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.

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

Running a modern research laboratory requires more than maintaining sophisticated instruments. Researchers need to complete training, obtain access, schedule and operate equipment, navigate billing, and more. For large, shared facilities, coordinating these activities across hundreds of instruments and thousands of users can be a significant undertaking. As research becomes increasingly interdisciplinary, another challenge is emerging: finding the right capability in the first place.

Addressing these challenges was a central theme of the 2026 NEMO User Meeting, hosted by MIT.nano Oct. 5–6. The meeting brought together 65 representatives from 25 laboratories and organizations, including facility directors, laboratory managers, technical staff, software developers, industry representatives, and government researchers. Sponsored by the Northeast Microelectronics Coalition (NEMC) hub, the meeting focused on sharing approaches to lab management and building connections among facilities.

At the center of the meeting was the NanoFab Equipment Management and Operations (NEMO) system, an open-source platform that provides tools for managing shared research facilities while allowing individual laboratories to adapt the system to their own needs. The meeting highlighted an important characteristic of the NEMO community: The same platform can serve a very diverse set of facilities, and while the scale or the scientific missions of the facilities may differ, their core operational challenges are often remarkably similar. 

For Anna Osherov, associate director of Characterization.nano and organizer of the meeting, the collaborative model is one of the most compelling aspects of NEMO.

“Open source changes the way we think about technology development,” she says. “We can build on what others have already developed, contribute improvements back to the community, and collectively create a platform that is more capable than what any one facility could develop on its own.”

NEMO was originally released as open-source software by the National Institute of Standards and Technology (NIST) in 2018. Its community edition (NEMO-CE), launched in 2023, allows facilities to develop and share extensions to the platform. At the October meeting, participants from organizations including MIT; Stanford, Columbia, and Princeton universities; the University of Pennsylvania; NIST; NASA’s Jet Propulsion Laboratory; and Atlantis Labs presented tools to address challenges ranging from chemical management and user administration to equipment status, training, and workflow automation.

The range of presentations illustrated how differently configured facilities are adapting a common platform to their own needs. Stanford, for example, presented real-time displays for equipment status and reservations, while Columbia shared its experience migrating to NEMO and extending the system for billing and facility workflows. Princeton demonstrated automated process charts for monitoring tool performance, and the University of Pennsylvania presented approaches to chemical management.

Overall, the projects demonstrated the premise behind the NEMO community: a solution developed to address a problem at one facility can become a resource for others.

“Together we can do way more than any one of us individually can,” said Vladimir Bulović, director of MIT.nano, during his opening remarks. “NEMO is the platform that gave us, finally, a streamlined way to do so.”

From individual facilities to a connected research ecosystem

As the NEMO community grows, another opportunity emerges. Universities and research organizations operate specialized facilities, each with unique equipment, expertise, and capabilities. But researchers cannot use a capability they are unaware of or cannot easily find.

This is particularly important for microelectronics R&D, where progress often depends on moving quickly between specialized capabilities. A researcher may need fabrication at one facility, advanced characterization at another, specialized microscopy somewhere else, and a process or analysis capability available at only a handful of institutions.

Finding those capabilities, and knowing where and when they can be accessed, can itself become a bottleneck.Traditionally, networks of facilities have relied on static webpages or databases to describe available resources. But as facilities evolve, this information can quickly become outdated.

At the meeting, MIT.nano presented Hydra, a new open-source platform being developed with support from NEMC as a key component of the NEMC Technical Concierge, a program that aims to help researchers identify and access specialized capabilities across a network of facilities. Hydra is designed to connect multiple NEMO installations and draw information directly from the systems that facilities already use, rather than requiring someone to manually update a separate database. The result is a compilation built from the ground truths of actively-used facility management systems. “Hydra does not replace any lab management system,” Osherov explained. “It connects, synchronizes, and keeps data up to date across all connected systems in real time.”

In this model, NEMO provides the operational source of information within an individual facility, while Hydra makes that information discoverable across facilities.

For a researcher, that could mean finding a specialized capability without first needing to know which laboratory might have the relevant equipment. 

“The focus is to make the distributed infrastructure supporting research more discoverable and usable,” Osherov said.

Building infrastructure that can change

The approach reflects a broader philosophy: Research infrastructure needs to evolve as research itself changes. “We need to build a flexible research space and a community-oriented space so that people feel empowered to go to it, use it, and then redefine it as the years progress, adapting it to the needs of the next generation of researchers,” said Bulović.

That flexibility applies to operational systems as well as physical laboratories. New instruments and research methods emerge, user communities change, and facilities develop new requirements. An open-source platform allows individual facilities to respond to those changes while providing a mechanism for improvements to be shared.

At MIT.nano alone, NEMO supports more than 1,600 active researchers using more than 200 tools, with approximately 160,000 hours of tool usage each year. For Jorg Scholvin, associate director of Fab.nano, operating at that scale makes the efficiency of everyday processes, and the ability to adapt and change quickly, important.

“A small improvement in an administrative workflow can be multiplied across thousands of users and tens of thousands of equipment interactions,” he said.

The NEMO wave

The sense of community and positive outlook for the future of shared facilities was apparent throughout the two-day meeting. Closing discussions looked ahead to the role of data integration, automation, and artificial intelligence in lab operations.

“The community is taking on a life of its own,” said Mathieu Rampant, CEO and co-founder of Atlantis Labs, describing a model in which facility managers can share ideas and solve problems together. “It’s more sustainable,” he added as he talked about having to do less as the NEMO-CE manager because the facilities managers are talking to each other — sharing ideas and solving problems as a network: “the NEMO wave,” Samantha Roberts, director of the City University of New York nanofabrication facility, called it. “Everyone is invested in it and cares about it. It takes a lot of effort to change your lab management system and if we keep this going, we can build out something that’s really cool and that works.”

For Osherov, the purpose remains straightforward.

“Ultimately, this is about enabling research. The better we can manage the operational side of our facilities, the more effectively we can make our instruments, expertise, and capabilities available to the research community.”

In microelectronics, where innovation increasingly depends on access to specialized and distributed capabilities, that availability matters. The ability to find the right capability at the right facility, using information drawn directly from actively used systems, can help researchers move more quickly from an idea to an experiment to real-world application and impact.

MIT was founded on the idea that people learn best by doing. For nearly 25 years, the Lemelson-MIT Program in the MIT School of Engineering has carried that idea into K–12 classrooms across the country, helping teachers guide students as they find real problems and invent solutions to them. 

Now the program is expanding that work into connected pathways that follow students from the early grades through college.

This month, Lemelson-MIT awarded multiyear Invention Education Pathways grants and stipends to 19 schools, districts, and colleges across three regions: Northern California, Tennessee, and Massachusetts/Connecticut. The partners include elementary, middle, and high schools, as well as a community college and two state universities, together forming routes for students to experience invention at every stage of their education.

The initiative arrives as MIT President Sally Kornbluth has launched MIT for America, a strategic effort to widen access to quality STEM education nationwide, from kindergarten to community college. One of its three focus areas, Design, Make and Innovate, aims to give students more chances to build, test, and prototype their way toward solutions, and to support teachers in bringing hands-on work into their classrooms. Invention education does exactly that.

Learning the way inventors work

In invention education, students work in teams to identify problems that matter to them and their communities, then develop prototypes of original solutions. Along the way, they apply knowledge from multiple disciplines and develop both technical skills and durable human skills such as empathy, collaboration, and communication. They also gain confidence in their own abilities.

“Understanding user needs is critical in product design,” says Michael Cima, faculty director of the Lemelson-MIT Program and a professor of materials science and engineering. “It’s the foundation everything else is built on. Invention education builds that understanding early, teaching students to ask whom they’re designing for and what is truly needed before they ever build a prototype.”

Rather than offering a one-time project or an after-school club, the pathways approach aims to give students repeated, connected invention experiences as they advance. Lemelson-MIT staff will work alongside educators in each region to assess, design, and implement learning experiences that help students apply scientific, technical, engineering, and mathematics (STEM) knowledge to real-world problems. Schools and colleges can adopt Lemelson-MIT curriculum resources selectively to jump-start or strengthen their offerings.

“We have learned that invention education can benefit all students, especially when students can experience learning through the invention process across multiple grade levels,” says Stephanie Couch, executive director of the Lemelson-MIT Program. “Our goal is to create opportunities for learning through invention education during the regular school day, beginning in the early grades and continuing into adulthood.”

Every student an inventor

The three regions were chosen through a nationwide search for schools and districts committed to building strong invention pathways from kindergarten through college and to increasing the number of inventors from diverse backgrounds.

That commitment rests on a simple observation from Lemelson-MIT’s work: Given the chance to invent, students of every background surprise themselves, and their teachers, with what they can do. The program’s experience, case studies, and published research show that invention education helps students, especially those underrepresented in STEM, see themselves as capable STEM learners and inventors. Students in the program’s flagship high school initiative, InvenTeams, have earned 24 U.S. patents.

The benefits extend into the workforce. “Employers benefit from graduates that have the types of capabilities they need, but have trouble finding,” Couch says. “Invention education connects students’ knowledge and passions with the technical and durable human skills employers are looking for.”

Participating institutions

• Northern California: California State University at Stanislaus; Columbia College; Columbia Union School District; Denair Unified School District; Folsom Cordova Unified School District; Stockton Unified School District; and Visions in Education University Prep Early College Academy
• Tennessee: Apison Elementary School; Battle Academy; Hixson Middle School; Normal Park Museum Magnet School; and Red Bank elementary, middle, and high schools
• Massachusetts and Connecticut: Berlin High School; Cambridge Rindge and Latin School; New Covenant School; Watertown High School; and Westfield State University

Looking ahead

Over the next three years, the Lemelson-MIT Program will work closely with educators in all three regions toward a shared goal: making invention education a regular part of the school day, rather than an occasional enrichment activity. 

Lemelson-MIT hopes that what these partners learn together will help show how invention pathways can take root in schools and colleges across the country.

Vera Grbic, communications officer at the Institute Discrimination and Harassment Response Office (IDHR), is an expert at creating clear, informative collateral. Whether she is improving the IDHR website to help people more easily find the support or resources they need or using newsletters and annual reports to explain IDHR's work and the policies that guide it, Grbic believes it is vital for members of the MIT community to know where to turn and what resources are available to them. 

Grbic earned a BA in communications, with a minor in English literature, from the University of Ottawa, and an MA in communications from Carleton University. Both institutions are in Canada's capital city of Ottawa.  

Before joining MIT in 2021, Grbic worked in a mix of federal government, nonprofit, and research organizations. After graduate school, she worked as a freelance journalist, during which she developed a skill she still relies on today: collaborating with subject-matter experts to ensure that creative communications are grounded in fact. As a senior communications officer for the National Research Council of Canada, she worked with engineers and scientists in areas such as artificial intelligence, climate research, aerospace, and physiology. She elevated their research and innovations to key audiences and helped create new partnership opportunities. In this role, she translated complex information into engaging communications that furthered the organization's goals. Grbic describes this work as the "through-line" connecting her previous experience to what she does today at MIT. 

IDHR addresses concerns related to discrimination, discriminatory harassment, and sexual misconduct by providing education, individualized supportive measures, information about available resources, and complaint resolution pathways that include both restorative and investigative processes. Grbic develops IDHR’s annual communications strategy and has spearheaded several key initiatives, including a website and annual report redesign and the launch of IDHR’s Data Dashboard. She collaborates with colleagues across MIT to gather content ideas for newsletters, data and stories for annual reports, and feedback on communications materials. She often partners with IDHR's prevention education and outreach team, which delivers training to the community. 

Grbic notes that being a communications professional means constantly learning, whether that entails researching a new topic, finding creative ways to tell a story, or meeting subject-matter experts who broaden her perspective. Accuracy is at the core of her approach. While she strives to find creative ways to explain policies and complex processes in ways that are accessible to everyone, collaboration and fact-checking are intrinsic to her work. Given the wide range of responsibilities within communications, Grbic is a jack-of-all-trades. Her experience includes creative communications, social media management, long-form storytelling, event production, report writing, video production, and website management.  

In addition to her communications responsibilities, Grbic co-organizes the annual Change-Maker Awards, which honor members of the MIT community who work diligently to prevent sexual misconduct and combat harassment. She takes great pride in helping lead an event that recognizes individuals who are making a meaningful difference in building a safer culture at MIT. Grbic describes the award winners as “true inspirations.” 

Grbic recognizes that nothing is done in a vacuum. She values the input of her team members and colleagues across MIT and is proud to incorporate their feedback into her content. Because no two projects are exactly alike, she enjoys the creativity and variety that her work offers. 

Soundbytes 

Q: What are your favorite projects that you have worked on? 

A: I was part of a team that helped develop and launch the most recent sexual misconduct surveys for students and employees. I also strategized a campaign to clarify IDHR services, which included whole-scale website improvement: rewriting many sections, adjusting navigation, improving visual design, and publishing a short video explaining our process. There was also a poster campaign about the services we offer the community. MIT is awash with important storytelling, so getting that messaging to people who need it is vital. These projects brought me pride because taking the time to understand a community's experiences can have a meaningful impact. 

Q: How would you describe the MIT community? 

A: Smart, in a well-rounded way. Everyone is an incredibly hard worker and thinks in deep, research- and experience-backed ways on how to solve complex projects.  

As an employee who has worked with students on Institute-wide initiatives, committees, and awards, I have seen how much they care about making the world a better place. The staff and faculty are gracious and bring a strong work ethic to everything they do in support of the MIT community. 

Q: Are you involved in any areas of the Institute that are not directly related to your job? 

A: I recently started participating in the MIT Women’s League, which I am excited to explore. When I was pregnant, I took prenatal classes at MIT, including a yoga class where I met people I am still in touch with years later. I also played in a volleyball league at the recreation facilities, which made me see the facilities from a different angle.  

I’ve attended many free conferences and events hosted by MIT clubs and schools. For example, a children’s clothing swap has been organized for years, which also provides an opportunity to connect with other families in the community. I also prioritize keeping up with current topics and have attended two AI conferences organized by the Institute.  

While these opportunities may feel secondary to the workplace, they are important. I’m glad I am in a space that helps feed my physical health and has opportunities for broader learning. 

Millions of people rely on the cloud every day, yet researchers working to secure and improve the cloud are often locked out of the technology it runs on. Jeffery Lim is trying to change that.

One such technology, single-root input/output virtualization (SR-IOV), lets major cloud providers like Amazon Web Services boost the performance of their systems. Cloud providers combine SR-IOV with custom hardware and software to deliver faster cloud services than traditional setups allow.

Lim is a computer engineer at MIT Lincoln Laboratory. While pursuing his master's degree, he noticed that academic research proposing smarter or safer cloud infrastructure did not account for SR-IOV. Very few open-source hardware platforms existed that would let researchers work directly with SR-IOV as they developed new software or models to improve the cloud.

So, Lim filled that gap with his master's thesis, building a hardware platform compatible with SR-IOV that captures key features of the infrastructure used by cloud providers. The result is a research and development platform that allows researchers to build, test, and evaluate realistic cloud designs.

"As a society, everyone uses the cloud now," Lim says. "I hope that people will use this platform to build systems more akin to how cloud providers operate, and, more broadly, that researchers everywhere can take advantage of SR-IOV for the performance benefits it offers."

Lim presented a paper at the IEEE High-Performance Extreme Computing Conference in September, and he plans to release this work open source. In recognition of the hardware's potential impact, the Boston University College of Engineering awarded Lim its 2026 Master's Societal Impact Award.

Winding path to cybersecurity

Growing up, Lim was fascinated with building computers. That fascination set him on a winding path to the Secure Resilient Systems and Technology Group at Lincoln Laboratory.

After studying electrical and computer engineering at the University of Colorado at Boulder, he spent several years in industry working on field-programmable gate arrays (FPGAs) — integrated circuits that can be repeatedly reprogrammed for different functions. This background led to his 2018 hire at the laboratory, where he developed FPGAs for small radars that detect the breathing of humans trapped in rubble and for large radars that track satellites and orbital debris from atop Millstone Hill. He next turned to investigating radiation-induced faults in integrated circuits aboard space platforms and ways to protect the chips.

This focus on safeguarding hardware turned his attention to cybersecurity, an area he had long found interest in but never fully explored. So, Lim transferred to his current group in 2022 to pursue this interest.

"I don't think there's another place in the world where I could say, 'Hey, I want to try something new,' and actually have that opportunity," Lim says. "Lincoln Laboratory does that really well, giving people the chance to transition into a different field of engineering or area of expertise."

To deepen his expertise, he applied and was accepted in 2024 to the Lincoln Scholars Program, which allows technical staff to pursue full-time graduate studies at the master's or doctoral level. Scholars remain employed and apply their academic work to national security needs.

As he pursued his master's degree at Boston University, Lim worked closely with his Lincoln Laboratory mentor Benjamin Nahill, who helped Lim keep his research tied to the laboratory's mission and provided a sounding board for ideas. "I'd call him my rubber ducky," Lim says, a nod to the classic software engineering practice of "rubber duck debugging" — reasoning things out through speaking aloud to a rubber duck.

"We talked about his thesis concept for quite a while, but the implementation suddenly became real toward the end," says Nahill. "He transformed what began as an idea for a research tool to an incredibly performant implementation. Having worked with Jeffery on projects in our group, I shouldn't have been surprised, but it was most impressive."

Missing piece for cloud research

When most people say "the cloud," they mean cloud services provided by companies such as Amazon, Microsoft, and Google. Virtualization is the invisible process that makes those services possible. Users uploading photos or streaming movies are interacting with virtual machines, running on servers in data centers.

In the past decade, SR-IOV has changed the way traditional virtualization works. Traditionally, a software component known as the hypervisor acts as a middleman between the virtual machines and the server's physical hardware. Its main role is to distribute computing resources as needed, and provide functions (such as networking) to the virtual machines.

In contrast, SR-IOV enables virtual machines to access a dedicated slice of the hardware device, reducing the hypervisor overhead. This capability is often found on specialized devices called Smart Network Interface Cards (SmartNICs). Major cloud platforms, such as Amazon Web Services’ Nitro System and Microsoft’s Azure Accelerated Networking, use SmartNICs with SR-IOV to provide high-speed networking to many virtual machines.

"This was the missing research capability that I set out to build: an open-source SmartNIC platform compatible with SR-IOV," Lim says. Because SmartNICs can be built on FPGAs, Lim had a familiar starting point. In fact, he found a couple of platforms that supported SR-IOV; however, they lacked features that would make them practical tools for researchers to utilize. He spent a couple of months building those required components.

The first component is a switching fabric. Often, cloud customers' applications or programs may be split across multiple virtual machines, which may be distributed across multiple servers. Therefore, the cloud system needs a way to determine whether a piece of data needs to stay local or leave the server. Lim's switching fabric handles that traffic-directing job.

The second component is a network function engine. This small, specialized processor is built to run security rules, like firewalls, that keep one customer's data safely separated from another's. To build it, Lim used Extended Berkeley Packet Filter, a system that allows custom code to inspect network traffic, approving or blocking data as needed. Rather than starting from scratch, he adapted the design from doctoral research conducted by Zaid Tahir, who studied under their shared advisor, Professor Martin Herbordt of Boston University.

Herbordt described Lim's efforts as "combining vision with heroic system-building, single-handedly putting together the hardware and software necessary to prove the concept."

"I'm very much a hands-on hardware guy, so, yes, I ended up building two servers at home to test my designs," Lim says. He also used the Mass Open Cloud’s Open Cloud Testbed, a test bed meant for testing cloud platforms, to gather metrics and validate his system.

Impact beyond the platform

Throughout the project, Lim maintained focus on applicability and transparency. "My biggest worry was, is this going to be relevant? Is this something that can actually be utilized?" Lim reflects.  

Earning the Societal Impact Award points to yes. The award recognizes projects that address pressing global challenges through innovative applications of technology. Lim's platform opens the door to evaluating a cloud trust model that remains largely opaque, even as many sectors of society, from health care to banking, increasingly rely on it.

"Right now, we have limited visibility into how major cloud providers implement and evolve their infrastructure," Lim says. "If researchers can catch up to how the modern cloud operates, they might develop new approaches that improve security or performance."

As part of his role as a Lincoln Scholar, Lim is also exploring how his work connects to Lincoln Laboratory's national security mission. He sees several potential avenues.

One program in his group is evaluating cyber-hardening techniques for hypervisors, as the cloud takes on an expanding role in defense systems. "Introducing Lim's SR-IOV-compatible platform to this work would help bring our research closer to how cloud environments operate, demonstrating the impact of our techniques in realistic environments," says Nathan Burow, the program's principal investigator. Another program is examining what it takes to stand up high-performance data centers, work that could draw directly on Lim's research into the technologies SR-IOV requires.

Or his path may lead to something new. The laboratory allocates internal technical investment funding — supported by the Office of the Under Secretary of War for Research and Development — for staff-proposed projects addressing emerging needs. Lim is considering a proposal to bring SR-IOV to the embedded-systems level, say, to drones or satellites.

"We saw how cloud providers moved from traditional virtualization to SR-IOV to maximize performance while maintaining isolation," he says. "I wonder if we can do the same at the embedded level, where power and compute constraints are even tighter. I'd love to complete that transition. I learned a lot from the cloud. Now, can we apply those lessons somewhere else?"

Utilizing waste carbon at scale, assessing public trust in artificial intelligence and data centers, and building a quantum toolkit for critical applications were among the topics energy experts explored at the MIT Energy Initiative (MITEI)’s annual research conference on Sept. 22-23.

The complexity of the energy transition demands “a truly interdisciplinary and international approach,” said Paula Hammond, Institute Professor and dean of the MIT School of Engineering, kicking off the conference, which was themed “Transitioning energy at a pivotal moment.” 

More than 200 people attended Energizing@MIT: MITEI’s Annual Research Conference in person and online, including faculty, scientists, and researchers; representatives of global energy firms; nonprofit organizations; and academic institutions.

With the rapid proliferation of artificial intelligence and data centers, the challenge of the energy transition grows ever greater, requiring “researchers who can pursue fundamental questions about materials, chemistry, and energy systems … engineers who can design new technologies and determine how they might function at scale … and experts in economics, policy, human behavior, and sociology who can help us understand how those technologies will interact with communities and with the world around us,” said Hammond. 

From keynote speakers to panelists, seed grant innovators to startup entrepreneurs, the conference featured exemplars in all these categories.

Deploying today’s technologies

Eric Toone, chief technical officer and managing partner at Breakthrough Energy, set the tone for day one with a keynote address that was clear-eyed in his assessment. “If energy is prosperity,” he told the audience, “at least today, prosperity is emissions.” Yet, Toone was optimistic when he heralded examples of Breakthrough Energy-funded ventures, from iron-air batteries to vacuum-insulated glass windows, which can reduce the carbon costs of energy. 

“We can absolutely develop new technologies that provide the exact same service, but at zero carbon,” he said.

The conference made Toone’s claim tangible as it spanned the full arc of decarbonization — from deploying what we know today to uncovering what may be possible tomorrow. 

There are “projects that are actually being developed, companies that are putting steel in the ground now, electrons onto the grid to deliver capacity now, or in the very, very near term,” said John Parsons, deputy director for research at the MIT Center for Energy and Environmental Policy Research. Parsons moderated a panel on the revival of nuclear fission power. 

Highlights included Crane Clean Energy Center’s restoration of the Three Mile Island Unit 1 nuclear plant, set to restart in mid-2027; work by Électricité de France (EDF) to extend and modernize France’s 57-reactor nuclear fleet, adapting plants to climate impacts; and GE Vernova’s development of mature light-water reactor technology, with a new plant under construction in Ontario.

Carbon utilization offers another path to mitigating carbon dioxide (CO2) emissions. “The land and ocean are passively taking up almost 50 percent of anthropogenic CO2 emissions,” said Carolyn Ruppel, MITEI’s deputy director for science and technology, who moderated the panel. “The idea is to find uses for some of the rest of it.” The session showcased research exploring a host of technologies for making products with captured CO2.

Nicola Ferralis, a research scientist in the MIT Department of Materials Science and Engineering (DMSE), highlighted the importance of large-scale uses for carbon waste, such as structural carbon for vehicles and buildings. “We need to find applications that are actually able to match the supply by mass,” he said. 

Admir Masic, an associate professor in the MIT Department of Civil and Environmental Engineering and DMSE, described self-heating concrete panels used in real-world conditions in Sapporo, Japan, as well as new concrete materials that can store energy.

“The pace of the transition to a renewable-energy economy will strongly rely on the availability of scalable, bulk energy-storage solutions,” said Masic.

Kripa K. Varanasi, a professor in the MIT Department of Mechanical Engineering, is working on ways to make technologies such as emissions-plume capture at power plants economically viable. 

In a panel devoted to startups with links to MITEI or MITEI member companies, speakers described moving novel energy technologies from research to commercialization: Alfredo Rivela of Adaion has developed an AI tool for electrical utilities that optimizes their use of the grid; Eddie Obropta of Raptor Maps trains drones to inspect solar farms; Michele Guarnieri’s HiBot has created a robot that detects issues in power lines; and Michael Wang of Lithios is conducting field pilots that use electrochemistry to separate lithium from low-grade brines.

Successful ventures, the speakers agreed, begin with a clear customer problem that can be validated by industry users and that offers a path to scaled deployment. For industry users, “innovation is not a corporate virtue,” said Steve Hellman, managing partner at Alliance Partners. “It’s an existential requirement.”

Early-stage research and basic science

The conference also offered a bounty of promising early research on technologies and approaches for cutting carbon emissions. A panel on MITEI’s Seed Innovation Fund Program projects introduced MIT scientists who are separating and recycling rare earth elements; designing algorithms to decide which power plants should run to meet electricity demand; and modeling the impacts on human and natural systems of development and decarbonization.

A poster session revealed the next generation of energy researchers in such fields as chemistry, architecture, aeronautics and astronautics, and computer science. Graduate students are developing a wide array of technologies for carbon mitigation and new energy systems, such as a steam electrolysis process for hydrogen production; models of end-to-end decarbonization in buildings; and designs for hydrogen- and methane-fueled aircraft.

The conference highlighted the importance of fundamental science in feeding the pipeline of new energy technologies. Wednesday’s keynote speaker, Danna Freedman, the F. G. Keyes Professor of Chemistry at MIT and inaugural director of Quantum@MIT, traced scientists’ evolving capacity to harness quantum computing as a tool for solving a range of problems, including those in energy.

“What we want to do at Q-MIT is solve the hard problem, and the hard problem isn’t developing these ridiculously intricate and beautiful quantum tools,” said Freedman. “It is connecting the quantum tools with the applications that will change the world.”

A companion panel explored one such application: quantum sensing, which leverages fundamental physics to capture a nanoscale view of materials and their properties. For instance, Lukasz Zielinski, scientific advisor to the Integrated Sensing and Measurement Program at SLB, uses a lidar camera with single-photon detectors to identify methane plumes. Moderator Paola Cappellaro, the Ford Professor of Engineering in the MIT Department of Nuclear Science and Engineering, described her work using diamond-based sensors for detecting tiny magnetic fields from battery current, potentially helping measure battery conditions without disturbing the battery electrically. Such quantum sensors, Cappellaro said, can be “practical and impactful.” 

Data centers and public trust

One critical challenge facing energy researchers and industry emerged in sessions throughout the two days: the explosive growth of AI and the energy-hungry data centers that fuel this technology. As Deepjyoti Deka, a MITEI principal research scientist, put it, “the big test for AI right now is whether AI can reduce energy usage, improve efficiency, as well as bring in innovation faster than the rate at which it is going to consume electricity to get trained and deployed.” 

“New ‘hyperscale’ facilities built for artificial intelligence can use more power than entire cities,” said Steve Clemmer, director of energy research in the Climate and Energy Program at the Union of Concerned Scientists, sharing a recent analysis of data center proliferation. With new facilities being 10 to even 10,000 times larger than data centers built just a decade ago, he said, wholesale electricity costs will rise, and “increased reliance on fossil fuels will result in tens of billions of dollars in health damages, and trillions in climate damages.” He noted that decision-makers can mitigate these harmful impacts through clean energy development, strong ratepayer protections, and other safeguards.

Given mounting opposition to data centers, Kathryn Kline Tyndall, associate director of energy at the Bipartisan Policy Center, recommended that industry proponents provide early, accessible information about power, water, quality-of-life, land-use, environmental, and economic impacts, tailored to each community in which they hope to set up shop.

Larry Susskind, the Ford Professor of Urban and Environmental Planning in the MIT Department of Urban Studies and Planning, recommended that developers go further, and demonstrate credible benefits to communities. To achieve this, Susskind suggests a facilitated negotiation process between developers and local groups whose object is a community benefits agreement — a deal that might involve altering the data center design, and providing workforce development, infrastructure investment, and clean energy partnerships.

Ultimately, developers must accept that they can’t rush community acceptance; they have to earn it by giving residents a meaningful role in shaping projects and sharing their benefits. 

“You’re going to have to proceed at the speed of trust,” said Susskind.

Creating better-performing materials comes down to having more control over their structure at the smallest possible scale. One particularly effective way to accomplish that is using nanoparticles, which have highly customizable shapes and sizes and have already shown promise in applications across energy, medicine, and electronics.

But the process of turning nanoparticles into useful new materials has been hampered by the difficulty of controlling the number and direction of the links between each particle and its neighbors. Researchers have tried changing the shape of particles or precisely aligning the rigid connector chains between them, but both of those methods limit the material structures nanoparticles are able to form and are difficult to scale.

A team including researchers from MIT is taking a different approach. The researchers developed a tunable way to use super-flexible connector molecules that regulate the packing of nanoparticle networks by intentionally deforming based on their surrounding environment. In a paper published today in Nature Materials, the researchers showed how connector flexibility and electric charges can be used to tune the network’s structure, and they used the technique to create new kinds of nanoparticle-based materials.

The approach could offer a more versatile way to create high-performing materials for photonics, chemical catalysis, medicine, agriculture, and more.

“People are interested in nanoparticle superlattices because of the promise of building designer materials where you have more say in what types of structures they can actually build into,” says Rob Macfarlane, the Richard P. Simmons (1953) Professor in Metallurgy in MIT’s Department of Materials Science and Engineering. “This is a new approach in nanoparticle assembly that hasn’t been investigated before. It’s exciting to have a new tool that is so counterintuitive. It’s the exact opposite of what the dogma has been for 20 years: that you need to make connector ligands rigid, and any deviation causes assembly to fail. We showed if you just crank it all the way to the opposite extreme, it works just as well.”

Joining Macfarlane on the paper are MIT PhD candidate Nicholas Sbalbi; University of Delaware doctoral student Stephen Kronenberger; MIT undergraduate Hana Shinzawa; Matthew Ye PhD ’26; and former University of Delaware professor Arthi Jayaraman.

More flexible structures

A foundational truth in materials science is that a material’s structure governs its function.

“That means if you know what your building blocks are and how they’re arranged, you can explain what properties materials have,” Macfarlane says. “Controlling that is how you make materials that have new properties.”

Nanoparticles give scientists a flexible building block to make materials with desired properties, and individual nanoparticles are already used in applications as diverse as drug delivery, energy storage, computing, and sensing. But assembling them into larger structures has proven to be a challenge because of how tightly they tend to pack together.

To overcome that challenge, researchers make nanoparticles different shapes, which changes how they connect. They can also make only specific parts of the particles’ surfaces sticky so they form different patterns.

“The challenge with both of those approaches is they’re difficult to do synthetically as we make the particles,” Macfarlane explains. “The methods for defining stickiness that have been developed are always bespoke: They work for one material, at one size scale, in one condition. It’s good for studying, but you can’t do much with it.”

Both methods also require connector molecules, called ligands, at the surface of the nanoparticles to be rigid so they stay at the specific angles needed to maintain the formation researchers want, which further limits their functionality.

For their new study, the researchers wanted to turn that approach on its head.

“We thought, ‘Let’s do the exact opposite: make the bonds so flexible it doesn’t matter what direction they’re initially pointing.’ The ligands can then reorganize as the particles come together, and that reorganization itself limits how many neighbors each particle can bind,” Macfarlane says.

Macfarlane’s group has spent years creating ways to make different kinds of particles from grafts of polymers. The nanoparticles in their new system have flexible ligands with electrically charged tips, some positively charged and others negatively charged. The opposites attract, so the ligands bend to connect with opposite charges.

“The idea is, instead of defining a specific direction the bonds are going to occur in with complicated synthesis methods, we just said, ‘The bonds are going to be as flexible as they want to be, and they’ll spontaneously go to whatever valency is going to maximize bond stability,’” Macfarlane says.

In experiments led by Sbalbi, the researchers showed they could control the connections by changing the nanoparticle sizes and ligand chain lengths. They also used the technique to assemble the nanoparticles into previously unseen crystal structures.

“Without having to predesign where the particles connect or what their shapes are, we can control the number of bonds each particle has,” Macfarlane explains. “The amount of flexibility of the polymer dictates how many bonds it has. It allows us to lower the number of bonds each particle has and make lattices we have never seen before.”

A new path toward customizable materials

Macfarlane believes the system is a step toward getting custom nanoparticle-based materials out of the research lab and into the world.

“We in the nanoparticle assembly community talk about having completely independent control over what your particle is and what assembly it goes into,” Macfarlane says. “In theory, that should be the case. In reality, it absolutely has not been.”

More broadly, the researchers say their approach establishes ligand flexibility as a new path to making material structures that can be customized at scale.

“Nanoparticle superlattices offer a way to create designer materials where your building blocks have all sort sorts of unique properties,” Macfarlane says. “But just densely packing these nanoparticles together limits your applications. This offers a way to get less dense formations of nanoparticles, and that’s where a lot of the interesting applications are.”

The work was supported, in part, by the National Science Foundation, the Department of Energy, the U.S. Army and the U.S. Navy.

Regina Barzilay, MIT’s School of Engineering Distinguished Professor of AI and Health, and James Collins, MIT’s Termeer Professor of Medical Engineering and Science, are the recipients of the 2026 Pritzker Prize for AI in Science Research Excellence. 

The award recognizes their joint work in “pioneering AI-enabled antibiotic discovery, integrating deep learning, bioengineering, and experimental validation to identify novel antibacterial candidates against drug-resistant pathogens.” 

Established in 2024 by the University of Chicago in partnership with Caltech, the Pritzker Prize for AI in Science Research Excellence recognizes contributions that jointly advance AI and the natural sciences or engineering.

Barzilay and Collins, who are faculty co-leads of the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) and affiliates of the MIT Schwarzman College of Computing and the MIT School of Engineering, published their first breakthrough paper together in 2020 with then-MIT postdoc and first author Jonathan Stokes (who worked in the Collins lab from 2017 to 2021) in Cell. The paper described the first artificial intelligence-discovered antibiotic, called halicin, named for the fictional AI system HAL 9000 in Stanley Kubrick’s 1968 film 2001: A Space Odyssey.

A broad-spectrum antibiotic that was originally studied as a potential diabetes drug, halicin was identified by a deep learning model trained on 2,500 molecules. Upon testing, the research team found that halicin was able to kill antibiotic-resistant bacteria (i.e., “superbugs”) such as Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis. A discovery process that once took years was completed in just a few weeks. Amidst the growing alarm around the global antimicrobial resistance crisis, the results demonstrated that AI could prove to be an effective tool for rapidly identifying new and effective antibiotics.

Following halicin, Barzilay and Collins worked with Stokes (now an assistant professor at McMaster University) to use machine learning to identify abaucin in 2023, a narrow-spectrum antibiotic demonstrating effectiveness against A. baumannii, an antibiotic-resistant bacteria commonly found on hospital doorknobs and in the wounds of U.S. soldiers stationed in Iraq and Afghanistan. Notably, abaucin’s “narrow spectrum” efficacy against a single species of treatment-resistant bacteria is its main advantage, minimizing the likelihood of bacteria developing resistance and harming beneficial bacteria in the gut.

“Historically, antibiotic discovery has depended on laborious, trial-and-error strategies,” says Collins. “This process is slow, costly, and unpredictable, often yielding few viable antibiotic candidates. With generative AI, we're now seeing a shift from AI being used as a tool for discovery to AI being used as a design tool. This shift opens new frontiers in antibiotic discovery that could bring us the next generation of lifesaving drugs.”

In Barzilay’s view, “despite significant progress in molecular modeling, we are still in early stages of drug discovery. With AI, we can only automate parts of the pipeline, rather than totally transforming the discovery process. To make it happen, we need to work really hard to build realistic AI models of human biology.”