Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Eminent theoretical physicist Jeffrey Goldstone, an MIT professor emeritus, Dirac Medalist, and holder of the Department of Physics’ Cecil and Ida Green chair, died Oct. 12 at age 93. 

A member of the MIT physics community for 49 years, Goldstone was especially known for his influential work in quantum field theory as well as contributions to quantum optimization algorithms. Notably, he is the namesake of the Goldstone boson and the related Goldstone theorem.

“Jeffrey was a giant of theoretical physics who will be long remembered and very much missed,” says Tracy Slatyer, director of the MIT Center for Theoretical Physics – a Leinweber Institute (CTP-LI) and professor of physics. “He contributed greatly to the CTP-LI over the years, and it was an honor to have him as a colleague.”

“Goldstone was a brilliant theoretical physicist and a charming, yet diffident and remarkably humble human being,” says Frank Wilczek, a CTP-LI colleague who is the Herman Feshbach Professor of Physics, Emeritus, and a 2004 Nobel laureate. “He made renowned, fundamental contributions to diverse areas of physics, ranging from condensed matter and quantum computing to string theory. Throughout, you can perceive a consistent style: enormous mathematical talent brought to bear on important, concrete, sharply posed physical problems.”

Goldstone is renowned for his remarkable range of groundbreaking discoveries. He began his career as a nuclear theorist. In his early work on nuclear matter with Hans Bethe, he developed the use of Feynman diagrams for non-relativistic many-fermion systems.  

He later turned to particle physics, investigating relativistic field theories with spontaneously broken symmetries. Building on ideas inspired by superconductivity, he demonstrated that spontaneously broken continuous symmetries must be accompanied by massless, spinless particles — now widely known as Goldstone bosons (or Nambu-Goldstone bosons, in recognition of Yoichiro Nambu’s earlier related insights). 

In subsequent work with Abdus Salam and Steven Weinberg, Goldstone formalized and generalized this result, which became known as the Goldstone theorem. With Claudio Rebbi, Charles Thorn, and Peter Goddard, he worked out the light-cone quantization of relativistic strings, a foundational result in string theory. Goldstone also worked with Roman Jackiw and Frank Wilczek on solitons in quantum field theory.  

Goldstone was born in 1933 in Manchester, England. He earned his PhD from Trinity College at the University of Cambridge in 1958 and completed postdoctoral work at Cambridge, Harvard University, CERN, and Copenhagen University. After shifting his focus to particle theory, he joined the faculty at Cambridge in 1962. He moved to MIT as a professor of physics in 1977 and as a researcher in the Laboratory for Nuclear Science, and was appointed the Cecil and Ida Green Professor of Physics in 1983. Goldstone served as director of the Center for Theoretical Physics (now known as the CTP-LI) from 1983 to 1989.  

“Goldstone’s name will always be associated with spontaneous symmetry breaking, where 'Goldstone’s theorem' and 'Goldstone bosons' feature prominently,” Wilczek notes. “His early, seminal papers relating the mathematical concept to physical phenomena gave firm foundations upon which people built fruitful theories of magnetism, meson physics, and eventually the Standard Model and early universe cosmology.

“Jeffrey Goldstone’s ideas are so deeply woven into physics that it takes an occasion like his passing to remind us how much we owe him. I had the pleasure to work with Jeffrey in the early days of fractional charge. The short paper we wrote together is one that I’m especially proud of. It contains the seeds of many later developments. Thank you, Jeffrey, for that fond memory.”

Alan Guth ’69, PhD ’72, MIT’s Victor F. Weisskopf Professor of Physics, Post-Tenure, says, “Jeffrey was a wonderful friend and colleague, and a towering figure in theoretical physics. When I was trying to arrange to come back to MIT after being a postdoc for nine years at other places, it was Jeffrey who I contacted to try to make this happen. When I first learned in 1981 that Andrei Linde had figured out how to make cosmic inflation work, in the form known as 'new inflation,’ I remember that I ran into Jeffrey's office to share my excitement. I have learned a great deal from Jeffrey over the years, and will forever have warm memories of our friendship.”

“Jeffrey Goldstone was a theoretical physicist of rare talent and impact,” says David Kaiser, MIT professor of physics and the Germeshausen Professor of the History of Science. “His landmark article, 'Field theories with superconductor solutions,' published in 1961, helped to launch the modern era of symmetry breaking in high-energy physics. Building on insights from neighboring fields such as condensed-matter physics, Goldstone demonstrated that for any model in which a continuous symmetry is spontaneously broken, there will emerge zero-mass, zero-spin particles now known as 'Goldstone bosons.' His watershed study, and several influential follow-up articles, have become central to our Standard Model of particle physics, with further application as far afield as early-universe cosmology to spin waves in magnetic materials and quantized vibrations in excited fluids. Later in his career, Goldstone turned his attention to quantum information science, helping to construct several novel algorithms for quantum computation. 

“Typically reserved, his close friends admired his droll humor. Writing to Harvard physicist Sidney Coleman in November 1976, for example, Goldstone declined Coleman’s invitation to attend an upcoming physics workshop. 'I expect to be in total confusion during January [1977] and wish to simplify my life,’ Goldstone replied. 'Forty-three years in the womb’ — including decades at his alma mater, the University of Cambridge — 'have unfitted me for coping with normal trivial details of survival. It’s probably a good thing I’m coming out, but the birth pangs are hell.’ 

“The birthing event to which Goldstone referred was his move to MIT to join the Physics Department faculty in 1977. Thank goodness he braved the move. Over nearly 50 years, Jeffrey Goldstone enriched our Department of Physics at MIT, and especially our Center for Theoretical Physics – A Leinweber Institute, with his deep insights and gentle manner.”

Even after retiring from MIT, he remained active in research for the next two decades, working in quantum computation. 

Edward Farhi, professor emeritus of physics and a frequent collaborator for over 20 years, describes Goldstone’s contribution to the field from its early stages as “very significant.”

“FFG [Farhi, Goldstone, and Samuel Gutmann] teamed up with Donner Professor of Mathematics Michael Sipser and showed that there was a problem which could not be sped up with a quantum computer, learning parity in the oracle setting,” Farhi says. “This was the first demonstration of this kind of limitation, although the same week another group proved the same thing by a different method. In 2000 FGG, along with Sipser, introduced the concept of Quantum Computation by Adiabatic Evolution. This is an all-purpose approach to optimization and was later shown to be universal, which means that any quantum computation can be encoded as quantum adiabatic. Furthermore, the company D-wave was founded to run this algorithm. D-wave is the progenitor of all of the superconducting hardware efforts such at the ones at Google and IBM. We later showed that using scattering theory a quantum computer could determine who wins a game provably faster than the best classical algorithm for this task.

“FGG introduced the Quantum Approximate Optimization Algorithm. This was the first time a general purpose quantum optimizer could provide worst-case performance guarantees for combinatorial optimization problems. These guarantees as of now (with one exception) do not beat the best classical. Still, this paper has over 6,000 citations, making QAOA one the most-studied quantum algorithms.

“In 2019, FGG and Leo Zhou applied the QAOA to the Sherrington-Kirkpatrick model. We showed how to analyze performance in the infinite size limit at any depth. Our work and others indicated that as depth increases with optimal parameters, the QAOA would approach the celebrated Parisi value. Recently the Open AI math dump, the conjecture that the QAOA would hit Parisi, was proven by the AI. I bet Jeffrey would have been surprised and impressed!”

Jeffrey Goldstone was a fellow of the American Physical Society Fellow, the Royal Society, and the American Academy of Arts and Sciences, and he was an honorary fellow of Trinity College, Cambridge. 

His honors included the Guthrie Medal of the Institute of Physics, the Dannie Heineman Prize for Mathematical Physics of the APS, and with Stanley Mandelstam the Dirac Medal (1991) of the Abdus Salam International Centre for Theoretical Physics, awarded for “his fundamental clarification of the phenomenon of spontaneous symmetry violation in relativistic field theory.”

He is survived by his son Andrew Goldstone, associate professor of English at Rutgers University’s New Brunswick campus; his daughter-in-law Anne Dewitt, a clinical associate professor at New York University’s Gallatin School of Individualized Study; and his granddaughter, Helen. His family is planning a memorial in 2027. 

MIT researchers will lead an up to $53 million effort, awarded by the U.S. Advanced Research Projects Agency for Health (ARPA-H) as part of its STREAM (Systems for Tracking and Resilience in Efficient Agricultural-input Management) program, to develop next-generation weed killers that help farmers and protect human health. 

More than 1 billion pounds of herbicide are used in the United States every year. Herbicides help farmers by preventing significant crop loss due to weeds, and they enable regenerative farming practices that protect the soil, but some weeds are becoming resistant to the most common herbicides. 

Exposure to herbicides may contribute to increased risk of cancer, metabolic and endocrine disorders, and other adverse health outcomes. Farm workers can be exposed from frequent herbicide use, but many U.S. residents are also exposed to these chemicals in small amounts through food and drinking water. 

The MIT-led research team will develop new generative artificial intelligence tools to help them discover herbicide alternatives that are more effective and developed with human health in mind. Such alternatives could also help to reduce U.S. dependency on imported phosphorus, which is a critical ingredient in a major herbicide, glyphosate.

“With herbicides like glyphosate and atrazine, there’s a real crisis because of resistance mutations that are coming,” says Christopher Voigt, head of MIT’s Department of Biological Engineering. “And if you combine the resistance mutations with issues around national security of the supply chain of the precursors to herbicides, we could find ourselves without these core agrochemicals that are absolutely essential for getting the high crop yields and economical prices that the United States now sees.”

Voigt is the principal investigator for the project, known as Precision Learning for On-target Weed control (PLOW). The team is also expected to include researchers from the Broad Institute of MIT and Harvard, the University of North Carolina at Chapel Hill, the University of Tennessee at Knoxville, the University of Illinois at Urbana-Champaign, and Robigo.

The researchers are grouped into three teams that will tackle different aspects of the project, aligned with the STREAM Technical Areas. The generative AI group will develop models that can predict molecules — either chemical compounds or small interfering RNA (siRNA) — that disrupt the same plant enzymes targeted by glyphosate and other herbicides.

Glyphosate kills weeds by interfering with an enzyme called EPSP synthase, which plants need to produce several critical amino acids. After many years of herbicide use, some weeds have begun to acquire genetic mutations that allow them to become resistant to those herbicides, which makes them less effective and poses a significant threat to crop yields.

“There’s a real need for new herbicides because of resistance mutations that have been arising,” Voigt says. “There are also concerns about herbicides’ possible impacts on the environment and on humans, so it’s important to identify alternative ways of making herbicides, which as a general class of molecule are really critical to agriculture.”

The MIT-led team plans to design generative AI models that will be able to predict molecules that can help prevent resistance from arising. The models will also optimize for other features such as low cost of synthesis, low risk of inducing resistance, and low risk of harming humans or causing ecological damage. 

The group developing these AI models is led by Connor Coley, the Warren K. Lewis Associate Professor of Chemical Engineering at MIT; Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT; and Alexander Tropsha, the K.H. Lee Distinguished Professor in the UNC Eshelman School of Pharmacy.

The models are expected to generate billions of possible candidates, which will then be tested by a second research team. This high-throughput screening group will be led by Angela Koehler, the Charles W. and Jennifer C. Johnson Professor of Biological Engineering at MIT, and Jaime Cheah, the associate director of scientific outreach and collaborative screening in the Center for the Development of Therapeutics at the Broad Institute.

“Those libraries will be screened both to identify those molecules that hit their target, but also to then provide all of that screening data back to the computational approach in order to improve it,” Voigt says.

Once the candidate pools are narrowed down, researchers on a third team will begin greenhouse studies to evaluate how each molecule performs under different agricultural conditions. That team will be led by Neal Stewart and Scott Lenaghan, co-directors of the Center for Agricultural Synthetic Biology at UTK; Patrick Tranel, the Tom and Peggy Ainsworth Professor of Crop Sciences at the University of Illinois; Tim Willson, the Harold Kohn Distinguished Professor at the UNC Eshelman School of Pharmacy; and Andee Wallace PhD ’20, CEO and founder of Robigo. 

The first greenhouse tests of initial RNAi candidates are expected to be complete by the end of the first year of the program.

As part of this project, the research team will also develop living microbial systems that degrade agrochemicals before they reach food, soil, or waterways. Engineered bacteria, fungi, and srRNA will convert chemicals and formulation ingredients into biomass, providing a scalable strategy to reduce environmental and human exposure.

This research is funded by the Advanced Research Projects Agency for Health (ARPA-H) under agreement number 140D042690030. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States government.

The United States used to be the world leader in metal casting, a process where liquid hot metal is poured into a mold to solidify into a desired shape. But more than 70 percent of American metal casting facilities have closed since the industry’s peak in the 1950s. Today, China and India dominate global metal casting production, which is critical for making the components of jet engines, nuclear and gas turbines, medical devices, and more.

Fabri is trying to revitalize American metal casting. The company, founded by Steven Davis ’24; Thomas Cole ’91, MBA ’13; and Pieter Coulier, is building a new kind of metal casting foundry that uses additive manufacturing, software, and vertical integration to eliminate many of the most labor-intensive steps associated with traditional metal casting. 

Fabri is already producing parts for a growing list of customers at its 16,000-square-foot plant in Billerica, Massachusetts, where it is applying its approach to one of the most multidisciplinary and challenging types of metal casting, known as investment casting. The company is working with Raytheon, Lockheed Martin, and Sikorsky Aircraft, among others, and is moving through qualification to make parts for the most critical aerospace and defense applications.

Fabri’s first foundry was designed to be quickly replicated. Even as it significantly expands capacity at its current plant, demand from customers is driving the company to build a far larger facility in 2027 that will produce bigger parts at higher volume as customer programs move into production.

“Metal casting is one of the oldest and most prevalent manufacturing methods in the world,” Davis says. “It’s a uniquely difficult process because there are so many different steps. There’s so much domain knowledge and experience you need to understand it. If we get cut off from the world’s casting capacity, that means no more pots and pans, no more storm drains and other basic things, but also no more aircraft. Lots of medical devices, like titanium hip implants, are cast. Power generation also depends on metal parts. These are very critical industries that we need to continue to produce.”

Faster metal production

Davis grew up competing in robotics competitions like BattleBots. The biggest challenge for his small, underfunded teams was getting the parts to make the robots.

Davis arrived at MIT in 2020, where he majored in mechanical engineering. As part of a summer internship after his sophomore year, he worked at Relativity Space, which builds reusable rockets. There he saw multi-million-dollar projects being delayed by the same hurdles he faced in robotics competitions. His team would wait weeks for expensive machines to make parts.

“I realized the problem was scale,” Davis says. “These production processes couldn’t even scale to aerospace, so they weren’t going to scale to far larger industries.”

When Davis returned to MIT that fall, he worked on adapting the precision and flexibility of 3D printing to producing metal parts. Davis also took several classes taught by John Hart, MIT’s Class of 1922 Professor and head of the Department of Mechanical Engineering. Hart co-founded several additive manufacturing companies including VulcanForms and Desktop Metal. He continues to advise Fabri.

“I was very interested in finding the best uses of additive manufacturing,” Davis recalls. “A lot of the most successful companies weren’t making end parts; they were making tools. My favorite example is Invisalign. They’re not printing the dental liners: They are printing the molds that make the aligners.”

Davis began thinking about starting a company roughly midway through his bachelor’s degree at MIT. He met Cole through his MIT network. Cole had founded a fully automated CNC machining company making dental implants that was acquired by AstraZeneca. Pieter Coulier, Fabri’s third co-founder, previously developed the simulation and metal 3D-printing algorithms at Velo3D that enabled SpaceX’s Raptor engines to be printable.

Davis also began visiting foundries, where he repeatedly heard their biggest bottleneck was labor.

“They all have huge ‘Help Wanted’ signs out front,” Davis says. “Capacity is going down. That’s why we realized automation had to be a core part of our thesis. According to the American Foundry Society, these workers are retiring at a rate of 3-to-1. It’s really hard to find these foundry engineers who understand what goes into the process and can design these molds. We have customers tell us they had an engineer retire and now they can’t make certain parts anymore. We want everything we learn to be encoded into our software forever.”

Investment casting is a complex, multistep process.

“Traditional investment casting starts by cutting a hard tool out of steel,” Davis explains. “Then you injection mold wax patterns into it. Those wax parts get glued together into what’s known as a tree. Then you dip the tree into liquid loaded with ceramic powder, you stucco it with sand, dry it, and repeat the process seven or eight times. That’s how you get the ceramic mold to pour your steel into to make the part.”

Fabri’s process starts with the part’s computer-aided design (CAD) file. The company’s proprietary software, FoundryOS, designs a pattern for each part, which Fabri prints in resin using established techniques including digital light processing (DLP) and stereolithography. The printed pattern is dipped in ceramic slurry to build the shell that molten metal is poured into. FoundryOS incorporates data from every step and uses machine learning models to improve the process and the parts with each casting.

“By printing that mold right off the bat, we remove the need for all those early steps,” Davis explains. “We fully automate the creation of the [complex patterns and channels] needed for making that tree. Instead, we print it all in one piece and consolidate a lot of that work.”

Davis says traditional tooling can cost upward of $1 million for complex enough designs and take many months to make — a big reason companies have to wait so long for parts.

Davis built the first prototype of Fabri’s printer in the basement of his MIT fraternity using funds from the MIT Sandbox Innovation Fund. After subleasing a space in Ashland, Massachusetts, Fabri was pouring metal within six months. Today, he says Fabri’s approach also cuts post processing and benefits from the increased flexibility of 3D printing, which allows Fabri to iterate on parts in days.

“What really hurts traditional foundries is they put a design into production, they tool up, they cut a tool, and when they get their first part, they realize something’s wrong,” Davis says. “Then they have to rework it or fix it. They’d like to fix the tooling in the process, but they can’t because they spent so much on the tool and waited six to nine months for it.”

Reinvigorating an industry

Fabri opened its first foundry in Ashland in 2025 and moved production to Billerica in spring of 2026. Davis says the company can go from receiving a design to producing a finished part in as little as a week. Fabri is making parts out of aluminum, copper alloys, and stainless steel, primarily for munitions, aviation, and naval applications. In early 2027, the company plans to begin casting nickel superalloys, the metals used in the turbine blades that can currently have four-year wait times.

The company’s process also enables complex geometries and organic topologies that hard tooling can’t produce.

“We’re building the software that can make these parts more intelligently and faster,” Davis says. “Qualifying for aerospace production with a company like Raytheon is a really big deal. Most startups and 3D-printing companies we know haven’t done it. The ability for us to go from opening our first facility in 2025 to now going into production with some of the nation’s most critical platforms is a huge milestone. We are going to be making parts that are going to fly in the systems and go into the munitions platforms that the country needs.”

When Andrew Voelsing was 8 years old, he decided he was going to MIT.

He was fascinated by space and dreamed of building spaceships. The Institute, and its well-known aerospace engineering program, seemed like the obvious destination. 

Years later, as a senior studying climate system science and engineering, Voelsing is preparing for a different kind of career: serving as a Navy officer, using his technical training to help the armed forces understand the ocean and atmosphere. 

Even during childhood, military service was never far from his mind. His mother attended the U.S. Naval Academy, and his father participated in the Reserve Officers’ Training Corps (ROTC) at the University of Notre Dame. The two met while serving in the Navy in Japan. Growing up, Voelsing heard their stories about military life and saw how their experiences as naval officers shaped their lives.

Voelsing was eventually accepted to the Naval Academy himself. But he chose MIT for the variety of faculty and activities he could pursue while also earning his commission as a Navy officer through the NROTC program. He will soon assume the highest-ranking student role of Battalion Commander, responsible for NROTC across Greater Boston.

But the path from an 8-year-old’s dream to his senior year in Cambridge, Massachusetts, was not quite as straightforward as he had imagined. After arriving at MIT, Voelsing struggled to find his footing academically.

“I thought I was smart in high school. I thought I was at the top of my class, and then I got here and I was failing midterms,” he says. “All my friends seemed to be getting things faster than I was, and I just felt really out of place.”

During the winter break of his first year, he talked with his father about how he was feeling. His father encouraged him to find something that combined what he was good at, what fulfilled him, and what the world needed.

The conversation helped Voelsing reconsider his academic path. He switched from aerospace engineering to climate system science and engineering (Course 1-12), a then-new major offered jointly by the departments of Civil and Environmental Engineering and of Earth, Atmospheric and Planetary Sciences, and returned to campus feeling energized by his studies. 

The change also helped him see how his education could serve a purpose beyond earning a degree. As he continued his ROTC training, he began to consider how technical knowledge about the environment could inform decisions with real-world significance.

The change, he says, was like night and day.

“I really figured out how to study,” he says. “Finding out my own vision of self and academic fulfillment helped me propel myself forward.”

A course in environmental history with Professor Susan Solomon, an atmospheric chemist who helped discover the cause of the Antarctic ozone hole, further shaped his interests. Each week, the class examined a different environmental issue, encouraging students to investigate primary sources and identify questions that had received less attention.

For his final paper, Voelsing researched an agroforestry project in Uganda. The project sparked a deeper interest in global agricultural and food systems, an area he hopes to explore in graduate school.

“There’s not going to be this one technology that comes along, and suddenly climate change isn’t an issue,” he says. “It will be a combination of 100 things making it 1 percent better for us to get across that finish line.”

Outside the classroom, one of Voelsing’s most formative experiences has come through Camp Kesem, a student-run nonprofit that provides free summer camps for children whose parents have been affected by cancer.

He joined MIT’s chapter as a first-year student. The chapter serves more than 200 campers, ages 5 to 18, giving them a week of sleepaway camp filled with activities such as ziplining, swimming, and games.

Over the years, he has taken on several leadership roles. He started as a counselor, then became operations coordinator, helping plan and run the camp. Later, he led alumni relations and organized a fundraising gala. As a senior, he returned to counseling, realizing that the part of the experience he valued most was spending time with the children.

“The thing I enjoy most isn’t all the planning; it’s not all the coordinating,” he says. “The thing I enjoy most is just spending time with kids.”

Camp Kesem also changed how he understands empathy. Before joining, he says, he was uncomfortable when other people cried. Through conversations with campers and fellow counselors, he learned the importance of sitting with someone through difficult emotions instead of trying to make those feelings go away.

“Being able to sit with a person as they grieve and experience that grief with them really changes your views on empathy and changes your views on what is actually important,” he says.

Voelsing has tried to bring that openness to his friendships on campus, too. He values conversations that allow people to understand how different experiences have shaped their perspectives, and he makes an effort to connect people who might not otherwise meet.

“I would give up a good night’s sleep seven nights a week for a good conversation or staying up talking to a friend,” he says. “And I often do.”

That same willingness to learn from others has informed his experience in ROTC. Through the program’s training and relationships with fellow midshipmen, Voelsing has gained a perspective on military service that extends beyond the family stories he grew up hearing.

He recently received his service assignment as a meteorology and oceanography officer, a specialty that will allow him to draw on his scientific background. 

Before beginning his service, however, Voelsing hopes to attend graduate school. He wants to deepen his expertise before joining the Navy full time, believing that further study will allow him to contribute more once he begins his career.

Balancing his academic work, ROTC commitments, and extracurricular activities requires him to be deliberate about how he spends his time. Rather than getting overwhelmed by everything on his plate, Voelsing tries to focus on the responsibility in front of him.   

“I’m trying to tackle it with enthusiasm, and trying to be where my feet are and do the thing well,” he says.

Looking back on his four years at MIT, Voelsing says the most important influence on him has not been a particular class or professor, but the community he has found among his friends. He values the way MIT brings together people with different interests, backgrounds, and ways of thinking, and he hopes to be someone who helps those people connect.

That sense of responsibility also informs how he thinks about the future. Whether in climate science or the military, he wants to approach difficult decisions with an awareness of their consequences and a willingness to take on challenges others might find daunting.

“Somebody’s got to do it. Why not me?” he says. “I think that is really the through line of what I want to do academically and the kind of life I want to lead.”

It was a landmark in history: In 1957, Ghana became the first sub-Saharan African state to achieve independence from colonial rule, having previously been under the control of Great Britain. 

But gaining political independence does not mean the past is left behind, and there has long been public debate about the many long-term effects of the colonial period on state and society in subsequent decades.  

Now, an empirically rich study explores one aspect of the Ghanaian state, the composition of its bureaucracy, during and after British rule. The study finds that two ethnic groups, the Ga and the Fante, which made up 14 percent of the population combined, held over 70 percent of public-sector positions in the 1950s, suggesting goods such as desirable jobs had been heavily distributed along group lines. 

“At the moment of independence, you have a state that is not meritocratic, and is highly captured by certain ethnic groups,” says MIT scholar Noah Nathan, co-author of a newly published paper detailing the study’s findings. As a result, he adds, “You can imagine the politics that creates, which are demands to equalize access to state power and state authority. And things start off on this foot of ethnic political competition over the state. That is in part a legacy of colonial state-building.”

At least in this case, “Independence in Africa wasn’t this hard break from the past,” suggests Nathan, a Ford International Professor of Political Science in MIT’s Department of Political Science. “Africans became the leaders of their countries, but the state institutions that they inherited weren’t born fresh at independence. They were carried over from the colonial period.”

The paper, “The Seeds of State Capture: Merit and Patrimonialism in the Colonial Bureaucracy,” is published in advance online form in the American Political Science Review. Nathan’s co-authors are Sarah Brierley, an associate professor at the London School of Economics and Political Science; George Kwaku Ofoso, an associate professor at the London School of Economics and Political Science; and Tingxuan Zhu PhD ’26, a recent graduate of MIT’s doctoral program in political science at MIT. 

Analyzing names

To conduct the study, the scholars dug into records from the period of British rule in Ghana, which extended from the 19th century until 1957. Using documents from the British National Archives, they examined individual-level employment records for 11,702 bureaucrats from the colonial-era civil service, building a dataset spanning from 1897 to 1955. The records included 6,879 Ghanaian civil servants and 4,805 European civil servants. The scholars’ aim was to shed light on the dynamics of civil service hiring and politics. 

British leaders at the time, and some commentators subsequently, have cast the colonial state as one built on rational, merit-based principles — what scholars call a “Weberian” bureaucracy, named after the famous German sociologist Max Weber. Scholars say that other types of bureaucracies, in contrast, display “patrimonialism,” with jobs being assigned not on merit but through patronage.

To analyze this question further, the scholars looked at 13.8 million names of registered voters from contemporary Ghana, along with localized ethnicity data from the 2010 national census. They used this to establish a baseline of the relationship between names and ethnicity in the country. Then, the scholars went back to the historical archives, and, using the names there, determined the ethnicities of past Ghanaian civil servants, from 1897 to 1955. 

Ultimately this approach showed that on the eve of independence, the Ga and Fante had managed to gain a percentage of civil service jobs that was five times greater than the percentage of the population they represented. That strongly suggests that the colonial-era bureaucracy built under British rule was not merit-based, at least in terms of overall composition. 

“Scholars have said this post-1957 independent Ghana brings with it the introduction of patrimonialism into the bureaucracy, and we’re saying this was going on long before,” Nathan explains. “It actually wasn’t meritocratic at all, and Kwame Nkrumah, the first president of Ghana, didn’t inherit some Weberian rational bureaucracy that he then destroyed. He inherited this thing rife with patrimonialism and patronage, that he then adapted. And that’s a very different interpretation of the post-independence period.”

Exams and education

To further examine the question of merit, the scholars even obtained individual-level results from Ghana’s civil service exams, then analyzed those in relation to which applicants got hired — or passed over for jobs. The results indicate little correspondence between exam results and job offers, again suggesting that ethnicity and social ties mattered more in hiring, on aggregate. 

“If you were from the right ethnic group, you were going to get a job, even if you did pretty poorly on the civil service exam,” Nathan says. “And people from disfavored ethnic groups who did much better on the exam were not getting the same positions.”

The study also rules out a seemingly logical explanation for at least part of the ethnic skew in the composition of the Ghanaian civil service — that hiring reflected levels of educational attainment, even if access to schooling itself might have reflected ethnic iniquities. But the data indicate that was not a factor. 

The scholars located every school in the country geographically, dating to the 1890s, and closely measured access to education for all ethnic groups over time. The Ga and Fante groups may have had some initial advantages in this regard, but by the 1930s, many other groups had caught up in educational attainment and still were not procuring the same relative numbers of civil services jobs through the 1950s. 

“At least in this case, that can’t explain why these two groups got favored in the hiring process,” Nathan says. “It wasn’t just a direct outcome of the fact that some parts of society had more education than others.”

As it happens, geographical proximity to civil service jobs is also not an explanatory factor; the scholars also examined the locations of government offices and found they cannot account for the quantity of jobs going to the Ga and Fante. 

Out of sight

Instead, the researchers believe the likeliest scenario is that, once established in the civil service, people from the Ga and Fante managed to use their social connections to get more and more jobs offered to others from their own groups. Moreover, they may have done so not necessarily with explicit British approval, but with something more resembling indifference on the part of the colonial rulers. 

“The mechanics of the hiring process were very decentralized,” Nathan says. “And the British didn’t know very much about the society they were governing. That created an information asymmetry where local bureaucrats could get friends into jobs in a way their superiors were either not aware of or didn’t have very much incentive to stop.”

All told, Nathan observes, “The ministry of finance in Ghana in 1957 isn’t some new thing that’s created from scratch. It’s the colonial ministry of finance that’s been in place for 60 years, and the bureaucrats in it showed up to work the next day in the same jobs. [The new leaders] inherited thousands of employees who had gotten there through this process.”

The study is one of several that Nathan has published using data to answer questions that span both political science and history. Quantifying the issue in a granular way helps illuminate the large-scale political dynamics.

“People have written about this in broad strokes, even the fact that certain ethnic groups in some countries got more access to positions than others, but they haven’t had detailed microdata on exactly who got which job and when,” Nathan says. “That’s the innovation of our paper … we could unpack the nuts and bolts of how this hiring process happened, and in doing so dispel some conventional wisdom about how African states were built in the colonial period.”

The new paper is not the only piece of scholarship taking this kind of data-intensive approach to studying colonial state-building and its legacy. Ada Johnson-Kanu, an assistant professor of political science at the University of Kentucky, has conducted this sort of study of Nigeria’s bureaucracy over time, in work she completed in 2021. 

Nathan, for his part, says he hopes that other scholars can expand upon this kind of data-driven approach to digging into certain historical questions. 

“There is just incredibly rich archival evidence out there in the world for a lot of countries,” Nathan observes. “I think this is a really fun way to do research.”

Today, a scientist who wants to simulate a physical process must pick the right tool for that one job, such as a solver built for flowing water or another designed to model the way a protein folds. Each one is hand-coded around the specific equations that dictate how that process behaves.

Researchers at MIT, the University of British Columbia (UBC), and elsewhere have now replaced that patchwork with a single deep-learning system, called WorldParticle, that could help engineers model different physical phenomena, from sliding sand to folding proteins, with one unified artificial intelligence architecture.

Scientists and engineers could use this system to model processes that typically require separate simulators, such as a fluid sloshing against a deformable solid. This could offer a faster, more accurate way to simulate how sloshing oil might impact an elastic valve in a storage tank, for instance. It could also help engineers study where and how granular materials, like sand or soil, could move when a soft robot touches them.

The system represents every material it simulates, from a dune of sand to a protein molecule, as tiny particles and predicts where those particles will move one step at a time. Because it learns the underlying dynamics rather than being programmed with an equation, the model can adapt to materials, forces, and environments it never encountered during training.  

In the long term, the researchers hope this work helps scientists discover new materials and explore physical processes they would be unable to effectively study with existing simulation methods.

“We want to have a GPT moment for simulation,” says Peter Yichen Chen, a former MIT postdoc who is now an assistant professor at UBC, and is a co-corresponding author of an open-access paper on this technique. “Every time people want to build a simulator for a particular physical phenomenon, they need to code it up from scratch. Our vision is to have a foundation model that works out of the box for any scenario you want to simulate. This is a first step in that direction.”

Chen is joined by co-corresponding and co-lead author Minghao Guo, an MIT postdoc; co-lead authors Caoliwen Wang and Siyuan Chen, UBC graduate students; Wojciech Matusik, an MIT professor of electrical engineering and computer science who leads the Computational Design and Fabrication Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL); and others at UBC, Georgia Tech, Inria, Meta, the University of Pennsylvania, the University of Utah, the University of California at Los Angeles, and the University of Hong Kong. The research will be presented at the ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia.

Unified simulation

For this work, the researchers drew inspiration from world models. These powerful AI systems learn the rules that govern the physical world so they can predict how objects or environments will change over time in response to certain actions or conditions.

The researchers applied these concepts to develop a unified AI architecture that can simulate multiple types of physical phenomena. 

“If you study deformable objects and fluids, you would need two simulators — one for each physical regime. But if you unify them into one model, maybe you could couple the physics of these two processes and gain insights into new problems?” Guo says.

WorldParticle breaks an object into thousands or millions of tiny, three-dimensional dots, or particles, using what is known as a Lagrangian representation. It tracks the movement and velocity of the particles over space and time to simulate the material’s dynamics.

Typically, when scientists model materials and physical processes using Lagrangian particles, they use a governing equation that defines the fundamental laws which control how that material behaves over time and space. They must convert that complex equation into a formula a numerical solver can handle, repeating these steps for each material and process they want to model.

Instead, the researchers designed a neural network that learns to solve governing equations in general, eliminating the need to design a new model and solver to handle each one. 

Once WorldParticle converts an object into particles, a neural network takes the 3D positions and physical properties of the particles as inputs. It outputs their positions and properties for the next frame of the simulation, predicting the material’s motion over time and space in one-second increments. 

“It is a similar architecture to ChatGPT. But instead of predicting one word at a time to write a piece of text, we are predicting how these particles will move, one step at a time,” Chen says.

Before each simulation step, WorldParticle separates the external forces that affect the physical system, like impulse forces that affect an ocean wave when it slams into a seawall. 

After predicting the initial states of the particles, WorldParticle integrates the external forces back into the simulation, correcting its particle predictions based on how those forces affect the object’s behavior. In this way, the system can generalize to unseen materials, changing environmental conditions, and varied forces.

A general, scalable framework

But because a complex object could be broken into millions of particles, the problem quickly becomes too computationally intensive for the model to handle. 

To streamline processing, the researchers employed a merging technique to combine and compress particles while still allowing WorldParticle to capture fine-grained details of their behavior.

“We needed to do a lot of detailed engineering and employ many tweaks to the architecture for it to be truly generalizable and scalable,” Guo says.

They demonstrated WorldParticle by modeling six different physical phenomena, including deformable objects, granular materials, and proteins, with higher accuracy than numerical simulation methods. 

For instance, WorldParticle effectively captured how a granular object would collapse into a pile while some baseline methods left sand particles floating in the air. Their method also modeled how protein structures change over time about 100 times faster than numerical simulators, Guo adds. 

The general-purpose framework could be applied to many materials and processes, including soft robot manipulation tasks, RNA evolution, and astrophysical phenomena like the merging of galaxies, the researchers say.

Moving forward, they want to adapt WorldParticle to simulate these and other physical systems. They also want to build on this work toward the development of a full, general-purpose simulation pipeline for scientific discovery. 

 “We see the unification of computation and science giving us new insights and tools to discover physical phenomena or explain some unexplainable physical phenomena,” Guo says. “With a unified model, we could unlock many discoveries that we can’t even imagine.”

Humans are intuitive by nature. We can sense meaning beyond the literal words that we encounter. Now, a new artificial intelligence-assisted learning platform developed by MIT engineers is enabling robots to read larger meaning into a human’s natural language commands. 

In a study to be presented at the annual Conference on Robot Learning this month, researchers from MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion-planning. 

With FLEX, humans can interact with a robot by giving it instructions in natural, conversational language. The robot can not only follow the instructions literally but also read into the words to draw larger generalizations that help to improve its motion-planning task. 

The team demonstrated FLEX in several simulated scenarios, including one in which a robot serves a cup of coffee. In these virtual scenarios, a person interacts with a robotic arm by typing commands in natural language. As the robot moves the coffee cup over a cluttered table, the person might type “Don’t spill on the laptop!” In response, the robot will reason about why it should avoid the laptop. For this, FLEX incorporates a large language model (LLM) — a type of AI system that is trained on enormous libraries of text, including social media posts, video transcriptions, and web content, which the LLM uses to identify larger trends, patterns, and generalizations. 

“LLMs have seen so many texts from the internet,” says study author Nathan Dennler, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “People might have written about, ‘Oh, my phone dropped in the water and now it doesn’t work,’ or ‘I spilled coffee on my computer and now it doesn’t work.’ So LLMs pick up on the idea that liquids plus devices leads to things not working.”

Through the LLM, the robot reasons that it should also avoid bumping into all electronics. In its updated path, the robot adjusts to avoid both the laptop and a nearby phone, without the person having to explicitly spell this out.

“The idea is that there is some higher-level intention behind the words that you say,” Dennler says. “People naturally get this. But you have to carefully explain that to a robot, which takes time and effort. FLEX gives you an interface of communication with a robot to say, ‘When people say this, they mean that.’”

The study was led by Andreea Bobu, assistant professor of aeronautics and astronautics at MIT. Co-authors are Audrey Lee SM ’26 and Ilia Sucholutsky of New York University.

Beyond what we say

At MIT, Bobu heads up the Collaborative Learning and Autonomy Research (CLEAR) Lab, where her group develops autonomous agents that learn to work for, with, and around people. She is focused on developing assistive and household robots that can carry out physical tasks, and reason and adapt in real-time, in response to a person’s natural, conversational commands. 

“Robots use math to do their trajectory planning. But we can’t expect a consumer to learn to do this math in order to tell a robot what to do,” says Dennler, who is a member of the CLEAR Lab. “Instead of having to get people to learn how to use a robot in a specific way, the goal is to interact with the robot through natural language.”

There is a large effort in the robotics community to incorporate natural language into robotic learning pipelines. Various strategies include using language to clarify a task’s goal, redirect a robot’s trajectory, and update its understanding of what is a safe path to take. These existing methods use natural language to tell the robot how to physically perform a task. But a robot could potentially learn much more from what we tell it. 

Bobu and her colleagues are expanding what robots can learn from natural language commands. In their new study, they focused on enabling a robot to interpret a person’s underlying, unspecified preference from limited natural language commands.  

Absorbing common sense

The team’s new robot learning platform, FLEX, is a three-step process that helps a robot read more into a natural language command, beyond what it is explicitly told to do. 

The first step, “Add,” instructs a robot to add a feature to its list of things in its environment that matter for the task at hand, based on a user’s natural language command. For instance, as a robot is moving a bowling ball from one location to another, a person might interject with “Don’t drop it on my mug!” The robot would then add the mug to a list of things to avoid in its path. This first step is how previous methods have worked. Every time a person makes a correction, the robot adds to its features list, and updates its trajectory to avoid those features. 

“The problem happens when your feature list becomes super long, and some features are no longer relevant because your environment may have changed,” says Dennler, who notes that this approach can grow tiresome if a person has to specify every specific feature to avoid. 

The second step, “Generalize,” is the new solution that FLEX provides. This step incorporates an LLM — in this case, GPT-4o — that considers the mug, along with any other features that the person has so far specified to avoid. The LLM automatically searches through its vast cache of data for patterns and connections among these features, to ultimately make a generalization that it should avoid objects that are fragile, even when the person has not explicitly said so. 

“LLMs have seen so many texts from the internet, from all sorts of people and places,” Dennler says. “Because they’ve trained so much on those things, some of that common sense gets absorbed into the LLM.”

In the third step, “Mask,” the robot’s standard computer vision system assesses its environment to determine what objects are no longer present, and then deletes those objects from its features list. This entire three-step process takes about five seconds. 

The team tested FLEX in simulations of a robot arm carrying out three different tasks: serving a cup of coffee, moving a candle, and carrying a bowling ball. In all three scenarios, volunteers interacted with the virtual robot by typing natural language commands, such as “Don’t spill on my laptop,” “Stay away from the stack of paper,” and “Don’t drop on the mug.” With FLEX, the robot followed all three commands literally and also reasoned at a higher level to not spill on electronics, stay away from flammable objects, and not drop its cargo on fragile things. 

“Everyone’s homes are different, and everyone cares about different things, so no robot comes out of the box knowing what matters to you,” Bobu says. “What excites me about FLEX is that it can pick that up from a sentence or two instead of asking you to spell out every rule — and that’s the difference between a robot people put up with and one they actually want in their home.” 

This work was partly supported by the MIT Generative AI Impact Consortium and the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing program.

Topology optimization is a computer design method that can help engineers design beams using reduced amounts of concrete and steel. But those intricate shapes produced from the tool are often too complex or expensive to pour into conventional rigid molds. The few that have been built have mostly relied on specialized equipment, such as concrete 3D printers or machine-carved molds, equipment that is out of reach in regions where new construction is expected to grow.  

Now, MIT engineers have shown that highly efficient beams can be cast in something more accessible: sewn fabric. In a paper published in the Journal of Building Engineering, the team designed and built a 3-meter (about 10-foot) test beam using fabric molds created through an automated pattern-making process. Compared with a conventional beam designed to carry the same weight, the optimized beam used about one-third as much concrete and half as much steel reinforcement. When tested, its measured response was within about 2 percent of what the team's computer model predicted.

Fabric formwork, in which concrete is poured into a mold made of textile, has been explored for years to create beams that vary in depth, rather than in rectangular shapes common in construction. But because fabric deforms under the weight of wet concrete, that flexibility has made the final shape hard to predict.

“The key realization was that this flexibility is not just a limitation to manage; with the right computational pattern design, it can become a way to produce optimized structural forms,” says Josephine Carstensen, the Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering and senior author of the study.

Fabric molds can also be inexpensive, lightweight, and fast compared to wood or steel molds. “You could sew a formwork together off site, pack it into a box, and bring it to a job site very easily,” says Jackson Jewett PhD ’25, co-author of the study. “We joke that we are developing the H&M of the construction industry.” 

Less material, lower carbon

Cement production accounts for roughly 8 percent of human-caused carbon emissions. 

“This work addresses concrete’s climate footprint primarily by reducing material demand,” Carstensen says. “Cement production is highly carbon-intensive, so using less concrete is an important part of decarbonizing construction.”

Where new construction happens matters, too. The world’s number of buildings is expected to nearly double by 2050, with about 85 percent of that growth in developing countries, where access to advanced construction equipment is often limited. That is why, Carstensen says, the team focused on an accessible, cost-conscious fabrication approach.

Designing the beam

Concrete is strong when compressed, but weak when pulled, so it’s reinforced with steel that can handle the pulling forces. The researchers first mapped a path for the steel reinforcement using a method called graphic statics, which can generate structural designs almost instantly. They used it to shape the steel bar so that it carries an equal share of the weight along its entire length to keep the reinforcement simple enough for builders to make. Topology optimization, which works more slowly but can place material freely, was used to determine where concrete was needed and where material could be removed from areas where it contributes little to the structural performance.

“Both methods are good at different things, so we tried to combine the strengths of both into a single methodology,” says Jewett.

The resulting beam has a top section, narrow angled legs, and a thin bottom section for the steel reinforcement. The team’s automated process translated the 3D beam design into flat fabric pieces that can be cut and sewn into a mold, accounting for how the fabric will deform once concrete is poured in. A simple timber frame supported the finished fabric mold.

Watching the beam take shape

For Mark Hellrich SM ’26, the study’s lead author, the main hands-on challenge was getting the concrete mix right. Woven cotton fabric lets water seep through, so a mix that is too runny loses water through the mold. It also presses harder against the fabric, pushing it away from the intended shape.

The team used a moderately wet, sand-rich mix with only small stones, so the concrete could flow through the beam’s narrow, angled legs and thin bottom section without getting stuck. They then packed it in by hand, prodding each layer and tapping and kneading the fabric from the outside.

“This is where the flexibility of the mold becomes an advantage: you can work the concrete through the interconnected passages without a vibrator, which a rigid mold of this shape would need,” says Hellrich. A concrete vibrator is a tool typically used to shake air pockets out of freshly poured concrete in rigid molds. 

Once the concrete was in place, the wet fabric revealed the beam’s final shape. “All three beams were free of wrinkles and creases,” says Hellrich. The top surface stayed close to the design, while the main difference was a downward sag along the bottom, with the angled legs folding slightly inward.

Finding the right fabric mold

To find out how much sewing precision the beam needs, the team built three molds for the same design, made from 36, 56, and 76 fabric pieces. Comparing the 3D scans of the cast beams with the target design, the researchers found that more seams gave finer control over the details, but did not necessarily make the beam’s overall shape more accurate.

“More seams improve local control because they divide the fabric into smaller, more constrained panels. But a more complex pattern also has more pieces to align and more seam length, so small cutting, sewing, and assembly inaccuracies can accumulate across the mold, explains Carstensen.

The 56-piece mold demonstrated the best balance, capturing the important design geometry without the added time, effort, and potential errors associated with the most complex pattern.

The researchers then load-tested one of the beams, gradually stacking 36 water-filled buckets on top of it, up to about 30 percent of its design load to reflect everyday service conditions. Weighted lines hung from the beam’s underside to precise measuring gauges below, tracking how far the beam bent as the load was added and removed. At the beam’s center, the measured deflection — its downward movement under the load — was within about 2 percent of the computer model’s prediction.

Where it fits in the real world

The team also compared the cost of a single fabric-formed beam with a conventional one and found that location makes all the difference. In the Global South, where materials tend to cost more relative to labor, all three fabric versions were cheaper, with savings of about 10 to 20 percent. In the Global North, higher labor costs for sewing reversed that advantage, making the fabric versions about 16 to 55 percent more expensive.

The findings suggest that fabric formwork is most promising for custom-shaped concrete components produced as a one-off or in small batches, such as exposed beams, roof or long-span framing, pedestrian structures, and landscape elements. For the repetitive, rectangular beams found in most buildings, conventional reusable molds remain the more economical choice where labor is expensive.

“To get started, a builder would need the digital pattern for the target component, a sewing machine and suitable fabric, and a simple timber support frame,” Carstensen says. “That is a much lower equipment threshold than robotic fabrication, concrete 3D printing, or CNC-milled molds.”

The researchers describe the 3-meter beam as a proof of concept comparable to a short building span. It is also a big step up from the 1.2-meter beam the team built in a previous project, Jewett notes.  

Building bigger isn’t just a matter of enlarging the design, Carstensen notes. As a beam gets longer, its own weight and the pressure on the fabric mold change, so a larger beam would need a new design. The next step, Carstensen says, is “to test multiple beams to failure.”  

“The nice thing about these optimization methods is they generate structures that look organic,” Jewett says. “Their geometries are clear expressions of how the forces move through the structural system. There is elegance to them. It makes you ask why we don’t design all our structures in this way.”

Joining Carstensen, Jewett, and Hellrich on the paper are co-authors Abhinit Sati, Edward Chien, and Emily Whiting, of Boston University’s Department of Computer Science.

The work was partially supported by the U.S. National Science Foundation.

Discovering new molecules for specific uses is a major driver of progress in a wide variety of industries, whether it’s pharmaceutical companies looking for new drugs, or electronics or energy companies seeking new materials with particular properties. But it’s also largely a trial-and-error process, because there is not yet any way to perfectly predict how a given chemical compound will behave in terms of the myriad different properties and responses materials can exhibit.

Recent progress has been made on using artificial intelligence and machine learning to supplement or speed up laborious calculations based on physical principles that can at least provide some initial guidance on which compounds might be worth synthesizing and testing. Now, a new method developed at MIT shows that when there are few data on some technologically relevant property, such as ligand affinities for a recently discovered cellular target for a potential drug, this approach can outperform other existing methods for winnowing down the field of possible molecules that might be worth looking at.

The findings were reported in the Sept. 14 issue of the Journal of Chemical Information and Modeling, in a paper by MIT graduate student Akshat Shirish Zalte and recent graduate Jackson Burns PhD ’26, Research Scientist Charlles Abreu, Professor William Green, and three researchers from the chemical company BASF. 

An earlier version of the paper has been online since last year, and the open-source model the team developed, called CheMeleon, has already found regular use by several pharmaceutical companies. CheMeleon was built using ChemProp, an open-source machine-learning package also developed and maintained by the Green group at MIT, and anyone can download CheMeleon through ChemProp and fine-tune it for their own purposes.

Earlier this year, there was a contest organized by OpenADMET for trying to predict the properties of a variety of new molecules that had not previously been synthesized, says Green, who is the Hoyt Hottel Professor of Chemical Engineering. “It was a big contest, a lot of people participated,” he says. When the winners were announced, it turned out that nine of the top 20 entries had used CheMeleon in their process. “So then we started getting all kinds of inquiries from the pharma companies about this, but our paper had just been submitted; it wasn’t even peer-reviewed or anything yet.”

While the paper went through that review process, its influence was already spreading. At least one pharma company, Green says, “sent a message to all their people, and said ‘Anytime you’re modeling a property, start with CheMeleon, that’s our new company policy’” when making initial decisions about which molecules to pursue.

The key to CheMeleon was pre-training a machine-learning model with detailed data about a million different molecules, so that it gets to learn correlations between the structure of a given molecule and how it behaves with respect to a catalog of about 1,600 descriptors that have been developed by chemists over decades. CheMeleon can then be fine-tuned to predict well-known properties like solubility and viscosity, to more esoteric properties such as bioactivity or reactivity, with a certain protein or enzyme.

Those million molecules are a minuscule fraction of the innumerable ways that atoms can bond together, but they provide enough examples so that once the AI system has been trained on them, it can work out on its own many underlying patterns and relationships, and then apply those to a new, unknown molecule that’s presented to it.

“Most of today’s deep-learning models require more data than traditional methods,” says Zalte. Some successful models use extensive data from the published literature in chemical journals, he says. “We don’t look at the literature at all. We just calculate a bunch of molecular descriptors like molecular weight or number of oxygens, among others.” Then predicting those descriptors forms the basis for their pre-training task.

CheMeleon is a pre-trained foundation model. Inspired by the well-known foundation models in language such as ChatGPT and Claide, which essentially are developed by feeding them massive amounts of information from the internet, CheMeleon uses molecular descriptors of a million molecules from PubChem, a large, freely available database of chemical information maintained by the U.S. National Institutes of Health. 

With classical expert-driven models, on the other hand, specialists in a field manually input detailed knowledge about their subject. In developing these classical models, he says, for example, people know from experiments that a certain property of interest, say solubility, may relate to particular descriptors of the same molecules, “like molecular weight, or the number of oxygens that are present, or how branchy or bulky the molecule is.” 

With this team’s approach, they can start with an existing neural network architecture and train it to learn underlying principles. “We’re not training it for a specific property, but as a general model that understands some level of chemistry, so that even if you fine-tune it with just a few data points for the task you care about, you can still get some good performance,” he says. That’s thanks to the fact that the model is “not having to learn everything from scratch.”

To verify its usefulness, “We compared our model not only against other foundation models that are out there, but also with classical models leveraging expert-designed principles,” Zalte says. Comparing its performance across 58 test cases, “we see that our model comes out on top.”

Green adds, “The CheMeleon foundation model drastically reduces the number of molecules that need to be tested experimentally to accurately predict the properties of new molecules. This is a huge advantage, since synthesizing and testing a large number of molecules is slow and expensive."

Besides the MIT members, the team also included Jochen Sieg, Christian Feldmann, and Miriam Mathea, researchers at BASF SE in Germany. The work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, along with fellowship support from U.S. Department of Energy and MathWorks.

Three MIT affiliates — Anthea Coster of MIT Haystack Observatory, Carolyn Ruppel ’86, SM ’86, PhD ’92 of the MIT Energy Initiative, and César Terrer of the Department of Civil and Environmental Engineering — have been elected as fellows of the American Geophysical Union (AGU) for 2026. 

The AGU also honored eight additional MIT alumni as new fellows: Claudia Benitez-Nelson PhD ’99; Robert Howarth PhD ’79; Douglas J. Jerolmack PhD ’06; Tamara Shapiro Ledley; Galen A. McKinley PhD ’02; Mark David Schmitz PhD ’02; Frederik J. Simons PhD ’02; and Dawn Y. Sumner PhD ’95.

With some 60,000 members in more than 135 countries, the AGU is the world’s largest professional organization for Earth and space scientists and annually elects less than 0.1% of its membership to fellow status. Fellows are chosen from among AGU members who “have made exceptional contributions to Earth and space science through a breakthrough, discovery, or innovation in their field.” AGU Fellows are recognized for their scientific eminence, demonstrated through breakthroughs, discoveries, or innovations that advance the Earth and space sciences. Fellows also act as external experts, advising government agencies and other organizations outside the sciences.

Anthea Coster

Anthea J. Coster is an MIT principal research scientist and Haystack Observatory assistant director emerita.

Coster joined MIT in 1984, originally at MIT Lincoln Laboratory. At Haystack, her career work focuses on atmospheric physics across the ionosphere, magnetosphere, and thermosphere, along with space weather effects and coupling across these different parts of the complex Earth system. She has been responsible for numerous breakthrough contributions in global navigation satellite signal (GNSS) applications to geospace research, including the first GNSS observations of storm-enhanced density across the heavily populated continental United States. She is a recent winner of the Appleton Prize from the International Radio Science Union.

One of the nomination letters for Coster’s AGU Fellowship states, “recognizing that high-level conferences alone were insufficient to provide the necessary training for researchers in developing nations, she co-founded the annual GNSS workshop series (since 2009) in partnership with Boston College and ICTP [the Abdus Salam International Center for Theoretical Physics], training 40–50 participants yearly from developing countries … Through the U.N.-sponsored International Space Weather Initiative (ISWI) and other programs, she has participated in numerous public outreach initiatives, consistently working to make space science accessible to diverse audiences worldwide.” Her nomination also mentioned “[h]er efforts in bringing space physics to South America, Africa, and tribal Alaska ... as does her work with advocating for opportunities for women and underserved populations,” and highlighted her integrity, work excellence, respect for colleagues, and collaboration efforts, as well as her public outreach activities.

Carolyn Ruppel

Carolyn Ruppel, who earned three degrees from the MIT Department of Earth, Atmospheric and Planetary Sciences, is currently the deputy director of science and technology at the MIT Energy Initiative (MITEI). Ruppel, a life member of the AGU, is an expert on gas hydrates, an icelike combination of water and concentrated methane that sequesters an estimated 15% of Earth’s massive methane budget. Before joining MITEI in 2025, she held positions at Woods Hole Oceanographic Institution (1992-93), Georgia Tech (1994-2006), and the U.S. Geological Survey’s (USGS’s) Woods Hole coastal and marine research center (2006-2025). In her current position, Ruppel supports Director William H. Green in leading MITEI’s strategic and technical activities. She coordinates among member companies, MIT researchers, and MITEI’s internal teams to manage its portfolio of energy research, education, and outreach.

Starting in 2010, Ruppel led the renowned USGS Gas Hydrates Project, focusing on the resource potential of these shallowly buried and widely distributed deposits, as well as possible feedbacks among warming climate, gas hydrate breakdown, and release of methane (a greenhouse gas) to the ocean and atmosphere. In 2011, Ruppel wrote the supplementary paper on gas hydrates for the MITEI study on “The Future of Natural Gas.” Ruppel also led the influential study debunking catastrophic scenarios for massive gas hydrate breakdown due to global warming and co-discovered over 700 seafloor methane seeps on the U.S. Atlantic margin. Ruppel’s other scientific contributions have been in the fields of continental rifting, subsea permafrost distribution, hydrate reservoir dynamics, and marine heat flow and acoustics.  

César Terrer

César Terrer is the TianFu Group Career Development Professor and associate professor of civil and environmental engineering at MIT.

Terrer studies the land carbon sink: The roughly 30 percent of annual carbon dioxide emissions that plants and soils absorb each year, and whether that will hold through this century. It is one of the largest uncertainties in climate projection. Earth system models disagree about the fate of the land sink by an amount comparable to decades of emissions, largely because they represent the biology that governs it through assumptions rather than measurements. Terrer’s group replaces those assumptions with data, combining global observations — CO₂ experiments, soil inventories, forest plots and satellites. That work has shown that soil nutrients limit how much extra carbon plants take up as CO₂ rises, about three times less than models had assumed; that plant and soil carbon trade off against one another rather than accumulating together as models assume; and that animals are part of the carbon cycle, with tropical forests that have lost their seed dispersers regrowing less than a quarter as much carbon as intact ones. His group is now testing whether soils, rather than vegetation, account for most of the recent land sink. 

All of Terrer’s first-author papers are cited in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. NASA selected him to co-lead the modeling and data synthesis working group for PANGEA, the scoping study shaping the agency’s next tropical field campaign, and Apple, Cargill, PepsiCo, Inditex, and other organizations have sought his advice on natural climate solutions.

Alumni honored

The eight additional MIT alumni honorees elected AGU Fellows graduated from the following departments:

Claudia Benitez-Nelson PhD ’99 — Earth, Atmospheric and Planetary Sciences (with WHOI);Robert Howarth PhD ’79 — Biology (with WHOI);Douglas J. Jerolmack PhD ’06 — Earth, Atmospheric and Planetary Sciences;Tamara Shapiro Ledley PhD ’83 — Meteorology;Galen A. McKinley PhD ’02 — Earth, Atmospheric and Planetary Sciences;Mark David Schmitz PhD ’02 — Earth, Atmospheric, and Planetary Sciences;Frederik J. Simons PhD ’02 — Earth, Atmospheric, and Planetary Sciences; andDawn Y. Sumner PhD ’95 — Earth, Atmospheric, and Planetary Sciences.

AGU Fellow awards will be presented at the AGU Fall Meeting in San Francisco in December.

The MIT Quilt Show, put on as a collaboration between the Crafters’ Circle, MIT Makerspaces, and the MIT Women’s League, and funded by the Council for the Arts at MIT, ran for three days in Lobby 13 Oct. 5-7 and drew 950 visitors.

“We got wonderful feedback throughout the show, and it was tremendously gratifying to see the MIT community turn out en masse to support this art form,” says Jane Halpern, show organizer and communications officer for the Department of Electrical Engineering and Computer Science.

The exhibit featured around 40 quilts and quilt-adjacent works from about 20 members of the MIT community, plus collaborative works such as an astrophysics community quilt, assembled by graduate student Swati Ravi, and an American flag featuring the embroidery work of refugees, assembled by Anh Vu Sawyer MBA ’20 and her husband Philip.

While the quilt show was a one-time event, Halpern encourages MIT community members who are interested in adding more quilts to their lives to join the Crafters’ Circle Slack channel; check for class listings in the Makerspaces; and visit the New England Quilt Museum in nearby Lowell, Massachusetts.

Most biologists who study pathogenic microbes have focused on strains such as Escherichia coli or Staphylococcus aureus, which are not only important for human health, but also relatively easy to grow in the lab. 

Rebecca Lamason, an MIT associate professor of biology, wants to take a more expansive approach that includes bacteria that are more challenging to study. Tackling those microbes, she says, can help scientists learn more about the huge diversity of strategies that pathogens have evolved to interact with their hosts. 

In her lab, she has devised new tools for analyzing pathogens such as Rickettsia parkeri, a tickborne bacterium that causes a mild form of spotted fever. She also studies Listeria monocytogenes, a microbe commonly linked to foodborne illnesses.

From studying these microbes, she hopes to learn more about the many strategies that pathogens have devised to take over host cell machinery, which also offers insight into the fundamental functions performed by those host cells.

“We cannot learn everything from a single type of bacterium or a single type of cell,” Lamason says. “There’s a rich diversity of biological mechanisms that are out there, and I think we have just barely scratched the surface on that.”

In just one recent example, her lab found that R. parkeri can form very tight connections with a cell structure called the endoplasmic reticulum (ER), which mimics some of the interactions that the host cell’s own organelles have with the ER. They’re still puzzling out the reason for this interaction, which hasn’t been seen with other microbes.

“It’s one of those things too where we had gone into the microscope room to look at something else to ask a different question, and we discovered this other unexpected interaction,” Lamason says. “This has been a really fun experience, and it’s also very common when you’re doing cell biology experiments: you see some phenomenon that you didn't expect.”

How systems interact

As a high school student, Lamason was drawn to cell biology and physiology, and she decided she wanted to pursue a PhD even before she knew exactly what that career path would look like.

“I didn’t have anybody in my family that had a PhD or worked in science, and so I didn’t really know what it took or what it was to get a PhD, but for some reason I wanted to do it,” she says. 

Her father was in the military, so her family moved quite a bit, mainly between Florida, California, and Virginia, before settling in Pennsylvania. After graduating from high school, where she was the editor-in-chief of the school newspaper, Lamason spent two years at a local community college before transferring to Millersville University to finish her bachelor’s degree.

At Millersville, she majored in molecular biology and biotechnology and worked in a lab studying chlorella viruses, which infect algae.

“We were characterizing different variants that we could isolate from a pond that was next to the building, so we were looking at very simple questions, but that experience allowed me to see what it was like to ask questions, what it was like to work regularly in a lab,” she recalls.

To gain more experience before applying to graduate school, she worked as a research technician at Hershey Medical Center for a couple of years, in a lab studying zebrafish as a model to understand the genetics of cancer.

As a PhD student at Johns Hopkins School of Medicine, where she studied immunology, Lamason realized that what she liked most about biology was studying how different biological systems interact with each together.

“The immune system was an interesting way to get that, because there were all these moving parts, all these cells that had to talk to each other to mount an effective immune response,” she says. “Studying immunology helped me realize what I liked more and more was cell biology. How does the cell, a really complex environment, respond to these external signals and commit to a program or a response that then supports some kind of function?”

For her postdoc, she chose a lab where she could look at those questions through the lens of host-pathogen relationships, at the University of California at Berkeley. 

“What really excited me about pathogens is that they are these wonderful little cell biologists, who often have to navigate the complex environment of the host cell and have evolved unexpected ways to solve problems,” she says. “If they have to get through a barrier, or they have to tell the cell not to die or they have to turn off a response, they have evolved distinct ways to do this.”

Working with Matthew Welch, a cell biologist at Berkeley, she began studying the two organisms that are now the focus of her current research: R. parkeri and L. monocytogenes.

R. parkeri, similar to viruses, cannot survive outside of a host cell and is fully dependent on the host cell machinery to replicate itself. L. monocytogenes, on the other hand, can survive and replicate on its own.

“These constraints shape the biology and the types of interactions that these organisms can use to cause infection,” Lamason says. “That’s what really got me interested in these two very different bacteria and in studying the different cellular and bacterial factors that drive a specific outcome.”

Solving technical challenges

In her lab at MIT, which she started in 2017, Lamason has identified many of the host proteins and other factors that help those two species of bacteria perform functions such as moving between infected cells, which they each do in different ways. In order to do much of this work, Lamason and her students, postdocs, and technicians have had to develop new techniques for studying these microbes. 

Until recently, there was no way to target specific genes in R. parkeri cells, but using CRISPR, Lamason’s lab developed the first technique for doing so, in 2024. That has made it easier for them to delve into the roles of specific genes of interest. They also developed ways to label bacterial proteins that are secreted into the host cell, making it much easier to pick them out from the cells’ own proteins.

“My lab recognizes the technical challenges that face our field, and so sometimes we’re building things to solve problems and sometimes we’re just chasing down really interesting observations and trying to understand how it works,” Lamason says.

When looking for faculty jobs, Lamason was drawn to MIT by the sense of collaboration she found. When members of her lab need to learn a new technique, or explore the workings of a cell organelle the lab hasn’t studied before, they can find help from one of the many other MIT labs that have expertise in different aspects of biology.

“That’s been the key to our success — not only attempting and having these riskier ideas, but having the support of the community to help us think about how do we execute on those,” she says. “There’s this wonderful community of scientists to help you think through and complete the kind of work that you want to do and also to push you to think bigger about how to approach a problem.”

When she’s not puzzling out biology problems, Lamason likes to take to the ice as part of a women’s hockey league in Boston. She took up the sport as a beginner a few years ago, in part as a way to take her mind off the everyday stresses of academia.

“It’s a good challenge, and a good way to get out of the lab and the office and give you something else to think about. If my grant got rejected, if I was having a bad day at work, it was a good way to shut my brain off for a little bit and focus on staying upright,” she says.

In the first fraction of a second following the Big Bang, matter existed as a trillion-degree plasma of quarks and gluons — subatomic particles that zinged around at close to the speed of light. This roiling soup lasted for just a few microseconds, before the particles coalesced into more stable protons and neutrons. 

To study this primordial fluid, scientists recreate quark-gluon plasma in large particle accelerators, where ions are smashed together at close to the speed of light. These collisions create droplets of plasma roughly the size of an atomic nucleus, that exist for only tens of septillionths of a second.

One way scientists study this plasma is by tracking other particles, such as heavy quarks, that are not part of the plasma but happen to move through it. The way a heavy particle moves through a fluid should tell something about the microscopic structure of the fluid itself. This general relationship was established famously by physicist Albert Einstein in 1905. 

But in such extreme conditions, where particles are moving at close to the speed of light, something in Einstein’s theory starts to break down; there are interactions within the plasma that the theory can’t quite capture.

Now, physicists at MIT and the University of California at Santa Barbara (UCSB) have taken up where Einstein’s theory left off. They have worked out a theory that predicts how a relativistic particle, moving at close to the speed of light, is likely to be pushed and pulled by a fluid as it flies through it. 

The researchers envision that the new theory could be applied to predict how a heavy quark will move through quark-gluon plasma. Such predictions could then be compared to experiments to characterize the microscopic structure of quark-gluon plasma, and what kind of fluid must have existed in the universe’s first moments.

“Our result reveals a new source of information carried by heavy quarks in rare, unusually large changes in their motion,” says Jean Du Plessis, a graduate student in MIT’s Center for Theoretical Physics – a Leinweber Institute. “How often those large changes occur depends on the microscopic nature of the plasma, raising the possibility that they could provide a new way to learn what the quark-gluon plasma is actually like inside.”

Du Plessis and Bruno Scheihing-Hitschfeld of the Kavli Institute for Theoretical Physics at UCSB report their findings today in Physical Review Letters. 

A path from pollen

In 1905, Einstein developed a theory to describe a particle’s motion through fluid and how that motion can reveal the fluid’s microscopic structure. His theory was inspired by the botanist Robert Brown. In 1827, Brown observed a surprising phenomenon: Grains of pollen appeared to randomly jiggle and swirl when suspended in seemingly still water. This curious jittering of particles in fluid was later named “Brownian motion.” 

Einstein’s 1905 theory showed that Brownian motion was a product of invisible, microscopic forces within the fluid. A particle’s motion is a balance between the drag that the fluid exerts on the particle to slow it down, and the random kicks within the fluid that keep the particle moving. These random kicks, Einstein argued, must come from microscopic forces within the fluid, exerted by none other than the atoms and molecules that make up the fluid. The energy of these microscopic particles can add up to kick and jostle larger particles. 

Since he first published his theory, scientists have applied Einstein’s explanation of Brownian motion to characterize a host of fluids, including quark-gluon plasma. Like most other fluids, it was assumed that a particle moving through quark-gluon plasma should experience a similar balance of microscopic kicks as a grain of pollen moving through water. 

This balance is represented by a bell-shaped probability distribution, where a particle is most likely to experience medium forces of kicks (the highest point of the bell), and an equally low probability of the most extreme kicks (the tail ends of the bell), as the particle moves through and eventually settles into a fluid. 

“Einstein showed that the drag and the random kicks must balance in just the right way for a particle eventually to come to the same temperature as its surroundings,” Du Plessis explains. “For the types of fluids he was thinking about, he was correct. But for particles that are moving at relativistic velocities, this relationship breaks down. And we’ve known for 20 years that something is missing. So we have to go beyond the work that Einstein did.”

Kick speed

In their new study, Du Plessis and Scheihing-Hitschfeld calculated the forces that a heavy quark would likely experience if it flew through a fluid at relativistic speeds, close to the speed of light. They did so by carrying out calculations of the strong nuclear force, which is the fundamental force that pulls together quarks and gluons. 

The team started with a scenario of a relativistic heavy quark flying through a gas of weakly interacting quarks and gluons. The weak interactions made this a manageable starting point for their calculations.

For this starting scenario, they calculated the heavy quark’s strong nuclear force and how much its momentum is likely to change as it moves through the gas of quarks and gluons. This calculation produced a curve, or a probability distribution of the quark’s changes in momentum. The same curve can represent the quark’s probability of being kicked with various force; stronger kicks cause bigger changes to the quark’s momentum. 

The curve that the researchers calculated was bell-shaped, similar to what Einstein found with other fluids, only for small kicks. And, unlike Einstein’s bell-shaped curve, what the team found showed a tilt for heavy quarks moving at relativistic speeds. The tails at either end of the curve were uneven, and revealed a surprising imbalance in the kinds of kicks that a heavy quark would experience.

“The usual Brownian picture assumes that large kicks become exceedingly rare,” Du Plessis says. “For heavy quarks, however, these rare kicks are more common than the bell curve predicts, and are not equally likely in opposite directions: A large kick that slows the quark down more than expected from drag is more likely than an equally large kick that leaves it moving faster than expected.”

The team carried out the same calculation in plasmas made up of different particles. They found that the same tilted curve applies, regardless of the makeup of the plasma. Furthermore, they found that this same structure was already hiding in an earlier calculation in a strongly coupled plasma, despite the differences in the microscopic physics.

“This is the first time that this seemingly universal structure was realized and really formalized,” Du Plessis says. “This gives a concrete path forward for how to better understand the quark-gluon plasma, using heavy quarks.”

In terms of what this means for the universe’s first fluid, Du Plessis says that primordial quark-gluon plasma is likely “more strongly interacting than this Brownian motion way of modeling it would suggest. It’s more like a liquid and less like a gas.” 

This research was supported, in part, by the Office of Nuclear Physics in the U.S. Department of Energy’s Office of Science, the U.S. National Science Foundation, and the Simons Foundation. 

In an emergency, a human might someday ask a robotic assistant to fetch a wall-mounted defibrillator located down the hall. But if the robot has incorrectly labeled the defibrillator as a fire extinguisher in its stored memory, it may not know where to find the life-saving device.

This robotic assistant could benefit from a new memory system created by MIT researchers. The system identifies entries in a robot’s memory that might be wrong and corrects the descriptions of mislabeled objects. 

In the future, this method could make household or industrial robots better able to perform complex, multistep tasks, such as clearing a dining room table or working alongside human collaborators to assemble a piece of machinery. 

Called UQ-DAAAM (which stands for Uncertainty Quantification for Describe Anything, Anywhere, at Any Moment), the system compares multiple descriptions of the same object, seen from different viewpoints, to quantify the robot’s uncertainty about what it saw. 

It then selects additional views to reduce the uncertainty and combines these captions into a more accurate description of the object, which it saves to memory.

The researchers also employed a special technique to streamline this process so a mobile robot can continually update uncertain memory entries in real time. 

When compared to baseline methods, UQ-DAAAM boosted the robot’s confidence and improved the accuracy of its answers to questions about objects, their locations, and when it saw them. 

“Contrary to ChatGPT, when we think about physical AI, it is not enough to simply put a disclaimer cautioning that sometimes the robot might be wrong. Allowing a robot to know what it doesn’t know is going to be essential for this technology to be deployed safely and have a positive impact on society,” says Luca Carlone, an associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), director of the MIT SPARK Laboratory, and the senior author of the paper presenting UQ-DAAAM.

He is joined on the paper by co-lead authors and MIT graduate students Harry Zhang and Nicolas Gorlo. The research will be presented at the Conference on Neural Information Processing Systems. 

Amending memory

This new method builds on the researchers’ prior work, DAAAM, a robot memory system that uses a vision-language model (VLM) to richly describe objects in scenes a robot captures with its onboard cameras. These descriptions are stored as detailed captions in a 3D map-based representation of the robot’s environment that it can call upon later to answer questions. 

But the robot sometimes sees objects only partially or from far away, or its camera might capture blurry or poorly lit images. In these cases, DAAAM might describe these objects incorrectly, such as by mistaking a gray chimney for a squirrel perched on a rooftop.

“With no way to correct itself, this wrong memory would persist as the robot proceeds in its environment, which could impact downstream applications,” Zhang says.

UQ-DAAAM integrates uncertainty quantification into the DAAAM framework. It allows the robot to measure its confidence in the accuracy of object descriptions and fix incorrect captions as it travels through an environment.

While on the move, the robot constantly collects and stores images of the world around it. Many of these images show the same objects captured from different angles. UQ-DAAAM uses these additional viewpoints to identify and correct unreliable descriptions.

First, it calculates the VLM’s confidence level in the words it generated to describe a potentially mislabeled object. Then, it measures consistency by comparing this description to captions of images that describe the same object from different vantage points.

“We fuse both sources of uncertainty into a single number that captures how certain the VLM is in its overall understanding of the object,” Carlone explains.

If that number exceeds a certain threshold, the uncertainty is too high, and UQ-DAAAM will refine the caption by using additional images of the object.

It does this using a heuristic, or simple rule, that determines which additional viewpoints will reduce the robot’s uncertainty about the object. For instance, it might choose an image captured a few frames later that clearly shows smoke emanating from the chimney. 

The researchers mathematically proved that this heuristic can identify the best frames to select without rerunning the VLM, dramatically reducing the amount of computation. 

“The key challenge Nico and Harry had to overcome is how to combine VLMs with uncertainty quantification into something that is running in a fraction of a second on a robot that is moving,” Carlone says.

Remembering with confidence

Once it has identified additional captions that will reduce its uncertainty about the object, the system uses an LLM to fuse them into a single object description. To boost accuracy, the LLM puts more weight on new captions and those with less ambiguity.

This refinement step is able to resolve ambiguities for about 92 percent of the objects in the robot’s environment, while only requiring two additional captions per object. It increased the accuracy of object descriptions by 5 percent compared to the state of the art.

UQ-DAAAM also provided more accurate answers to questions about the objects it observed, where it saw them, and when it last encountered them than baseline methods.

“Performing this uncertainty quantification procedure across the entire map the robot generates allows it to improve its understanding of what it has observed,” Gorlo says.

Next, the researchers want to use UQ-DAAAM to guide different types of robots, like mobile robotic manipulators, and apply their uncertainty quantification techniques to other domains. 

In addition, they are studying the use of UQ-DAAAM to identify areas of high uncertainty the robot could revisit to improve its understanding of the environment. They are also exploring uncertainty quantification in systems that predict the future state of a robot’s environment.

This research is supported, in part, by the U.S. Army Research Laboratory and the U.S. Office of Naval Research. Carlone is currently on sabbatical as an Amazon Scholar; this article describes work performed at MIT and is not associated with Amazon.

Before he passed away on July 11, Susumu Tonegawa had been a professor at MIT for more than 40 years. At a memorial service Oct. 16, colleagues, mentees and other friends spanning almost the whole arc of his illustrious MIT career gathered with members of his family to celebrate his life with recollections and stories that praised not only his science and achievements, but even more so his humanity.

The picture that 16 speakers collectively painted was one of a man who simultaneously demanded excellence and yet consistently demonstrated kindness and a joyous enthusiasm for science (and art and baseball). 

Although he was a 1987 Nobel laureate for his breakthrough immunology research and is renowned for his groundbreaking research on memory, the testimonials dwelled little on his awards. Instead, they focused on a much more personal and intimate degree of inspiration and impact. 

Scientist and mentor

Picower Professor Myriam Heiman, director of The Picower Institute for Learning and Memory that Tonegawa founded as MIT’s Center for Learning and Memory in 1994, had been a colleague of Tonegawa’s for 15 years. But she shared that his work in the fields of immunology and memory had been an inspiration to her long before that — ever since she was an undergraduate student at Princeton University. There, she studied his work to explain the diversity of antibodies during an immunology class at the same time she was reading a Proust novel dedicated to how memory and art can restore and make immutable what would otherwise be lost to time.

Professor Amy Keating, head of Tonegawa’s home department of Biology, hailed his passion for molecular biology as an inspiring example for students. She noted that he took big risks to learn the emerging subject in the 1960s by moving across the Pacific Ocean to enroll in graduate school in San Diego. And while he achieved historic results by applying molecular biology to both immunology and neuroscience, he only did so after persisting through worries that he and his fellow PhD students shared about the future of the field when they graduated in 1968.

Much like Tonegawa learned molecular biology from the ground up, former graduate student Arvind Govindarajan PhD '05, now at McKinsey & Co., joined Tonegawa’s lab in 1999 based on his curiosity about the brain, rather than based on having had any prior neuroscience training. He affirmed that Tonegawa encouraged his mentees to be ambitious and to dream big, even if they didn’t start out familiar with the subject matter. Govindarajan quoted Tonegawa as saying, “It is irrelevant what you know. It only relevant what you want to work on.” And similarly encouraged him to “have no fear, if the problem is interesting, go forth and do it, but when you publish, it has to be perfect.”

Another early mentee, Tom McHugh PhD '01 of the RIKEN Center for Brain Science in Japan, similarly described Susumu’s assertive encouragement, quoting Tonegawa as saying, “A journal doesn’t make your paper good, your paper makes the journal good.” But McHugh also noted that Tonegawa advised against ever resting on one’s laurels by often quoting the Japanese proverb: “after victory, tighten your helmet.”

Although Tonegawa pushed for excellence, even down to the details, 1980s mentee Juan Lafaille of New York University reminded the gathering, “he was, at least, as demanding of himself as he was of us.”

And Tomás Ryan, a postdoc in Tonegawa’s lab from 2010 to 2016, emphatically seconded the idea that Tonegawa’s advice was not only worth receiving, but also passing on. He shared a list of seven lessons that guide how he runs his own lab at Trinity College in Dublin, from where he flew in to join the ceremony.

“In Ireland we often say, you should never meet your heroes, you’ll only be disappointed,” Ryan said. “For me, the exception to this maxim was Susumu … I was incredibly fortunate to have Susumu as a mentor.”

Builder of communities

Many other speakers remembered Susumu as a community builder, not least because he founded the Picower Institute. 

MIT School of Science Dean Nergis Mavalvala praised Tonegawa for building a community of neuroscience at MIT that has had impact well beyond his pioneering work on how memories are stored, recalled, and associated with feelings and relationships. The Picower Institute today comprises 16 labs and about 400 people, and hundreds more have trained in its labs over the decades.

“It takes a vision bigger than your own science and your own lab to be part of founding something this influential, and I really admire him for that,” Mavalvala said. 

Matt Wilson, the first professor Tonegawa hired into the “Center for Learning and Memory,” recalled Tonegawa’s absolute confidence. Wilson for instance, didn’t bother renovating any lab space, because he believed (correctly, it turned out) that Tonegawa would deliver on the promise of an entirely new building (Building 46).

“The thing that really struck me was not just his infectious enthusiasm, but the absolute certainty that he had that this was going to be a success, that this was going to have impact,” Wilson said. “We built everything from scratch.”

Govindarajan recalled how Tonegawa involved himself deeply in the design of Building 46, working with architect Charles Correa to make sure every lab space would be the best it could be. And MIT Corporation member and longtime Tonegawa friend Barrie Zesiger fondly recalled a story about how when the building was new, Tonegawa stood with her at a huge window overlooking the train tracks that run directly underneath. They stayed perfectly still with their hands on the window as the train ran under, but after it passed he was elated that the vibrations were so minimal.

“It was like a child, the joy on his face,” she recalled. 

Biology Professor Chris Kaiser recalled how as an assistant professor he worked with Tonegawa when he was recruiting young faculty to the new learning and memory center, including Wilson, Troy Littleton, and Elly Nedivi.

Zesiger said, “He loved his learning and memory colleagues — loved them all, all of you.”

Honest, joyous, and kind

Picower Professor Li-Huei Tsai, among the later of Tonegawa’s Picower faculty recruits, said she frequently sought his advice during her 17 years as Picower Institute director. He was “unfailingly honest,” yet warm and supportive, she said. And he never lost his sense of wonder.

“I came to value that honesty enormously,” Tsai said. “It is only now beginning to sink in that I will no longer be able to turn to him for his sage advice.’’

Longtime and current lab member Michele Pignatelli also admired the childlike passion and wonder that Tsai and so many others recalled. He recounted how, when he first learned from Tonegawa’s wife Mayumi that Tonegawa had passed away, he initially felt heartbreak and anxiety, but that quickly transitioned to “an overwhelming sense of gratitude.” 

“Working with Susumu has been the greatest honor of my life,” Pignatelli said. “We will keep the flame burning.”

In his own personal tribute to Tonegawa, Peter Mombaerts PhD '92, a graduate student from Tonegawa’s immunology days who is now the director of Max Planck Research Unit for Neurogenetics in Frankfurt, was one of the few to dwell even for just a moment on Tonegawa’s Nobel Prize. But he did so only to make the point that Tonegawa did not suffer from “Nobelitis,” a loss of humility and perspective said to sometimes afflict those who’ve achieved their field’s highest honor.

In an especially poignant anecdote illustrating Tonegawa’s capacity for humility and kindness, current lab postdoc Quentin Ferry shared that he has a young daughter with a set of disabilities that often leave people unsure of how to interact with or understand her. She is often therefore left out. But at a lab cookout last summer, Ferry heard his daughter laughing. Tonegawa was playing with her joyfully.

“He didn’t see a condition to be nervous around, he saw a little girl who wanted to play,” Ferry said. “We will all remember Susumu’s brilliance. What I wish is that we also carry forward his kindness.”

Memorial fund

At the memorial, Picower Institute director Myriam Heiman announced that a generous donor has pledged $100,000 to launch the Susumu Tonegawa Memorial Fund, an endowed fund that will provide enduring support for curiosity-driven research at the Picower Institute. 

Additional gifts will help grow the fund and expand its long-term impact, ensuring that Tonegawa's spirit of scientific discovery continues to inspire future generations of researchers. To contribute, give online or email memorial-gifts-office@mit.edu.

MIT researchers will lead a four-year, up to $38 million project, funded by the U.S. Advanced Research Projects Agency for Health (ARPA-H), aimed at developing a noninvasive, wearable patch that continually monitors biomarkers of heart disease and sepsis.

The project is called PYTHIA, which stands for “predictive platform for physiological and therapeutic health intervention and adaptive management.”

According to the project plan, the patch, which will capture clinical-grade health information in real time, will be powered by heat from the patient’s body. It could provide essential information to help clinicians diagnose heart failure and sepsis early, before the diseases have caused irreversible damage. 

The researchers believe the minimally invasive device could also track the progress of a patient’s recovery after they are discharged from the hospital, and assist clinicians in developing more effective therapeutic approaches.

Catching common killers

Heart disease is the number one cause of death in the U.S., while sepsis kills at least 350,000 U.S. adults each year and is the leading cause of death in hospitals, according to the Centers for Disease Control and Prevention.

“We targeted these diseases because they are very lethal, but we also see opportunities for detecting biomarkers related to heart disease and sepsis to offer a solution that could really help improve diagnosis and treatment,” says principal investigator Ahmad Bahai, a professor of the practice in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics at MIT.

Bahai will be joined on the project by MIT researchers Joel Voldman, the William R. Brody Professor of EECS; Tim Swager, the John D. MacArthur Professor of Chemistry; Hadley Sikes, the Willard Henry Dow Professor in Chemical Engineering; and Colin Stultz, the Nina T. and Robert H. Rubin Professor of EECS; with additional clinical and industry collaborators.

“We have created an ecosystem of R&D, with MIT at the center of it, to facilitate not only leading-edge research, but also a path toward translation into a device that can be used in the clinic,” Bahai says. “Our goal with this project is to impact the future of personalized health care.”

PYTHIA grew out of the MIT Program for Health Sciences and Semiconductors (HS+S), which Bahai co-founded when he joined MIT three years ago. The program focuses on leveraging advances in semiconductor technology and nanotechnology to invent new, transformative approaches to diagnostics, therapeutics, and drug discovery.

Inspired by his previous successful work on continuous glucose monitors, Bahai conducts research on a wide range of technologies including electrochemical devices, optics, magnetics, and ultrasonics in his “Waves, Bites, and Molecules” lab. His group’s goal is to offer clinical-grade wearable devices that track biomarkers continuously to enable real-time intervention techniques.

The glucose monitor research inspired Bahai and his colleagues to consider other biomarkers they could monitor in real-time using semiconductor technology, which led to the launch of PYTHIA.

The chip-based, wearable patch will monitor heart failure and sepsis biomarkers in interstitial fluid, which is the fluid under the skin. The biomarkers, molecules in blood plasma that are related to the onset of these diseases, are hard to detect because they exist in very low concentrations.

To measure the biomarkers in detail, the system will execute a chemical reaction that binds certain chemicals with the biomarker molecules in the interstitial fluid, making them easier to identify. The advanced microelectronics in the patch will conduct measurements, capturing biomarker data with enough sensitivity for clinical applications. 

Since the device will conduct measurements continuously, amassing a huge amount of data, PYTHIA will incorporate on-chip AI models to translate raw data into useful information for clinicians.

“This is kind of like having the patient give a blood test every 15 minutes or so, but it happens automatically. That is the power of this technology,” Bahai says.

From here, the team will start developing and testing electronic components for the tiny, low-power chip. Then they plan to advance the wearable device through pre-clinical and clinical trials, to the point that it can be commercially scaled and manufactured, Bahai says.

“This project has real potential to improve health care because, in addition to leveraging MIT’s leading-edge science and technology, we are also teaming up with clinical and industrial collaborators with direct experience of the practical constraints, the regulatory environment, and the clinical need,” he says. “The impact of this research could be huge.”

PYTHIA is funded through the ARPA-H Delphi Program led by Program Manager Leonard Tender. Delphi research teams apply a microelectronics design strategy to biosensing in an effort to develop devices that can safely, securely, and continuously monitor the body’s internal health signals. 

The project described here is supported by ARPA-H under agreement number AY2AX000243. The content of this announcement does not necessarily represent the official views of the Advanced Research Projects Agency for Health.

The MIT Combat Robotics Club (CRC) uses the tagline “build, battle, destroy, repeat,” which is a pretty spot-on description for a club that embraces learning opportunities through destruction. 

“We make fighting robots,” explains Ryan Duarte, the 2025-26 CRC captain and current captain of a sub team for CRC’s ARES robot. “We try and make something indestructible to kill someone else's creation [that] they've tried to make indestructible … and they're trying to kill ours. In other competitions, the winner is who scores more points. You can tell at the end of a match by looking at [the robots] who the winner is.” 

The club, which was founded in 2001 and is housed in the MIT Edgerton Center, was rebooted in 2023 with new members and designs. CRC sub teams compete in the National Havoc Robot League (NHRL) Professional Robot Fighting League. Competitions are hosted approximately every two months, including “house battles” on campus for the MIT community and local teams. Fans of the hit show “BattleBots” might remember some of CRC’s past robots, including Uppercut, SawBlaze, the Dentist, and Overhaul.

ARES, named for the Greek god of war, competes in the 30-pound weight class (other NHRL categories include the 3lb and 12lb weight classes). The robot combines a “Beater Bar,” or vertical spinner, with a compact, dual redundant poly-v belt brushless weapon power system on a high-performance brushless four-wheel drive with lightweight aluminum, titanium, and ultra-high-molecular-weight polyethylene chassis. 

Abi Dixon, 2026-27 CRC captain, says the fighting machine was created from “from steel, titanium, aluminum, and passion.” The robot was a top seed going into last winter’s 2025 NHRL World Championships, but failed to emerge victorious. “Our two losses with ARES were against two opponents which shared a similar weapon type which our underdeveloped armor couldn't withstand,” says Duarte. The team saw the losses as an opportunity to make improvements. 

 “A unique thing about our outlook for these competitions is that no matter how poorly a match goes on our end, afterwards the dominant emotion is still pride,” says Dixon. 

“Being in this community and on this team has changed me,” she says, because “how can you put so much energy, so much love, so much blood, sweat, and tears into a machine and then be totally OK with putting it inside the cage and it exploding, and then literally picking up your pride off the floor, smushing it all back together, and getting back in there? You grow an understanding of finding the wins, even when things look like a complete loss.”

ARES, captained by Duarte, along with fellow Department of Mechanical Engineering (MechE) undergraduates Henry Dang, Kendall Binkley, and Liam Gault, has been undergoing improvements. The team took second place at an April competition and third in October. CRC is simultaneously upgrading ARES and another robot, KRATOS, while continuing progress toward a next generation of robots. 

At MIT, the CRC is open to everyone regardless of year, major, or prior experience. It is entirely student-run, self-taught, and highly collaborative. Participants engineer tough, reliable, performant systems and have an opportunity to learn a variety of skills, from computer-aided design and finite element analysis to CNC machining and welding. 

“Before MIT, I never had the opportunity to independently say, ‘I'm going to make this,’ and complete everything in the engineering process from start to finish. CRC embodies that vision, and solidified my desire to become an engineer,” says Eleni Morales, CRC team member, 2025-26 co-manufacturing chair, and undergraduate in MechE.

CRC welcomed 54 new members this fall semester. As an onboarding exercise, the new members are building 1-pound, or “ant weight,” combat robots in teams of three students. 

“We remain committed to making the team accessible for students with all levels of technical experience (including none!) through a structured onboarding process that prioritizes each and every member feeling as though they have technical ownership over a part of their sub team’s robot,” says Dixon.

The team will host their third annual MIT Combat Robotics House Battle on Nov. 14 at the new Met Warehouse. 

Global development and prosperity have become increasingly dependent on minerals and metals. This growing dependence has drawn attention to the mining industry and underscored national economic and security vulnerabilities across supply chains. As demand for these materials rises, the challenge for expanding capacity will be to develop scalable pathways to produce, use, and steward them. 

In this interview, Professor Elsa Olivetti, co-director of the MIT Minerals Stewardship Consortium, discusses how collaboration can strengthen mineral and metal supply chains and focus research on practical impact.

Q. In its first decades, MIT had a Department of Mining and Metallurgy. In recent years, we’ve seen increasing interest in mining and minerals at MIT. What explains this resurgence?

A: Six of MIT’s first 14 graduates in 1868 earned degrees in geology and mining. In 1889, one of those graduates, Robert Hallowell Richards, was the MIT professor who explicitly linked metallurgy to mining engineering within Course III [Materials Science and Engineering]. MIT shaped its curriculum to meet the nation’s increasing need for advanced materials for railroads, industrial machinery, and electrification.

It feels very similar to our time. We’ve all heard about the growing demand for minerals to support the energy transition, defense applications, data centers, and global economic development. That includes tiny amounts of gallium for defense radar systems and vast quantities of steel, aluminum, and copper for the grid. But each element and supply chain has its own “personality.” For example, gallium is a by-product of the process to make aluminum, so increasing its supply requires coordination beyond mining.

Mining and mineral processing are environmentally intensive — and becoming more so. It's harder to obtain newly mined minerals, even from places where mining already occurs. Ore bodies are deeper, lower-grade, and more often located in socially and environmentally sensitive regions. Mine projects face shifting water availability, extreme climate events, the concerns of nearby communities, fluctuating mining costs, and advancing digital technologies that change how ore is monitored, extracted, and processed over a mine’s life.

And that explains why we are seeing a renewed focus on mining and minerals at MIT. To meet this moment and serve the nation, not only do we need advances in science and engineering for extraction, but also innovations in the markets and governance to incentivize developers and communities to engage continuously and transparently. Our campus is compelled by mining’s broad, systems-level impact on people, the complex science, and the entrepreneurial opportunity. 

Q. President [Sally] Kornbluth has called curiosity-driven science our “intellectual rocket fuel.” What does fundamental research contribute to solving the challenges of mining and critical minerals? 

A: What’s exciting to me is that these challenges call for fundamental research across MIT. 

As an example, rare-earth elements have very similar chemical properties, making them difficult to separate from each other for the different end use applications we depend on. Researchers are exploring engineered microbes that can bind one metal selectively and withstand harsh conditions. This work draws on basic advances in producing engineered proteins and computational models that predict how metals bind. Other teams are investigating electrochemical approaches to extraction, studying how ions behave in ore minerals including oxides and sulfides to improve separation.

We also need contributions from different disciplines. Researchers in earth, atmospheric, and planetary sciences are partnering with colleagues in aeronautics and astronautics to advance remote sensing of changes in land use. Experts in automation and control are developing ways to keep sensitive separation processes within narrow temperature ranges, while mechanical engineers study fluid dynamics relevant to deep-sea mining. Some minerals are essential but traded in small volumes, raising distinct economic questions. Urban planners examine how community knowledge can inform more equitable and resilient decisions.

And we have to move ideas from the lab to the field. MIT’s Office of the Vice President for Energy and Climate, the MIT Energy Initiative, and other programs are seeding early-stage work. Some projects have attracted follow-on support, including the U.S. Department of Energy’s Genesis Mission, to advance technologies toward pilot testing. MIT startups Lithios and RockZero are pursuing lithium extraction from brines and hard rock, respectively. SiTration, Nth Cycle, and Found Industries are pioneering metal recovery from mining and industrial waste. These examples show how basic discoveries can lead to technologies and policy impact — and how challenges in the field can prompt new research questions.

Q. We see regular calls from the government and others to re-shore the production and refinement of critical minerals and rare-earth elements. What is the role MIT can play, and where do you see opportunities for collaboration?

A: Building domestic and allied capacity is more than just opening mines. Extraction, separation, refining, and manufacturing are technically demanding, and the economics are challenging. Trade restrictions can disrupt supply at any point along the supply chain. A domestic magnet industry, for example, needs reliable access to separated rare earth oxides. Otherwise, we replace a dependence on imported magnets with a dependence on imported materials. There must be capability across the entire supply chain to establish resilience, and it needs to be done responsibly and with a robust workforce. This will take collaboration across industry, government, and research institutions.

MIT can play a strong role as a convener, particularly among industry partners. 

In September 2025, we launched the MIT Minerals Stewardship Consortium (MSC) with three inaugural members — BHP, Vale, and Rio Tinto — which MIT Sloan Professor Chris Knittel and I lead. All of this came together when staff from MIT’s Industrial Liaison Program came to me and said, "We've been having conversations with companies, and they're describing similar needs. Is there an opportunity to have them work together with MIT?" The MSC is a platform to gather stakeholders from across the supply chain with specific aims, such as identifying barriers to technology adoption, to curate research questions and collaborative next steps.

The consortium brings together faculty expertise in operations, urban planning, aerospace engineering, materials science, and economics. Through the MSC and other campus efforts, MIT connects industry, government, startups, and researchers to identify shared priorities and move research toward practical impact.

The U.S. administration and other governments are mobilizing capital, driving regulation, and building international and corporate alliances to strengthen critical mineral supply chains. Through the MSC and engagement across campus, MIT can bring together stakeholders to identify priorities, coordinate efforts, and ensure we help build competitive, resilient supply chains.

Endometriosis affects more than 265 million women worldwide — 1 out of every 10 women of reproductive age, according to the United Nations — yet it remains one of the most misunderstood, under-researched, and chronically stigmatized conditions in modern medicine. 

A new documentary, “End of the Cycle,” presented by The Endometriosis Collective, is changing that conversation about this painful disease, one powerful story at a time.

Directed by Sammy Jaye and Soraya Simi, the film weaves together intimate personal narratives, cutting-edge science, historical revelations, and cultural commentary to challenge decades of misconceptions. The result is both deeply personal and urgently scientific, sending out a clarion call for research investment, medical accountability, and systemic change.

The film features a talented ensemble of women living with endometriosis — comedian Amy Schumer, U.S. Olympian Brittany Brown, actress Janel Parrish, actress and musician Fọlákẹ́ Olówòfóyèkù, dancer Julianne Hough, and influencer and filmmaker Sammy Jaye Lang. When asked to describe endometriosis in three words, their responses cut to the heart of the condition: “agony, isolation, harmful.”

The film also profiles 20th century icon Marilyn Monroe, who struggled with endometriosis until her death in 1962 at age 36.

But “End of the Cycle” goes beyond depicting suffering; it also depicts action, resilience, and hope. Alongside patient stories, the film features leading researchers and clinicians, including MIT Professor Linda Griffith, director of the Center for Gynepathology Research at MIT. Griffith brings both her pioneering scientific expertise and her personal journey living with endometriosis — a perspective that has shaped her life’s work.

“There’s a fair degree of consensus about the definition of endometriosis,” Griffith explains in the film, “but we need to understand the basic process of how normal tissue is regulated so that we can understand the diseased tissue better. That’s my goal.”

Today, her research at MIT is pioneering new approaches to understanding and treating endometriosis. “Only by working together across all our disciplines and putting all our talents in all the different places they are needed, we will find answers, treatments, cures, and even prevention,” Griffith says.

On Sept. 30, more than 250 members of the MIT community gathered alongside industry partners, clinicians, patient advocates, and endometriosis researchers to mark Menstruation Science Day at MIT, centered around a screening of “End of the Cycle.” The event brought together students, faculty, donors, entrepreneurs, and leaders in women’s health for a single purpose: to challenge misconceptions about endometriosis and to catalyze the next wave of research, innovation, and care.

Griffith moderated an expert panel that followed. The panelists — Sammy Jaye Lang, filmmaker with the Endometriosis Collective; Steve Palmer, senior communications associate at Baylor College of Medicine; Eliezer Calo, associate professor of biology at MIT; Stephanie Morris, an obstetrician-gynecologist at Newton-Wellesley Hospital; and Aleshia Carlsen-Bryan, associate director of prehealth advising at MIT Health — offered multiple perspectives on where women’s health innovation stands and what it will take to accelerate progress.

Video by Sammy Jaye and Soraya Simi | 1 hour, 21 minutes