required complex, energy-intensive systems to preprocess methane to a form where pure hydrogen can be extracted. Such preprocessing steps could be replaced with a compact “membrane reactor,” through which a methane gas would directly flow, and the membrane inside would filter out pure hydrogen. Such reactors would significantly cut down the size, complexity, and cost of producing hydrogen from steam methane reforming, and Kim estimates a membrane would have to work reliably in temperatures of up to nearly 1,000 kelvins. The team’s new membrane could work well within such conditions.
Ammonia cracking is another way to produce hydrogen, by “cracking” or breaking apart ammonia. As ammonia is very stable in liquid form, scientists envision that it could be used as a carrier for hydrogen and be safely transported to a hydrogen fuel station, where ammonia could be fed into a membrane reactor that again pulls out hydrogen and pumps it directly into a fuel cell vehicle. Ammonia cracking is still largely in pilot and demonstration stages, and Kim says any membrane in an ammonia cracking reactor would likely operate at temperatures of around 800 kelvins — within the range of the group’s new plug-based design.
Karnik emphasizes that their results are just a start. Adopting the membrane into working reactors will require further development and testing to ensure it remains reliable over much longer periods of time.
“We showed that instead of making a film, if you make discretized nanostructures you can get much more thermally stable membranes,” Karnik says. “It provides a pathway for designing membranes for extreme temperatures, with the added possibility of using smaller amounts of expensive palladium, toward making hydrogen production more efficient and affordable. There is potential there.”
This work was supported by Eni S.p.A. via the MIT Energy Initiative.
This work utilized facilities at the MIT Materials Research Laboratory (MRL), the MIT Laboratory for Manufacturing and Productivity (LMP), and MIT.nano.
A cysteine-rich diet may promote regeneration of the intestinal lining, study suggestsThe findings may offer a new way to help heal tissue damage from radiation or chemotherapy treatment.A diet rich in the amino acid cysteine may have rejuvenating effects in the small intestine, according to a new study from MIT. This amino acid, the researchers discovered, can turn on an immune signaling pathway that helps stem cells to regrow new intestinal tissue.
This enhanced regeneration may help to heal injuries from radiation, which often occur in patients undergoing radiation therapy for cancer. The research was conducted in mice, but if future research shows similar results in humans, then delivering elevated quantities of cysteine, through diet or supplements, could offer a new strategy to help damaged tissue heal faster, the researchers say.
“The study suggests that if we give these patients a cysteine-rich diet or cysteine supplementation, perhaps we can dampen some of the chemotherapy or radiation-induced injury,” says Omer Yilmaz, director of the MIT Stem Cell Initiative, an associate professor of biology at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research. “The beauty here is we’re not using a synthetic molecule; we’re exploiting a natural dietary compound.”
While previous research has shown that certain types of diets, including low-calorie diets, can enhance intestinal stem cell activity, the new study is the first to identify a single nutrient that can help intestinal cells to regenerate.
Yilmaz is the senior author of the study, which appears today in Nature. Koch Institute postdoc Fangtao Chi is the paper’s lead author.
Boosting regeneration
It is well-established that diet can affect overall health: High-fat diets can lead to obesity, diabetes, and other health problems, while low-calorie diets have been shown to extend lifespans in many species. In recent years, Yilmaz’s lab has investigated how different types of diets influence stem cell regeneration, and found that high-fat diets, as well as short periods of fasting, can enhance stem cell activity in different ways.
“We know that macro diets such as high-sugar diets, high-fat diets, and low-calorie diets have a clear impact on health. But at the granular level, we know much less about how individual nutrients impact stem cell fate decisions, as well as tissue function and overall tissue health,” Yilmaz says.
In their new study, the researchers began by feeding mice a diet high in one of 20 different amino acids, the building blocks of proteins. For each group, they measured how the diet affected intestinal stem cell regeneration. Among these amino acids, cysteine had the most dramatic effects on stem cells and progenitor cells (immature cells that differentiate into adult intestinal cells).
Further studies revealed that cysteine initiates a chain of events leading to the activation of a population of immune cells called CD8 T cells. When cells in the lining of the intestine absorb cysteine from digested food, they convert it into CoA, a cofactor that is released into the mucosal lining of the intestine. There, CD8 T cells absorb CoA, which stimulates them to begin proliferating and producing a cytokine called IL-22.
IL-22 is an important player in the regulation of intestinal stem cell regeneration, but until now, it wasn’t known that CD8 T cells can produce it to boost intestinal stem cells. Once activated, those IL-22-releasing T cells are primed to help combat any kind of injury that could occur within the intestinal lining.
“What’s really exciting here is that feeding mice a cysteine-rich diet leads to the expansion of an immune cell population that we typically don’t associate with IL-22 production and the regulation of intestinal stemness,” Yilmaz says. “What happens in a cysteine-rich diet is that the pool of cells that make IL-22 increases, particularly the CD8 T-cell fraction.”
These T cells tend to congregate within the lining of the intestine, so they are already in position when needed. The researchers found that the stimulation of CD8 T cells occurred primarily in the small intestine, not in any other part of the digestive tract, which they believe is because most of the protein that we consume is absorbed by the small intestine.
Healing the intestine
In this study, the researchers showed that regeneration stimulated by a cysteine-rich diet could help to repair radiation damage to the intestinal lining. Also, in work that has not been published yet, they showed that a high-cysteine diet had a regenerative effect following treatment with a chemotherapy drug called 5-fluorouracil. This drug, which is used to treat colon and pancreatic cancers, can also damage the intestinal lining.
Cysteine is found in many high-protein foods, including meat, dairy products, legumes, and nuts. The body can also synthesize its own cysteine, by converting the amino acid methionine to cysteine — a process that takes place in the liver. However, cysteine produced in the liver is distributed through the entire body and doesn’t lead to a buildup in the small intestine the way that consuming cysteine in the diet does.
“With our high-cysteine diet, the gut is the first place that sees a high amount of cysteine,” Chi says.
Cysteine has been previously shown to have antioxidant effects, which are also beneficial, but this study is the first to demonstrate its effect on intestinal stem cell regeneration. The researchers now hope to study whether it may also help other types of stem cells regenerate new tissues. In one ongoing study, they are investigating whether cysteine might stimulate hair follicle regeneration.
They also plan to further investigate some of the other amino acids that appear to influence stem cell regeneration.
“I think we’re going to uncover multiple new mechanisms for how these amino acids regulate cell fate decisions and gut health in the small intestine and colon,” Yilmaz says.
The research was funded, in part, by the National Institutes of Health, the V Foundation, the Koch Institute Frontier Research Program via the Kathy and Curt Marble Cancer Research Fund, the Bridge Project — a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center, the American Federation for Aging Research, the MIT Stem Cell Initiative, and the Koch Institute Support (core) Grant from the National Cancer Institute.
System lets people personalize online social spaces while staying connected with othersBy enabling users to easily create social apps that serve communities’ needs, the Graffiti framework aims to promote healthier online interactions.Say a local concert venue wants to engage its community by giving social media followers an easy way to share and comment on new music from emerging artists. Rather than working within the constraints of existing social platforms, the venue might want to create its own social app with the functionality that would be best for its community. But building a new social app from scratch involves many complicated programming steps, and even if the venue can create a customized app, the organization’s followers may be unwilling to join the new platform because it could mean leaving their connections and data behind.
Now, researchers from MIT have launched a framework called Graffiti that makes building personalized social applications easier, while allowing users to migrate between multiple applications without losing their friends or data.
“We want to empower people to have control over their own designs rather than having them dictated from the top down,” says electrical engineering and computer science graduate student Theia Henderson.
Henderson and her colleagues designed Graffiti with a flexible structure so individuals have the freedom to create a variety of customized applications, from messenger apps like WhatsApp to microblogging platforms like X to location-based social networking sites like Nextdoor, all using only front-end development tools like HTML.
The protocol ensures all applications can interoperate, so content posted on one application can appear on any other application, even those with disparate designs or functionality. Importantly, Graffiti users retain control of their data, which is stored on a decentralized infrastructure rather than being held by a specific application.
While the pros and cons of implementing Graffiti at scale remain to be fully explored, the researchers hope this new approach can someday lead to healthier online interactions.
“We’ve shown that you can have a rich social ecosystem where everyone owns their own data and can use whatever applications they want to interact with whoever they want in whatever way they want. And they can have their own experiences without losing connection with the people they want to stay connected with,” says David Karger, professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
Henderson, the lead author, and Karger are joined by MIT Research Scientist David D. Clark on a paper about Graffiti, which will be presented at the ACM Symposium on User Interface Software and Technology.
Personalized, integrated applications
With Graffiti, the researchers had two main goals: to lower the barrier to creating personalized social applications and to enable those personalized applications to interoperate without requiring permission from developers.
To make the design process easier, they built a collective back-end infrastructure that all applications access to store and share content. This means developers don’t need to write any complex server code. Instead, designing a Graffiti application is more like making a website using popular tools like Vue.
Developers can also easily introduce new features and new types of content, giving them more freedom and fostering creativity.
“Graffiti is so straightforward that we used it as the infrastructure for the intro to web design class I teach, and students were able to write the front-end very easily to come up with all sorts of applications,” Karger says.
The open, interoperable nature of Graffiti means no one entity has the power to set a moderation policy for the entire platform. Instead, multiple competing and contradictory moderation services can operate, and people can choose the ones they like.
Graffiti uses the idea of “total reification,” where every action taken in Graffiti, such as liking, sharing, or blocking a post, is represented and stored as its own piece of data. A user can configure their social application to interpret or ignore those data using its own rules.
For instance, if an application is designed so a certain user is a moderator, posts blocked by that user won’t appear in the application. But for an application with different rules where that person isn’t considered a moderator, other users might just see a warning or no flag at all.
“Theia’s system lets each person pick their own moderators, avoiding the one-sized-fits-all approach to moderation taken by the major social platforms,” Karger says.
But at the same time, having no central moderator means there is no one to remove content from the platform that might be offensive or illegal.
“We need to do more research to understand if that is going to provide real, damaging consequences or if the kind of personal moderation we created can provide the protections people need,” he adds.
Empowering social media users
The researchers also had to overcome a problem known as context collapse, which conflicts with their goal of interoperation.
For instance, context collapse would occur if a person’s Tinder profile appeared on LinkedIn, or if a post intended for one group, like close friends, would create conflict with another group, such as family members. Context collapse can lead to anxiety and have social repercussions for the user and their different communities.
“We realize that interoperability can sometimes be a bad thing. People have boundaries between different social contexts, and we didn’t want to violate those,” Henderson says.
To avoid context collapse, the researchers designed Graffiti so all content is organized into distinct channels. Channels are flexible and can represent a variety of contexts, such as people, applications, locations, etc.
If a user’s post appears in an application channel but not their personal channel, others using that application will see the post, but those who only follow this user will not.
“Individuals should have the power to choose the audience for whatever they want to say,” Karger adds.
The researchers created multiple Graffiti applications to showcase personalization and interoperability, including a community-specific application for a local concert venue, a text-centric microblogging platform patterned off X, a Wikipedia-like application that enables collective editing, and a real-time messaging app with multiple moderation schemes patterned off WhatsApp and Slack.
“It also leaves room to create so many social applications people haven’t thought of yet. I’m really excited to see what people come up with when they are given full creative freedom,” Henderson says.
In the future, she and her colleagues want to explore additional social applications they could build with Graffiti. They also intend to incorporate tools like graphical editors to simplify the design process. In addition, they want to strengthen Graffiti’s security and privacy.
And while there is still a long way to go before Graffiti could be implemented at scale, the researchers are currently running a user study as they explore the potential positive and negative impacts the system could have on the social media landscape.
MIT cognitive scientists reveal why some sentences stand out from othersSentences that are highly dissimilar from anything we’ve seen before are more likely to be remembered accurately.“You still had to prove yourself.”
“Every cloud has a blue lining!”
Which of those sentences are you most likely to remember a few minutes from now? If you guessed the second, you’re probably correct.
According to a new study from MIT cognitive scientists, sentences that stick in your mind longer are those that have distinctive meanings, making them stand out from sentences you’ve previously seen. They found that meaning, not any other trait, is the most important feature when it comes to memorability.
“One might have thought that when you remember sentences, maybe it’s all about the visual features of the sentence, but we found that that was not the case. A big contribution of this paper is pinning down that it is the meaning-related space that makes sentences memorable,” says Greta Tuckute PhD ’25, who is now a research fellow at Harvard University’s Kempner Institute.
The findings support the hypothesis that sentences with distinctive meanings — like “Does olive oil work for tanning?” — are stored in brain space that is not cluttered with sentences that mean almost the same thing. Sentences with similar meanings end up densely packed together and are therefore more difficult to recognize confidently later on, the researchers believe.
“When you encode sentences that have a similar meaning, there’s feature overlap in that space. Therefore, a particular sentence you’ve encoded is not linked to a unique set of features, but rather to a whole bunch of features that may overlap with other sentences,” says Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences (BCS), a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.
Tuckute and Thomas Clark, an MIT graduate student, are the lead authors of the paper, which appears in the Journal of Memory and Language. MIT graduate student Bryan Medina is also an author.
Distinctive sentences
What makes certain things more memorable than others is a longstanding question in cognitive science and neuroscience. In a 2011 study, Aude Oliva, now a senior research scientist at MIT and MIT director of the MIT-IBM Watson AI Lab, showed that not all items are created equal: Some types of images are much easier to remember than others, and people are remarkably consistent in what images they remember best.
In that study, Oliva and her colleagues found that, in general, images with people in them are the most memorable, followed by images of human-scale space and close-ups of objects. Least memorable are natural landscapes.
As a follow-up to that study, Fedorenko and Oliva, along with Ted Gibson, another faculty member in BCS, teamed up to determine if words also vary in their memorability. In a study published earlier this year, co-led by Tuckute and Kyle Mahowald, a former PhD student in BCS, the researchers found that the most memorable words are those that have the most distinctive meanings.
Words are categorized as being more distinctive if they have a single meaning, and few or no synonyms — for example, words like “pineapple” or “avalanche” which were found to be very memorable. On the other hand, words that can have multiple meanings, such as “light,” or words that have many synonyms, like “happy,” were more difficult for people to recognize accurately.
In the new study, the researchers expanded their scope to analyze the memorability of sentences. Just like words, some sentences have very distinctive meanings, while others communicate similar information in slightly different ways.
To do the study, the researchers assembled a collection of 2,500 sentences drawn from publicly available databases that compile text from novels, news articles, movie dialogues, and other sources. Each sentence that they chose contained exactly six words.
The researchers then presented a random selection of about 1,000 of these sentences to each study participant, including repeats of some sentences. Each of the 500 participants in the study was asked to press a button when they saw a sentence that they remembered seeing earlier.
The most memorable sentences — the ones where participants accurately and quickly indicated that they had seen them before — included strings such as “Homer Simpson is hungry, very hungry,” and “These mosquitoes are — well, guinea pigs.”
Those memorable sentences overlapped significantly with sentences that were determined as having distinctive meanings as estimated through the high-dimensional vector space of a large language model (LLM) known as Sentence BERT. That model is able to generate sentence-level representations of sentences, which can be used for tasks like judging meaning similarity between sentences. This model provided researchers with a distinctness score for each sentence based on its semantic similarity to other sentences.
The researchers also evaluated the sentences using a model that predicts memorability based on the average memorability of the individual words in the sentence. This model performed fairly well at predicting overall sentence memorability, but not as well as Sentence BERT. This suggests that the meaning of a sentence as a whole — above and beyond the contributions from individual words — determines how memorable it will be, the researchers say.
Noisy memories
While cognitive scientists have long hypothesized that the brain’s memory banks have a limited capacity, the findings of the new study support an alternative hypothesis that would help to explain how the brain can continue forming new memories without losing old ones.
This alternative, known as the noisy representation hypothesis, says that when the brain encodes a new memory, be it an image, a word, or a sentence, it is represented in a noisy way — that is, this representation is not identical to the stimulus, and some information is lost. For example, for an image, you may not encode the exact viewing angle at which an object is shown, and for a sentence, you may not remember the exact construction used.
Under this theory, a new sentence would be encoded in a similar part of the memory space as sentences that carry a similar meanings, whether they were encountered recently or sometime across a lifetime of language experience. This jumbling of similar meanings together increases the amount of noise and can make it much harder, later on, to remember the exact sentence you have seen before.
“The representation is gradually going to accumulate some noise. As a result, when you see an image or a sentence for a second time, your accuracy at judging whether you’ve seen it before will be affected, and it’ll be less than 100 percent in most cases,” Clark says.
However, if a sentence has a unique meaning that is encoded in a less densely crowded space, it will be easier to pick out later on.
“Your memory may still be noisy, but your ability to make judgments based on the representations is less affected by that noise because the representation is so distinctive to begin with,” Clark says.
The researchers now plan to study whether other features of sentences, such as more vivid and descriptive language, might also contribute to making them more memorable, and how the language system may interact with the hippocampal memory structures during the encoding and retrieval of memories.
The research was funded, in part, by the National Institutes of Health, the McGovern Institute, the Department of Brain and Cognitive Sciences, the Simons Center for the Social Brain, and the MIT Quest for Intelligence.
Responding to the climate impact of generative AIExplosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.
The energy demands of generative AI are expected to continue increasing dramatically over the next decade.
For instance, an April 2025 report from the International Energy Agency predicts that the global electricity demand from data centers, which house the computing infrastructure to train and deploy AI models, will more than double by 2030, to around 945 terawatt-hours. While not all operations performed in a data center are AI-related, this total amount is slightly more than the energy consumption of Japan.
Moreover, an August 2025 analysis from Goldman Sachs Research forecasts that about 60 percent of the increasing electricity demands from data centers will be met by burning fossil fuels, increasing global carbon emissions by about 220 million tons. In comparison, driving a gas-powered car for 5,000 miles produces about 1 ton of carbon dioxide.
These statistics are staggering, but at the same time, scientists and engineers at MIT and around the world are studying innovations and interventions to mitigate AI’s ballooning carbon footprint, from boosting the efficiency of algorithms to rethinking the design of data centers.
Considering carbon emissions
Talk of reducing generative AI’s carbon footprint is typically centered on “operational carbon” — the emissions used by the powerful processors, known as GPUs, inside a data center. It often ignores “embodied carbon,” which are emissions created by building the data center in the first place, says Vijay Gadepally, senior scientist at MIT Lincoln Laboratory, who leads research projects in the Lincoln Laboratory Supercomputing Center.
Constructing and retrofitting a data center, built from tons of steel and concrete and filled with air conditioning units, computing hardware, and miles of cable, consumes a huge amount of carbon. In fact, the environmental impact of building data centers is one reason companies like Meta and Google are exploring more sustainable building materials. (Cost is another factor.)
Plus, data centers are enormous buildings — the world’s largest, the China Telecomm-Inner Mongolia Information Park, engulfs roughly 10 million square feet — with about 10 to 50 times the energy density of a normal office building, Gadepally adds.
“The operational side is only part of the story. Some things we are working on to reduce operational emissions may lend themselves to reducing embodied carbon, too, but we need to do more on that front in the future,” he says.
Reducing operational carbon emissions
When it comes to reducing operational carbon emissions of AI data centers, there are many parallels with home energy-saving measures. For one, we can simply turn down the lights.
“Even if you have the worst lightbulbs in your house from an efficiency standpoint, turning them off or dimming them will always use less energy than leaving them running at full blast,” Gadepally says.
In the same fashion, research from the Supercomputing Center has shown that “turning down” the GPUs in a data center so they consume about three-tenths the energy has minimal impacts on the performance of AI models, while also making the hardware easier to cool.
Another strategy is to use less energy-intensive computing hardware.
Demanding generative AI workloads, such as training new reasoning models like GPT-5, usually need many GPUs working simultaneously. The Goldman Sachs analysis estimates that a state-of-the-art system could soon have as many as 576 connected GPUs operating at once.
But engineers can sometimes achieve similar results by reducing the precision of computing hardware, perhaps by switching to less powerful processors that have been tuned to handle a specific AI workload.
There are also measures that boost the efficiency of training power-hungry deep-learning models before they are deployed.
Gadepally’s group found that about half the electricity used for training an AI model is spent to get the last 2 or 3 percentage points in accuracy. Stopping the training process early can save a lot of that energy.
“There might be cases where 70 percent accuracy is good enough for one particular application, like a recommender system for e-commerce,” he says.
Researchers can also take advantage of efficiency-boosting measures.
For instance, a postdoc in the Supercomputing Center realized the group might run a thousand simulations during the training process to pick the two or three best AI models for their project.
By building a tool that allowed them to avoid about 80 percent of those wasted computing cycles, they dramatically reduced the energy demands of training with no reduction in model accuracy, Gadepally says.
Leveraging efficiency improvements
Constant innovation in computing hardware, such as denser arrays of transistors on semiconductor chips, is still enabling dramatic improvements in the energy efficiency of AI models.
Even though energy efficiency improvements have been slowing for most chips since about 2005, the amount of computation that GPUs can do per joule of energy has been improving by 50 to 60 percent each year, says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory and a principal investigator at MIT’s Initiative on the Digital Economy.
“The still-ongoing ‘Moore’s Law’ trend of getting more and more transistors on chip still matters for a lot of these AI systems, since running operations in parallel is still very valuable for improving efficiency,” says Thomspon.
Even more significant, his group’s research indicates that efficiency gains from new model architectures that can solve complex problems faster, consuming less energy to achieve the same or better results, is doubling every eight or nine months.
Thompson coined the term “negaflop” to describe this effect. The same way a “negawatt” represents electricity saved due to energy-saving measures, a “negaflop” is a computing operation that doesn’t need to be performed due to algorithmic improvements.
These could be things like “pruning” away unnecessary components of a neural network or employing compression techniques that enable users to do more with less computation.
“If you need to use a really powerful model today to complete your task, in just a few years, you might be able to use a significantly smaller model to do the same thing, which would carry much less environmental burden. Making these models more efficient is the single-most important thing you can do to reduce the environmental costs of AI,” Thompson says.
Maximizing energy savings
While reducing the overall energy use of AI algorithms and computing hardware will cut greenhouse gas emissions, not all energy is the same, Gadepally adds.
“The amount of carbon emissions in 1 kilowatt hour varies quite significantly, even just during the day, as well as over the month and year,” he says.
Engineers can take advantage of these variations by leveraging the flexibility of AI workloads and data center operations to maximize emissions reductions. For instance, some generative AI workloads don’t need to be performed in their entirety at the same time.
Splitting computing operations so some are performed later, when more of the electricity fed into the grid is from renewable sources like solar and wind, can go a long way toward reducing a data center’s carbon footprint, says Deepjyoti Deka, a research scientist in the MIT Energy Initiative.
Deka and his team are also studying “smarter” data centers where the AI workloads of multiple companies using the same computing equipment are flexibly adjusted to improve energy efficiency.
“By looking at the system as a whole, our hope is to minimize energy use as well as dependence on fossil fuels, while still maintaining reliability standards for AI companies and users,” Deka says.
He and others at MITEI are building a flexibility model of a data center that considers the differing energy demands of training a deep-learning model versus deploying that model. Their hope is to uncover the best strategies for scheduling and streamlining computing operations to improve energy efficiency.
The researchers are also exploring the use of long-duration energy storage units at data centers, which store excess energy for times when it is needed.
With these systems in place, a data center could use stored energy that was generated by renewable sources during a high-demand period, or avoid the use of diesel backup generators if there are fluctuations in the grid.
“Long-duration energy storage could be a game-changer here because we can design operations that really change the emission mix of the system to rely more on renewable energy,” Deka says.
In addition, researchers at MIT and Princeton University are developing a software tool for investment planning in the power sector, called GenX, which could be used to help companies determine the ideal place to locate a data center to minimize environmental impacts and costs.
Location can have a big impact on reducing a data center’s carbon footprint. For instance, Meta operates a data center in Lulea, a city on the coast of northern Sweden where cooler temperatures reduce the amount of electricity needed to cool computing hardware.
Thinking farther outside the box (way farther), some governments are even exploring the construction of data centers on the moon where they could potentially be operated with nearly all renewable energy.
AI-based solutions
Currently, the expansion of renewable energy generation here on Earth isn’t keeping pace with the rapid growth of AI, which is one major roadblock to reducing its carbon footprint, says Jennifer Turliuk MBA ’25, a short-term lecturer, former Sloan Fellow, and former practice leader of climate and energy AI at the Martin Trust Center for MIT Entrepreneurship.
The local, state, and federal review processes required for a new renewable energy projects can take years.
Researchers at MIT and elsewhere are exploring the use of AI to speed up the process of connecting new renewable energy systems to the power grid.
For instance, a generative AI model could streamline interconnection studies that determine how a new project will impact the power grid, a step that often takes years to complete.
And when it comes to accelerating the development and implementation of clean energy technologies, AI could play a major role.
“Machine learning is great for tackling complex situations, and the electrical grid is said to be one of the largest and most complex machines in the world,” Turliuk adds.
For instance, AI could help optimize the prediction of solar and wind energy generation or identify ideal locations for new facilities.
It could also be used to perform predictive maintenance and fault detection for solar panels or other green energy infrastructure, or to monitor the capacity of transmission wires to maximize efficiency.
By helping researchers gather and analyze huge amounts of data, AI could also inform targeted policy interventions aimed at getting the biggest “bang for the buck” from areas such as renewable energy, Turliuk says.
To help policymakers, scientists, and enterprises consider the multifaceted costs and benefits of AI systems, she and her collaborators developed the Net Climate Impact Score.
The score is a framework that can be used to help determine the net climate impact of AI projects, considering emissions and other environmental costs along with potential environmental benefits in the future.
At the end of the day, the most effective solutions will likely result from collaborations among companies, regulators, and researchers, with academia leading the way, Turliuk adds.
“Every day counts. We are on a path where the effects of climate change won’t be fully known until it is too late to do anything about it. This is a once-in-a-lifetime opportunity to innovate and make AI systems less carbon-intense,” she says.