Superconductors are like the express trains in a metro system. Any electricity that “boards” a superconducting material can zip through it without stopping and losing energy along the way. As such, superconductors are extremely energy efficient, and are used today to power a variety of applications, from MRI machines to particle accelerators.

But these “conventional” superconductors are somewhat limited in terms of uses because they must be brought down to ultra-low temperatures using elaborate cooling systems to keep them in their superconducting state. If superconductors could work at higher, room-like temperatures, they would enable a new world of technologies, from zero-energy-loss power cables and electricity grids to practical quantum computing systems. And so scientists at MIT and elsewhere are studying “unconventional” superconductors — materials that exhibit superconductivity in ways that are different from, and potentially more promising than, today’s superconductors.

In a promising breakthrough, MIT physicists have today reported their observation of new key evidence of unconventional superconductivity in “magic-angle” twisted tri-layer graphene (MATTG) — a material that is made by stacking three atomically-thin sheets of graphene at a specific angle, or twist, that then allows exotic properties to emerge.

MATTG has shown indirect hints of unconventional superconductivity and other strange electronic behavior in the past. The new discovery, reported in the journal Science, offers the most direct confirmation yet that the material exhibits unconventional superconductivity.

In particular, the team was able to measure MATTG’s superconducting gap — a property that describes how resilient a material’s superconducting state is at given temperatures. They found that MATTG’s superconducting gap looks very different from that of the typical superconductor, meaning that the mechanism by which the material becomes superconductive must also be different, and unconventional.

“There are many different mechanisms that can lead to superconductivity in materials,” says study co-lead author Shuwen Sun, a graduate student in MIT’s Department of Physics. “The superconducting gap gives us a clue to what kind of mechanism can lead to things like room-temperature superconductors that will eventually benefit human society.”

The researchers made their discovery using a new experimental platform that allows them to essentially “watch” the superconducting gap, as the superconductivity emerges in two-dimensional materials, in real-time. They plan to apply the platform to further probe MATTG, and to map the superconducting gap in other 2D materials — an effort that could reveal promising candidates for future technologies.

“Understanding one unconventional superconductor very well may trigger our understanding of the rest,” says Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT and the senior author of the study. “This understanding may guide the design of superconductors that work at room temperature, for example, which is sort of the Holy Grail of the entire field.”

The study’s other co-lead author is Jeong Min Park PhD ’24; Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan are also co-authors.

The ties that bind

Graphene is a material that comprises a single layer of carbon atoms that are linked in a hexagonal pattern resembling chicken wire. A sheet of graphene can be isolated by carefully exfoliating an atom-thin flake from a block of graphite (the same stuff of pencil lead). In the 2010s, theorists predicted that if two graphene layers were stacked at a very special angle, the resulting structure should be capable of exotic electronic behavior.

In 2018, Jarillo-Herrero and his colleagues became the first to produce magic-angle graphene in experiments, and to observe some of its extraordinary properties. That discovery sprouted an entire new field known as “twistronics,” and the study of atomically thin, precisely twisted materials. Jarillo-Herrero’s group has since studied other configurations of magic-angle graphene with two, three, and more layers, as well as stacked and twisted structures of other two-dimensional materials. Their work, along with other groups, have revealed some signatures of unconventional superconductivity in some structures.

Superconductivity is a state that a material can exhibit under certain conditions (usually at very low temperatures). When a material is a superconductor, any electrons that pass through can pair up, rather than repelling and scattering away. When they couple up in what is known as “Cooper pairs,” the electrons can glide through a material without friction, instead of knocking against each other and flying away as lost energy. This pairing up of electrons is what enables superconductivity, though the way in which they are bound can vary.

“In conventional superconductors, the electrons in these pairs are very far away from each other, and weakly bound,” says Park. “But in magic-angle graphene, we could already see signatures that these pairs are very tightly bound, almost like a molecule. There were hints that there is something very different about this material.”

Tunneling through

In their new study, Jarillo-Herrero and his colleagues aimed to directly observe and confirm unconventional superconductivity in a magic-angle graphene structure. To do so, they would have to measure the material’s superconducting gap.

“When a material becomes superconducting, electrons move together as pairs rather than individually, and there’s an energy ‘gap’ that reflects how they’re bound,” Park explains. “The shape and symmetry of that gap tells us the underlying nature of the superconductivity.”

Scientists have measured the superconducting gap in materials using specialized techniques, such as tunneling spectroscopy. The technique takes advantage of a quantum mechanical property known as “tunneling.” At the quantum scale, an electron behaves not just as a particle, but also as a wave; as such, its wave-like properties enable an electron to travel, or “tunnel,” through a material, as if it could move through walls.

Such tunneling spectroscopy measurements can give an idea of how easy it is for an electron to tunnel into a material, and in some sense, how tightly packed and bound the electrons in the material are. When performed in a superconducting state, it can reflect the properties of the superconducting gap. However, tunneling spectroscopy alone cannot always tell whether the material is, in fact, in a superconducting state. Directly linking a tunneling signal to a genuine superconducting gap is both essential and experimentally challenging.

In their new work, Park and her colleagues developed an experimental platform that combines electron tunneling with electrical transport — a technique that is used to gauge a material’s superconductivity, by sending current through and continuously measuring its electrical resistance (zero resistance signals that a material is in a superconducting state).

The team applied the new platform to measure the superconducting gap in MATTG. By combining tunneling and transport measurements in the same device, they could unambiguously identify the superconducting tunneling gap, one that appeared only when the material exhibited zero electrical resistance, which is the hallmark of superconductivity. They then tracked how this gap evolved under varying temperature and magnetic fields. Remarkably, the gap displayed a distinct V-shaped profile, which was clearly different from the flat and uniform shape of conventional superconductors.

This V shape reflects a certain unconventional mechanism by which electrons in MATTG pair up to superconduct. Exactly what that mechanism is remains unknown. But the fact that the shape of the superconducting gap in MATTG stands out from that of the typical superconductor provides key evidence that the material is an unconventional superconductor.

In conventional superconductors, electrons pair up through vibrations of the surrounding atomic lattice, which effectively jostle the particles together. But Park suspects that a different mechanism could be at work in MATTG.

“In this magic-angle graphene system, there are theories explaining that the pairing likely arises from strong electronic interactions rather than lattice vibrations,” she posits. “That means electrons themselves help each other pair up, forming a superconducting state with special symmetry.”

Going forward, the team will test other two-dimensional twisted structures and materials using the new experimental platform.

“This allows us to both identify and study the underlying electronic structures of superconductivity and other quantum phases as they happen, within the same sample,” Park says. “This direct view can reveal how electrons pair and compete with other states, paving the way to design and control new superconductors and quantum materials that could one day power more efficient technologies or quantum computers.”

This research was supported, in part, by the U.S. Army Research Office, the U.S. Air Force Office of Scientific Research, the MIT/MTL Samsung Semiconductor Research Fund, the Sagol WIS-MIT Bridge Program, the National Science Foundation, the Gordon and Betty Moore Foundation, and the Ramon Areces Foundation.

Traditional folk ballads are one of our most enduring forms of cultural expression. They can also be lost to society, forgotten over time. That’s why, in the mid-1700s, when a Scottish woman named Anna Gordon was found to know three dozen ancient ballads, collectors tried to document all of these songs — a volume of work that became a kind of sensation in its time, a celebrated piece of cultural heritage.

That story is told in MIT Professor Emerita Ruth Perry’s latest book, “The Ballad World of Anna Gordon, Mrs. Brown of Falkland,” published this year by Oxford University Press. In it, Perry details what we know about the ways folk ballads were created and transmitted; how Anna Gordon came to know so many; the social and political climate in which they existed; and why these songs meant so much in Scotland and elsewhere in the Atlantic world. Indeed, Scottish immigrants brought their music to the U.S., among other places.

MIT News sat down with Perry, who is MIT’s Ann Fetter Friedlaender Professor of Humanities, Emerita, to talk about the book.

Q: This is fascinating topic with a lot of threads woven together. To you, what is the book about?

A: It’s really three books. It’s a book about Anna Gordon and her family, a very interesting middle-class family living in Aberdeen in the middle of the 18th century. And it’s a book about balladry and what a ballad is — a story told in song, and ballads are the oldest known poetry in English. Some of them are gorgeous. Third, it’s a book about the relationship between Scotland and England, the effects of the Jacobite uprising in 1745, social attitudes, how people lived, what they ate, education — it’s very much about 18th century Scotland.

Q: Okay, who was Anna Gordon, and what was her family milieu?

A: Anna’s father, Thomas Gordon, was a professor at King’s College, now the University of Aberdeen. He was a professor of humanity, which in those days meant Greek and Latin, and was well-connected to the intellectual community of the Scottish Enlightenment. A friend of his, an Edinburgh writer, lawyer, and judge, William Tytler, who heard cases all over the country and always stayed with Thomas Gordon and his family when he came to Aberdeen, was intensely interested in Scottish traditional music. He found out that Anna Gordon had learned all these ballads as a child, from her mother and aunt and some servants. Tytler asked if she would write them down, both tunes and words.

That was the earliest manuscript of ballads ever collected from a named person in Scotland. Once it was in existence, all kinds of people wanted to see it; it got spread throughout the country. In my book, I detail much of the excitement over this manuscript.

The thing about Anna’s ballads is: It’s not just that there are more of them, and more complete versions that are fuller, with more verses. They’re more beautiful. The language is more archaic, and there are marvelous touches. It is thought, and I agree, that Anna Gordon was an oral poet. As she remembered ballads and reproduced them, she improved on them. She had a great memory for the best bits and would improve other parts.

Q: How did it come about that at this time, a woman such as Anna Gordon would be the keeper and creator of cultural knowledge?

A: Women were more literate in Scotland than elsewhere. The Scottish Parliament passed an act in 1695 requiring every parish in the Church of Scotland to have not only a minister, but a teacher. Scotland was the most literate country in Europe in the 18th century. And those parish schoolmasters taught local kids. The parents did have to pay a few pennies for their classes, and, true, more parents paid for sons than for daughters. But there were daughters who took classes. And there were no opportunities like this in England at the time. Education was better for women in Scotland. So was their legal position, under common law in Scotland. When the Act of Union was formed in 1707, Scotland retained its own legal system, which had more extensive rights for women than in England.

Q: I know it’s complex, but generally, why was this?

A: Scotland was a much more democratic country, culture, and society than England, period. When Elizabeth I died in 1603, the person who inherited the throne was the King of Scotland James VI, who went to England with his court — which included the Scottish aristocracy. So, the Scottish aristocracy ended up in London. I’m sure they went back to their hunting lodges for the hunting season, but they didn’t live there [in Scotland] and they didn’t set the tone of the country. It was democratized because all that was left were a lot of lawyers and ministers and teachers.

Q: What is distinctive about the ballads in this corpus of songs Anna Gordon knew and documented?

A: A common word about ballads is that there’s a high body count, and they’re all about people dying and killing each other. But that is not true of Anna Gordon’s ballads. They’re about younger women triumphing in the world, often against older women, which is interesting, and even more often against fathers. The ballads are about family discord, inheritance, love, fidelity, lack of fidelity, betrayal. There are ballads about fighting and bloodshed, but not so many. They’re about the human condition. And they have interesting qualities because they’re oral poetry, composed and remembered and changed and transmitted from mouth to ear and not written down. There are repetitions and parallelisms, and other hallmarks of oral poetry. The sort of thing you learned when you read Homer.

Q: So is this a form of culture generated in opposition to those controlling society? Or at least, one that’s popular regardless of what some elites thought?

A: It is in Scotland, because of the enmity between Scotland and England. We’re talking about the period of Great Britain when England is trying to gobble up Scotland and some Scottish folks don’t want that. They want to retain their Scottishness. And the ballad was a Scottish tradition that was not influenced by England. That’s one reason balladry was so important in 18th-century Scotland. Everybody was into balladry partly because it was a unique part of Scottish culture.

Q: To that point, it seems like an unexpected convergence, for the time, to see a more middle-class woman like Anna Gordon transmitting ballads that had often been created and sung by people of all classes.

A: Yes. At first I thought I was just working on a biography of Anna Gordon. But it’s fascinating how the culture was transmitted, how intellectually rich that society was, how much there is to examine in Scottish culture and society of the 18th century. Today people may watch “Outlander,” but they still wouldn’t know anything about this!

A new 3D human brain tissue platform developed by MIT researchers is the first to integrate all major brain cell types, including neurons, glial cells, and the vasculature, into a single culture. 

Grown from individual donors’ induced pluripotent stem cells, these models — dubbed Multicellular Integrated Brains (miBrains) — replicate key features and functions of human brain tissue, are readily customizable through gene editing, and can be produced in quantities that support large-scale research.

Although each unit is smaller than a dime, miBrains may be worth a great deal to researchers and drug developers who need more complex living lab models to better understand brain biology and treat diseases.

“The miBrain is the only in vitro system that contains all six major cell types that are present in the human brain,” says Li-Huei Tsai, Picower Professor, director of The Picower Institute for Learning and Memory, and a senior author of the open-access study describing miBrains, published Oct. 17 in the Proceedings of the National Academy of Sciences.

“In their first application, miBrains enabled us to discover how one of the most common genetic markers for Alzheimer’s disease alters cells’ interactions to produce pathology,” she adds.

Tsai’s co-senior authors are Robert Langer, David H. Koch (1962) Institute Professor, and Joel Blanchard, associate professor in the Icahn School of Medicine at Mt. Sinai in New York, and a former Tsai Laboratory postdoc. The study is led by Alice Stanton, former postdoc in the Langer and Tsai labs and now assistant professor at Harvard Medical School and Massachusetts General Hospital, and Adele Bubnys, a former Tsai lab postdoc and current senior scientist at Arbor Biotechnologies.

Benefits from two kinds of models

The more closely a model recapitulates the brain’s complexity, the better suited it is for extrapolating how human biology works and how potential therapies may affect patients. In the brain, neurons interact with each other and with various helper cells, all of which are arranged in a three-dimensional tissue environment that includes blood vessels and other components. All of these interactions are necessary for health, and any of them can contribute to disease.

Simple cultures of just one or a few cell types can be created in quantity relatively easily and quickly, but they cannot tell researchers about the myriad interactions that are essential to understanding health or disease. Animal models embody the brain’s complexity, but can be difficult and expensive to maintain, slow to yield results, and different enough from humans to yield occasionally divergent results.

MiBrains combine advantages from each type of model, retaining much of the accessibility and speed of lab-cultured cell lines while allowing researchers to obtain results that more closely reflect the complex biology of human brain tissue. Moreover, they are derived from individual patients, making them personalized to an individual’s genome. In the model, the six cell types self-assemble into functioning units, including blood vessels, immune defenses, and nerve signal conduction, among other features. Researchers ensured that miBrains also possess a blood-brain-barrier capable of gatekeeping which substances may enter the brain, including most traditional drugs.

“The miBrain is very exciting as a scientific achievement,” says Langer. “Recent trends toward minimizing the use of animal models in drug development could make systems like this one increasingly important tools for discovering and developing new human drug targets.”

Two ideal blends for functional brain models

Designing a model integrating so many cell types presented challenges that required many years to overcome. Among the most crucial was identifying a substrate able to provide physical structure for cells and support their viability. The research team drew inspiration from the environment that surrounds cells in natural tissue, the extracellular matrix (ECM). The miBrain’s hydrogel-based “neuromatrix” mimics the brain’s ECM with a custom blend of polysaccharides, proteoglycans, and basement membrane that provide a scaffold for all the brain’s major cell types while promoting the development of functional neurons.

A second blend would also prove critical: the proportion of cells that would result in functional neurovascular units. The actual ratios of cell types have been a matter of debate for the last several decades, with even the more advanced methodologies providing only rough brushstrokes for guidance, for example 45-75 percent for oligodendroglia of all cells or 19-40 percent for astrocytes.

The researchers developed the six cell types from patient-donated induced pluripotent stem cells, verifying that each cultured cell type closely recreated naturally-occurring brain cells. Then, the team experimentally iterated until they hit on a balance of cell types that resulted in functional, properly structured neurovascular units. This laborious process would turn out to be an advantageous feature of miBrains: because cell types are cultured separately, they can each be genetically edited so that the resulting model is tailored to replicate specific health and disease states.

“Its highly modular design sets the miBrain apart, offering precise control over cellular inputs, genetic backgrounds, and sensors — useful features for applications such as disease modeling and drug testing,” says Stanton.

Alzheimer’s discovery using miBrain

To test miBrain’s capabilities, the researchers embarked on a study of the gene variant APOE4, which is the strongest genetic predictor for the development of Alzheimer’s disease. Although one brain cell type, astrocytes, are known to be a primary producer of the APOE protein, the role that astrocytes carrying the APOE4 variant play in disease pathology is poorly understood.

MiBrains were well-suited to the task for two reasons. First of all, they integrate astrocytes with the brain’s other cell types, so that their natural interactions with other cells can be mimicked. Second, because the platform allowed the team to integrate cell types individually, APOE4 astrocytes could be studied in cultures where all other cell types carried APOE3, a gene variant that does not increase Alzheimer’s risk. This enabled the researchers to isolate the contribution APOE4 astrocytes make to pathology.

In one experiment, the researchers examined APOE4 astrocytes cultured alone, versus ones in APOE4 miBrains. They found that only in the miBrains did the astrocytes express many measures of immune reactivity associated with Alzheimer’s disease, suggesting the multicellular environment contributes to that state.

The researchers also tracked the Alzheimer’s-associated proteins amyloid and phosphorylated tau, and found all-APOE4 miBrains accumulated them, whereas all-APOE3 miBrains did not, as expected. However, in APOE3 miBrains with APOE4 astrocytes, they found that APOE4 miBrains still exhibited amyloid and tau accumulation.

Then the team dug deeper into how APOE4 astrocytes’ interactions with other cell types might lead to their contribution to disease pathology. Prior studies have implicated molecular cross-talk with the brain’s microglia immune cells. Notably, when the researchers cultured APOE4 miBrains without microglia, their production of phosphorylated tau was significantly reduced. When the researchers dosed APOE4 miBrains with culture media from astrocytes and microglia combined, phosphorylated tau increased, whereas when they dosed them with media from cultures of astrocytes or microglia alone, the tau production did not increase. The results therefore provided new evidence that molecular cross-talk between microglia and astrocytes is indeed required for phosphorylated tau pathology.

In the future, the research team plans to add new features to miBrains to more closely model characteristics of working brains, such as leveraging microfluidics to add flow through blood vessels, or single-cell RNA sequencing methods to improve profiling of neurons.

Researchers expect that miBrains could advance research discoveries and treatment modalities for Alzheimer’s disease and beyond. 

“Given its sophistication and modularity, there are limitless future directions,” says Stanton. “Among them, we would like to harness it to gain new insights into disease targets, advanced readouts of therapeutic efficacy, and optimization of drug delivery vehicles.”

“I’m most excited by the possibility to create individualized miBrains for different individuals,” adds Tsai. “This promises to pave the way for developing personalized medicine.”

Funding for the study came from the BT Charitable Foundation, Freedom Together Foundation, the Robert A. and Renee E. Belfer Family, Lester A. Gimpelson, Eduardo Eurnekian, Kathleen and Miguel Octavio, David B. Emmes, the Halis Family, the Picower Institute, and an anonymous donor.

A large-scale screen of tuberculosis proteins has revealed several possible antigens that could be developed as a new vaccine for TB, the world’s deadliest infectious disease.

In the new study, a team of MIT biological engineers was able to identify a handful of immunogenic peptides, out of more than 4,000 bacterial proteins, that appear to stimulate a strong response from a type of T cells responsible for orchestrating immune cells’ response to infection.

There is currently only one vaccine for tuberculosis, known as BCG, which is a weakened version of a bacterium that causes TB in cows. This vaccine is widely administered in some parts of the world, but it poorly protects adults against pulmonary TB. Worldwide, tuberculosis kills more than 1 million people every year.

“There’s still a huge TB burden globally that we’d like to make an impact on,” says Bryan Bryson, an associate professor of biological engineering at MIT and a member of the Ragon Institute of Mass General Brigham, MIT, and Harvard. “What we’ve tried to do in this initial TB vaccine is focus on antigens that we saw frequently in our screen and also appear to stimulate a response in T cells from people with prior TB infection.”

Bryson and Forest White, the Ned C. and Janet C. Rice Professor of Biological Engineering at MIT, and a member of the Koch Institute for Integrative Cancer Research, are the senior authors of the study, which appears today in Science Translational Medicine. Owen Leddy PhD ’25 is the paper’s lead author.

Identifying vaccine targets

Since the BCG vaccine was developed more than 100 years ago, no other TB vaccines have been approved for use. Mycobacterium tuberculosis produces more than 4,000 proteins, which makes it a daunting challenge to pick out proteins that might elicit a strong immune response if used as a vaccine.

In the new study, Bryson and his students set out to narrow the field of candidates by identifying TB proteins presented on the surface of infected human cells. When an immune cell such as a phagocyte is infected with Mycobacterium tuberculosis, some of the bacterial proteins get chopped into fragments called peptides, which are then displayed on the surface of the cell by MHC proteins. These MHC-peptide complexes act as a signal that can activate T cells.

MHCs, or major histocompatibility complexes, come in two types known as class I and class II. Class I MHCs activate killer T cells, while class II MHCs stimulate helper T cells. In human cells, there are three genes that can encode MHC-II proteins, and each of these comes in hundreds of variants. This means that any two people can have a very different repertoire of MHC-II molecules, which present different antigens.

“Instead of looking at all of those 4,000 TB proteins, we wanted to ask which of those proteins from TB actually end up being displayed to the rest of the immune system via MHC,” Bryson says. “If we could just answer that question, then we could design vaccines to match that.”

To try to answer the question, the researchers infected human phagocytes with Mycobacterium tuberculosis. After three days, they extracted MHC-peptide complexes from the cell surfaces, then identified the peptides using mass spectrometry.

Focusing on peptides bound to MHC-II, the researchers found 27 TB peptides, from 13 proteins, that appeared most often in the infected cells. Then, they further tested those peptides by exposing them to T cells donated by people who had previously been infected with TB.

They found that 24 of these peptides did elicit a T cell response in at least some of the samples. None of the proteins from which these peptides came worked for every single donor, but Bryson believes that a vaccine using a combination of these peptides would likely work for most people.

“In a perfect world, if you were trying to design a vaccine, you would pick one protein and that protein would be presented across every donor. It should work for every person,” Bryson says. “However, using our measurements, we’ve not yet found a TB protein that covers every donor we’ve analyzed thus far.”

Enter mRNA vaccines

Among the vaccine candidates that the researchers identified are several peptides from a class of proteins called type 7 secretion systems (T7SSs). Some of these peptides also turned up in an earlier study from Bryson’s lab on MHC-1.

“Type 7 secretion system substrates are a very small sliver of the overall TB proteome, but when you look at MHC class I or MHC class II, it seems as though the cells are preferentially presenting these,” Bryson says.

Two of the best-known of these proteins, EsxA and EsxB, are secreted by bacteria to help them escape from the membranes that phagocytes use to envelop them within the cell. Neither protein can break through the membrane on its own, but when joined together to form a heterodimer, they can poke holes, which also allow other T7SS proteins to escape.

To evaluate whether the proteins they identified could make a good vaccine, the researchers created mRNA vaccines encoding two protein sequences — EsxB and EsxG. The researchers designed several versions of the vaccine, which were targeted to different compartments within the cells.

The researchers then delivered this vaccine into human phagocytes, where they found that vaccines that targeted cell lysosomes — organelles that break down molecules — were the most effective. These vaccines induced 1,000 times more MHC presentation of TB peptides than any of the others.

They later found that the presentation was even higher if they added EsxA to the vaccine, because it allows the formation of the heterodimers that can poke through the lysosomal membrane.

The researchers currently have a mix of eight proteins that they believe could offer protection against TB for most people, but they are continuing to test the combination with blood samples from people around the world. They also hope to run additional studies to explore how much protection this vaccine offers in animal models. Tests in humans are likely several years away.

The research was funded by the MIT Center for Precision Cancer Research at the Koch Institute, the National Institutes of Health, the National Institute of Environmental Health Sciences, and the Frederick National Laboratory for Cancer Research.

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.

Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even the best models can only process a few images at a time. In a real-world disaster where every second counts, a search-and-rescue robot would need to quickly traverse large areas and process thousands of images to complete its mission.

To overcome this problem, MIT researchers drew on ideas from both recent artificial intelligence vision models and classical computer vision to develop a new system that can process an arbitrary number of images. Their system accurately generates 3D maps of complicated scenes like a crowded office corridor in a matter of seconds. 

The AI-driven system incrementally creates and aligns smaller submaps of the scene, which it stitches together to reconstruct a full 3D map while estimating the robot’s position in real-time.

Unlike many other approaches, their technique does not require calibrated cameras or an expert to tune a complex system implementation. The simpler nature of their approach, coupled with the speed and quality of the 3D reconstructions, would make it easier to scale up for real-world applications.

Beyond helping search-and-rescue robots navigate, this method could be used to make extended reality applications for wearable devices like VR headsets or enable industrial robots to quickly find and move goods inside a warehouse.

“For robots to accomplish increasingly complex tasks, they need much more complex map representations of the world around them. But at the same time, we don’t want to make it harder to implement these maps in practice. We’ve shown that it is possible to generate an accurate 3D reconstruction in a matter of seconds with a tool that works out of the box,” says Dominic Maggio, an MIT graduate student and lead author of a paper on this method.

Maggio is joined on the paper by postdoc Hyungtae Lim and senior author Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), and director of the MIT SPARK Laboratory. The research will be presented at the Conference on Neural Information Processing Systems.

Mapping out a solution

For years, researchers have been grappling with an essential element of robotic navigation called simultaneous localization and mapping (SLAM). In SLAM, a robot recreates a map of its environment while orienting itself within the space.

Traditional optimization methods for this task tend to fail in challenging scenes, or they require the robot’s onboard cameras to be calibrated beforehand. To avoid these pitfalls, researchers train machine-learning models to learn this task from data.

While they are simpler to implement, even the best models can only process about 60 camera images at a time, making them infeasible for applications where a robot needs to move quickly through a varied environment while processing thousands of images.

To solve this problem, the MIT researchers designed a system that generates smaller submaps of the scene instead of the entire map. Their method “glues” these submaps together into one overall 3D reconstruction. The model is still only processing a few images at a time, but the system can recreate larger scenes much faster by stitching smaller submaps together.

“This seemed like a very simple solution, but when I first tried it, I was surprised that it didn’t work that well,” Maggio says.

Searching for an explanation, he dug into computer vision research papers from the 1980s and 1990s. Through this analysis, Maggio realized that errors in the way the machine-learning models process images made aligning submaps a more complex problem.

Traditional methods align submaps by applying rotations and translations until they line up. But these new models can introduce some ambiguity into the submaps, which makes them harder to align. For instance, a 3D submap of a one side of a room might have walls that are slightly bent or stretched. Simply rotating and translating these deformed submaps to align them doesn’t work.

“We need to make sure all the submaps are deformed in a consistent way so we can align them well with each other,” Carlone explains.

A more flexible approach

Borrowing ideas from classical computer vision, the researchers developed a more flexible, mathematical technique that can represent all the deformations in these submaps. By applying mathematical transformations to each submap, this more flexible method can align them in a way that addresses the ambiguity.

Based on input images, the system outputs a 3D reconstruction of the scene and estimates of the camera locations, which the robot would use to localize itself in the space.

“Once Dominic had the intuition to bridge these two worlds — learning-based approaches and traditional optimization methods — the implementation was fairly straightforward,” Carlone says. “Coming up with something this effective and simple has potential for a lot of applications.

Their system performed faster with less reconstruction error than other methods, without requiring special cameras or additional tools to process data. The researchers generated close-to-real-time 3D reconstructions of complex scenes like the inside of the MIT Chapel using only short videos captured on a cell phone.

The average error in these 3D reconstructions was less than 5 centimeters.

In the future, the researchers want to make their method more reliable for especially complicated scenes and work toward implementing it on real robots in challenging settings.

“Knowing about traditional geometry pays off. If you understand deeply what is going on in the model, you can get much better results and make things much more scalable,” Carlone says.

This work is supported, in part, by the U.S. National Science Foundation, U.S. Office of Naval Research, and the National Research Foundation of Korea. Carlone, currently on sabbatical as an Amazon Scholar, completed this work before he joined Amazon.

What if clinicians could place tiny electronic chips in the brain that electrically stimulate a precise target, through a simple injection in the arm? This may someday help treat deadly or debilitating brain diseases, while eliminating surgery-related risks and costs.

MIT researchers have taken a major step toward making this scenario a reality. They developed microscopic, wireless bioelectronics that could travel through the body’s circulatory system and autonomously self-implant in a target region of the brain, where they would provide focused treatment.

In a study on mice, the researchers show that after injection, these miniscule implants can identify and travel to a specific brain region without the need for human guidance. Once there, they can be wirelessly powered to provide electrical stimulation to the precise area. Such stimulation, known as neuromodulation, has shown promise as a way to treat brain tumors and diseases like Alzheimer’s and multiple sclerosis.

Moreover, because the electronic devices are integrated with living, biological cells before being injected, they are not attacked by the body’s immune system and can cross the blood-brain barrier while leaving it intact. This maintains the barrier’s crucial protection of the brain.

The researchers demonstrated the use of this technology, which they call “circulatronics,” to target brain inflammation, a major factor in the progression of many neurological diseases. They show that the implants can provide localized neuromodulation deep inside the brain achieving high precision, to within several microns around the target area.

In addition, the biocompatible implants do not damage surrounding neurons.

While brain implants usually require hundreds of thousands of dollars in medical costs and risky surgical procedures, circulatronics technology holds the potential to make therapeutic brain implants accessible to all by eliminating the need for surgery, says Deblina Sarkar, the AT&T Career Development Associate Professor in the MIT Media Lab and MIT Center for Neurobiological Engineering, head of the Nano-Cybernetic Biotrek Lab, and senior author of a study on the work.

She is joined on the paper by lead author Shubham Yadav, an MIT graduate student; as well as others at MIT, Wellesley College, and Harvard University. The research appears today in Nature Biotechnology.

Hybrid implants

The team has been working on circulatronics for more than six years. The electronic devices, each about one-billionth the length of a grain of rice, are composed of organic semiconducting polymer layers sandwiched between metallic layers to create an electronic heterostructure.

They are fabricated using CMOS-compatible processes in the MIT.nano facilities, and then integrated with living cells to create cell-electronics hybrids. To do this, the researchers lift the devices off the silicon wafer on which they are fabricated, so they are free-floating in a solution.

“The electronics worked perfectly when they were attached to the substrate, but when we originally lifted them off, they didn’t work anymore. Solving that challenge took us more than a year,” Sarkar says.

Key to their operation is the high wireless power conversion efficiency of the tiny electronics. This enables the devices to work deep inside the brain and still harness enough energy for neuromodulation.

The researchers use a chemical reaction to bond the electronic devices to cells. In the new study, they fused the electronics with a type of immune cell called monocytes, which target areas of inflammation in the body. They also applied a fluorescent dye, allowing them to trace the devices as they crossed the intact blood-brain barrier and self-implanted in the target brain region.

While they explored brain inflammation in this study, the researchers hope to use different cell types and engineer the cells to target specific regions of the brain.

“Our cell-electronics hybrid fuses the versatility of electronics with the biological transport and biochemical sensing prowess of living cells,” Sarkar says. “The living cells camouflage the electronics so that they aren’t attacked by the body’s immune system and they can travel seamlessly through the bloodstream. This also enables them to squeeze through the intact blood-brain barrier without the need to invasively open it.”

Over the course of about four years, the team tried many methods to autonomously and noninvasively cross the blood-brain barrier before they perfected this cellular integration technique.

In addition, because the circulatronics devices are so tiny, they offer much higher precision than conventional electrodes. They can self-implant, leading to millions of microscopic stimulation sites that take the exact shape of the target region.

Their small size also enables the biocompatible devices to live alongside neurons without causing harmful effects. Through a series of biocompatibility tests, the researchers found that circulatronics can safely integrate among neurons without impacting the brain processes behind cognition or motion.

After the devices have self-implanted in the target region, a clinician or researcher uses an external transmitter to provide electromagnetic waves, in the form of near-infrared light, that power the technology and enable electrical stimulation of the neurons.

Targeting deadly diseases

The Sarkar lab is currently working on developing their technology to treat multiple diseases including brain cancer, Alzheimer’s disease, and chronic pain.

The tiny size and self-implantation capabilities of circulatronics devices could make them well-suited to treat brain cancers such as glioblastoma that cause tumors at multiple locations, some of which may be too small to identify with imaging techniques. They may also provide new avenues for treating especially deadly cancers like diffuse intrinsic pontine glioma, an aggressive type of tumor found in the brain stem that usually cannot be surgically removed.

“This is a platform technology and may be employed to treat multiple brain diseases and mental illnesses,” Sarkar says. “Also, this technology is not just confined to the brain but could also be extended to other parts of the body in future.”

The researchers hope to move the technology into clinical trials within three years through the recently launched startup Cahira Technologies.

They are also exploring integration of additional nanoelectronic circuits into their devices to enable functionalities including sensing, feedback based on-chip data analysis, and capabilities such as creating synthetic electronic neurons.

“Our tiny electronic devices seamlessly integrate with the neurons and co-live and co-exist with the brain cells creating a unique brain-computer symbiosis. We are working dedicatedly to employ this technology for treating neural diseases, where drugs or standard therapies fail, for alleviating human suffering and envision a future where humans could transcend beyond diseases and biological limitations,” says Sarkar.

One of the highest-risk components of nuclear waste is iodine-129 (I-129), which stays radioactive for millions of years and accumulates in human thyroids when ingested. In the U.S., nuclear waste containing I-129 is scheduled to be disposed of in deep underground repositories, which scientists say will sufficiently isolate it.

Meanwhile, across the globe, France routinely releases low-level radioactive effluents containing iodine-129 and other radionuclides into the ocean. France recycles its spent nuclear fuel, and the reprocessing plant discharges about 153 kilograms of iodine-129 each year, under the French regulatory limit.

Is dilution a good solution? What’s the best way to handle spent nuclear fuel? A new study by MIT researchers and their collaborators at national laboratories quantifies I-129 release under three different scenarios: the U.S. approach of disposing spent fuel directly in deep underground repositories, the French approach of dilution and release, and an approach that uses filters to capture I-129 and disposes of them in shallow underground waste repositories.

The researchers found France’s current practice of reprocessing releases about 90 percent of the waste’s I-129 into the biosphere. They found low levels of I-129 in ocean water around France and the U.K.’s former reprocessing sites, including the English Channel and North Sea. Although the low level of I-129 in the water in Europe is not considered to pose health risks, the U.S. approach of deep underground disposal leads to far less I-129 being released, the researchers found.

The researchers also investigated the effect of environmental regulations and technologies related to I-129 management, to illuminate the tradeoffs associated with different approaches around the world.

“Putting these pieces together to provide a comprehensive view of Iodine-129 is important,” says MIT Assistant Professor Haruko Wainwright, a first author on the paper who holds a joint appointment in the departments of Nuclear Science and Engineering and of Civil and Environmental Engineering. “There are scientists that spend their lives trying to clean up iodine-129 at contaminated sites. These scientists are sometimes shocked to learn some countries are releasing so much iodine-129. This work also provides a life-cycle perspective. We’re not just looking at final disposal and solid waste, but also when and where release is happening. It puts all the pieces together.”

MIT graduate student Kate Whiteaker SM ’24 led many of the analyses with Wainwright. Their co-authors are Hansell Gonzalez-Raymat, Miles Denham, Ian Pegg, Daniel Kaplan, Nikolla Qafoku, David Wilson, Shelly Wilson, and Carol Eddy-Dilek. The study appears today in Nature Sustainability.

Managing waste

Iodine-129 is often a key focus for scientists and engineers as they conduct safety assessments of nuclear waste disposal sites around the world. It has a half-life of 15.7 million years, high environmental mobility, and could potentially cause cancers if ingested. The U.S. sets a strict limit on how much I-129 can be released and how much I-129 can be in drinking water — 5.66 nanograms per liter, the lowest such level of any radionuclides.

“Iodine-129 is very mobile, so it is usually the highest-dose contributor in safety assessments,” Wainwright says.

For the study, the researchers calculated the release of I-129 across three different waste management strategies by combining data from current and former reprocessing sites as well as repository assessment models and simulations.

The authors defined the environmental impact as the release of I-129 into the biosphere that humans could be exposed to, as well as its concentrations in surface water. They measured I-129 release per the total electrical energy generated by a 1-gigawatt power plant over one year, denoted as kg/GWe.y.

Under the U.S. approach of deep underground disposal with barrier systems, assuming the barrier canisters fail at 1,000 years (a conservative estimate), the researchers found 2.14 x 10–8 kg/GWe.y of I-129 would be released between 1,000 and 1 million years from today.

They estimate that 4.51 kg/GWe.y of I-129, or 91 percent of the total, would be released into the biosphere in the scenario where fuel is reprocessed and the effluents are diluted and released. About 3.3 percent of I-129 is captured by gas filters, which are then disposed of in shallow subsurfaces as low-level radioactive waste. A further 5.2 percent remains in the waste stream of the reprocessing plant, which is then disposed of as high-level radioactive waste.

If the waste is recycled with gas filters to directly capture I-129, 0.05 kg/GWe.y of the I-129 is released, while 94 percent is disposed of in the low-level disposal sites. For shallow disposal, some kind of human disruption and intrusion is assumed to occur after government or institutional control expires (typically 100-1,000 years). That results in a potential release of the disposed amount to the environment after the control period.

Overall, the current practice of recycling spent nuclear fuel releases the majority of I-129 into the environment today, while the direct disposal of spent fuel releases around 1/100,000,000 that amount over 1 million years. When the gas filters are used to capture I-129, the majority of I-129 goes to shallow underground repositories, which could be accidentally released through human intrusion down the line.

The researchers also quantified the concentration of I-129 in different surface waters near current and former fuel reprocessing facilities, including the English Channel and the North Sea near reprocessing plants in France and U.K. They also analyzed the U.S. Columbia River downstream of a site in Washington state where material for nuclear weapons was produced during the Cold War, and they studied a similar site in South Carolina. The researchers found far higher concentrations of I-129 within the South Carolina site, where the low-level radioactive effluents were released far from major rivers and hence resulted in less dilution in the environment.

“We wanted to quantify the environmental factors and the impact of dilution, which in this case affected concentrations more than discharge amounts,” Wainwright says. “Someone might take our results to say dilution still works: It’s reducing the contaminant concentration and spreading it over a large area. On the other hand, in the U.S., imperfect disposal has led to locally higher surface water concentrations. This provides a cautionary tale that disposal could concentrate contaminants, and should be carefully designed to protect local communities.”

Fuel cycles and policy

Wainwright doesn’t want her findings to dissuade countries from recycling nuclear fuel. She says countries like Japan plan to use increased filtration to capture I-129 when they reprocess spent fuel. Filters with I-129 can be disposed of as low-level waste under U.S. regulations.

“Since I-129 is an internal carcinogen without strong penetrating radiation, shallow underground disposal would be appropriate in line with other hazardous waste,” Wainwright says. “The history of environmental protection since the 1960s is shifting from waste dumping and release to isolation. But there are still industries that release waste into the air and water. We have seen that they often end up causing issues in our daily life — such as CO2, mercury, PFAS and others — especially when there are many sources or when bioaccumulation happens. The nuclear community has been leading in waste isolation strategies and technologies since the 1950s. These efforts should be further enhanced and accelerated. But at the same time, if someone does not choose nuclear energy because of waste issues, it would encourage other industries with much lower environmental standards.”

The work was supported by MIT’s Climate Fast Forward Faculty Fund and the U.S. Department of Energy.

Although heart cells and skin cells contain identical instructions for creating proteins encoded in their DNA, they’re able to fill such disparate niches because molecular machinery can cut out and stitch together different segments of those instructions to create endlessly unique combinations.

The ingenuity of using the same genes in different ways is made possible by a process called splicing and is controlled by splicing factors; which splicing factors a cell employs determines what sets of instructions that cell produces, which, in turn, gives rise to proteins that allow cells to fulfill different functions. 

In an open-access paper published today in Nature Biotechnology, researchers in the MIT Department of Biology outlined a framework for parsing the complex relationship between sequences and splicing regulation to investigate the regulatory activities of splicing factors, creating models that can be applied to interpret and predict splicing regulation across different cell types, and even different species. Called Knockdown Activity and Target Models from Additive regression Predictions, KATMAP draws on experimental data from disrupting the expression of a splicing factor and information on which sequences the splicing factor interacts with to predict its likely targets. 

Aside from the benefits of a better understanding of gene regulation, splicing mutations — either in the gene that is spliced or in the splicing factor itself — can give rise to diseases such as cancer by altering how genes are expressed, leading to the creation or accumulation of faulty or mutated proteins. This information is critical for developing therapeutic treatments for those diseases. The researchers also demonstrated that KATMAP can potentially be used to predict whether synthetic nucleic acids, a promising treatment option for disorders including a subset of muscular atrophy and epilepsy disorders, affect splicing.

Perturbing splicing 

In eukaryotic cells, including our own, splicing occurs after DNA is transcribed to produce an RNA copy of a gene, which contains both coding and non-coding regions of RNA. The noncoding intron regions are removed, and the coding exon segments are spliced back together to make a near-final blueprint, which can then be translated into a protein. 

According to first author Michael P. McGurk, a postdoc in the lab of MIT Professor Christopher Burge, previous approaches could provide an average picture of regulation, but could not necessarily predict the regulation of splicing factors at particular exons in particular genes.

KATMAP draws on RNA sequencing data generated from perturbation experiments, which alter the expression level of a regulatory factor by either overexpressing it or knocking down its levels. The consequences of overexpression or knockdown are that the genes regulated by the splicing factor should exhibit different levels of splicing after perturbation, which helps the model identify the splicing factor’s targets. 

Cells, however, are complex, interconnected systems, where one small change can cause a cascade of effects. KATMAP is also able to distinguish between direct targets from indirect, downstream impacts by incorporating known information about the sequence the splicing factor is likely to interact with, referred to as a binding site or binding motif.

“In our analyses, we identify predicted targets as exons that have binding sites for this particular factor in the regions where this model thinks they need to be to impact regulation,” McGurk says, while non-targets may be affected by perturbation but don’t have the likely appropriate binding sites nearby. 

This is especially helpful for splicing factors that aren’t as well-studied. 

“One of our goals with KATMAP was to try to make the model general enough that it can learn what it needs to assume for particular factors, like how similar the binding site has to be to the known motif or how regulatory activity changes with the distance of the binding sites from the splice sites,” McGurk says. 

Starting simple

Although predictive models can be very powerful at presenting possible hypotheses, many are considered “black boxes,” meaning the rationale that gives rise to their conclusions is unclear. KATMAP, on the other hand, is an interpretable model that enables researchers to quickly generate hypotheses and interpret splicing patterns in terms of regulatory factors while also understanding how the predictions were made. 

“I don’t just want to predict things, I want to explain and understand,” McGurk says. “We set up the model to learn from existing information about splicing and binding, which gives us biologically interpretable parameters.” 

The researchers did have to make some simplifying assumptions in order to develop the model. KATMAP considers only one splicing factor at a time, although it is possible for splicing factors to work in concert with one another. The RNA target sequence could also be folded in such a way that the factor wouldn’t be able to access a predicted binding site, so the site is present but not utilized.

“When you try to build up complete pictures of complex phenomena, it’s usually best to start simple,” McGurk says. “A model that only considers one splicing factor at a time is a good starting point.” 

David McWaters, another postdoc in the Burge Lab and a co-author on the paper, conducted key experiments to test and validate that aspect of the KATMAP model.

Future directions

The Burge lab is collaborating with researchers at Dana-Farber Cancer Institute to apply KATMAP to the question of how splicing factors are altered in disease contexts, as well as with other researchers at MIT as part of an MIT HEALS grant to model splicing factor changes in stress responses. McGurk also hopes to extend the model to incorporate cooperative regulation for splicing factors that work together. 

“We’re still in a very exploratory phase, but I would like to be able to apply these models to try to understand splicing regulation in disease or development. In terms of variation of splicing factors, they are related, and we need to understand both,” McGurk says.

Burge, the Uncas (1923) and Helen Whitaker Professor and senior author of the paper, will continue to work on generalizing this approach to build interpretable models for other aspects of gene regulation.

“We now have a tool that can learn the pattern of activity of a splicing factor from types of data that can be readily generated for any factor of interest,” says Burge, who is also an extra-mural member of the Koch Institute for Integrative Cancer Research and an associate member of the Broad Institute of MIT and Harvard. “As we build up more of these models, we’ll be better able to infer which splicing factors have altered activity in a disease state from transcriptomic data, to help understand which splicing factors are driving pathology.”

MIT engineers have developed a flexible drug-delivery patch that can be placed on the heart after a heart attack to help promote healing and regeneration of cardiac tissue.

The new patch is designed to carry several different drugs that can be released at different times, on a pre-programmed schedule. In a study of rats, the researchers showed that this treatment reduced the amount of damaged heart tissue by 50 percent and significantly improved cardiac function.

If approved for use in humans, this type of patch could help heart attack victims recover more of their cardiac function than is now possible, the researchers say.

“When someone suffers a major heart attack, the damaged cardiac tissue doesn’t regenerate effectively, leading to a permanent loss of heart function. The tissue that was damaged doesn’t recover,” says Ana Jaklenec, a principal investigator at MIT’s Koch Institute for Integrative Cancer Research. “Our goal is to restore that function and help people regain a stronger, more resilient heart after a myocardial infarction.”

Jaklenec and Robert Langer, the David H. Koch Institute Professor at MIT and a member of the Koch Institute, are the senior authors of the new study, which appears today in Cell Biomaterials. Former MIT postdoc Erika Wangis the lead author of the paper.

Programmed drug delivery

After a heart attack, many patients end up having bypass surgery, which improves blood flow to the heart but doesn’t repair the cardiac tissue that was damaged. In the new study, the MIT team wanted to create a patch that could be applied to the heart at the same time that the surgery is performed.

This patch, they hoped, could deliver drugs over an extended time period to promote tissue healing. Many diseases, including heart conditions, require phase-specific treatment, but most systems release drugs all at once. Timed delivery better synchronizes therapy with recovery.

“We wanted to see if it’s possible to deliver a precisely orchestrated therapeutic intervention to help heal the heart, right at the site of damage, while the surgeon is already performing open-heart surgery,” Jaklenec says.

To achieve this, the researchers set out to adapt drug-delivery microparticles they had previously developed, which consist of capsules similar to tiny coffee cups with lids. These capsules are made from a polymer called PLGA and can be sealed with a drug inside.

By changing the molecular weight of the polymers used to form the lids, the researchers can control how quickly they degrade, which enables them to program the particles to release their contents at specific times. For this application, the researchers designed particles that break down during days 1-3, days 7-9, and days 12-14 after implantation.

This allowed them to devise a regimen of three drugs that promote heart healing in different ways. The first set of particles release neuregulin-1, a growth factor that helps to prevent cell death. At the next time point, particles release VEGF, a growth factor that promotes formation of blood vessels surrounding the heart. The last batch of particles releases a small molecule drug called GW788388, which inhibits the formation of scar tissue that can occur following a heart attack.

“When tissue regenerates, it follows a carefully timed series of steps,” Jaklenec says. “Dr. Wang created a system that delivers key components at just the right time, in the sequence that the body naturally uses to heal.”

The researchers embedded rows of these particles into thin sheets of a tough but flexible hydrogel, similar to a contact lens. This hydrogel is made from alginate and PEGDA, two biocompatible polymers that eventually break down in the body. For this study, the researchers created compact, miniature patches only a few millimeters across.

“We encapsulate arrays of these particles in a hydrogel patch, and then we can surgically implant this patch into the heart. In this way, we’re really programming the treatment into this material,” Wang says.

Better heart function

Once they created these patches, the researchers tested them on spheres of heart tissue that included cardiomyocytes generated from induced pluripotent stem cells. These spheres also included endothelial cells and human ventricular cardiac fibroblasts, which are also important components of the heart.

The researchers exposed those spheres to low-oxygen conditions, mimicking the effects of a heart attack, then placed the patches over them. They found that the patches promoted blood vessel growth, helped more cells to survive, and reduced the amount of fibrosis that developed.

In tests in a rat model of heart attack, the researchers also saw significant improvements following treatment with the patch. Compared to no treatment or IV injection of the same drugs, animals treated with the patch showed 33 percent higher survival rates, a 50 percent reduction in the amount of damaged tissue, and significantly increased cardiac output.

The researchers showed that the patches would eventually dissolve over time, becoming a very thin layer over the course of a year without disrupting the heart’s mechanical function.

“This is an important way to combine drug delivery and biomaterials to potentially new treatments for patients,” Langer says.

Of the drugs tested in this study, neuregulin-1 and VEGF have been tested in clinical trials to treat heart conditions, but GW788388 has only been explored in animal models. The researchers now hope to test their patches in additional animal models in hopes of running a clinical trial in the future.

The current version of the patch needs to be implanted surgically, but the researchers are exploring the possibility of incorporating these microparticles into stents that could be inserted into arteries to deliver drugs on a programmed schedule.

Other authors of the paper include Elizabeth Calle, Binbin Ying, Behnaz Eshaghi, Linzixuan Zhang, Xin Yang, Stacey Qiaohui Lin, Jooli Han, Alanna Backx, Yuting Huang, Sevinj Mursalova, Chuhan Joyce Qi, and Yi Liu.

The researchers were supported by the Natural Sciences and Engineering Research Council of Canada and the U.S. National Heart, Lung, and Blood Institute.

More than 70 aircraft are struck by lightning every day. If you happen to be flying when a strike occurs, chances are you won’t feel a thing, thanks to lightning protection measures that are embedded in key zones throughout the aircraft.

Lightning protection systems work well, largely because they are designed for planes with a “tube-and-wing” structure, a simple geometry common to most aircraft today. But future airplanes may not look and fly the same way. The aviation industry is exploring new designs, including blended-wing bodies and truss-braced wings, partly to reduce fuel and weight costs. But researchers don’t yet know how these unconventional designs might respond to lightning strikes.

MIT aerospace engineers are hoping to change that with a new physics-based approach that predicts how lightning would sweep across a plane with any design. The tool then generates a zoning map highlighting sections of an aircraft that would require various degrees of lightning protection, given how they are likely to experience a strike.

“People are starting to conceive aircraft that look very different from what we’re used to, and we can’t apply exactly what we know from historical data to these new configurations because they’re just too different,” says Carmen Guerra-Garcia, associate professor of aeronautics and astronautics (AeroAstro) at MIT. “Physics-based methods are universal. They’re agnostic to the type of geometry or vehicle. This is the path forward to be able to do this lightning zoning and protect future aircraft.”

She and her colleagues report their results in a study appearing this week in IEEE Access. The study’s first author is AeroAstro graduate student Nathanael Jenkins. Other co-authors include Louisa Michael and Benjamin Westin of Boeing Research and Technology.

First strike

When lightning strikes, it first attaches to a part of a plane — typically a sharp edge or extremity — and hangs on for up to a second. During this brief flash, the plane continues speeding through the air, causing the lightning current to “sweep” over parts of its surface, potentially changing in intensity and re-attaching at certain points where the intense current flow could damage vulnerable sections of an aircraft.

In previous work, Guerra-Garcia’s group developed a model to predict the parts of a plane where lightning is most likely to first connect. That work, led by graduate student Sam Austin, established a starting point for the team’s new work, which aims to predict how and where the lightning will then sweep over the plane’s surface. The team next converted their lightning sweep predictions into zoning maps to identify vulnerable regions requiring certain levels of protection.

A typical tube-and-wing plane is divided into three main zones, as classified by the aviation industry. Each zone has a clear description of the level of current it must withstand in order to be certified for flight. Parts of a plane that are more likely to be hit by lightning are generally classified as zone 1 and require more protection, which can include embedded metal foil in the skin of the airplane that conducts away a lightning current.

To date, an airplane’s lightning zones have been determined over many years of flight inspections after lightning strikes and fine-tuning of protection measures. Guerra-Garcia and her colleagues looked to develop a zoning approach based on physics, rather than historical flight data. Such a physics-based mapping could be applied to any shape of aircraft, such as unconventional and largely untested designs, to identify regions that really require reinforcement.

“Protecting aircraft from lightning is heavy,” Jenkins says. “Embedding copper mesh or foil throughout an aircraft is an added weight penalty. And if we had the greatest level of protection for every part of the plane’s surface, the plane would weigh far too much. So zoning is about trying to optimize the weight of the system while also having it be as safe as possible.”

In the zone

For their new approach, the team developed a model to predict the pattern of lightning sweep and the corresponding lightning protection zones, for a given airplane geometry. Starting with a specific airplane shape — in their case, a typical tube-and-wing structure — the researchers simulated the fluid dynamics, or how air would flow around a plane, given a certain speed, altitude, and pitch angle. They also incorporated their previous model that predicts the places where lightning is more likely to initially attach.

For each initial attachment point, the team simulated tens of thousands of potential lightning arcs, or angles from which the current strikes the plane. They then ran the model forward to predict how the tens of thousands of potential strikes would follow the air flow across the plane’s surface. These runs produced a statistical representation of where lightning, striking a specific point on a plane, is likely to flow and potentially cause damage. The team converted this statistical representation into a map of zones of varying vulnerability.

They validated the method on a conventional tube-and-wing structure, showing that the zoning maps generated by the physics-based approach were consistent with what the aviation industry has determined over decades of fine-tuning.

“We now have a physics-based tool that provides some metrics like the probability of lightning attachment and dwell time, which is how long an arc will linger at a specific point,” Guerra-Garcia explains. “We convert those physics metrics into zoning maps to show, if I’m in this red region, the lightning arc will stay for a long time, so that region needs to be heavily protected.”

The team is starting to apply the approach to new geometries, such as blended-wing designs and truss-braced structures. The researchers envision that the tool can help designers incorporate safe and efficient lightning-protection systems early on in the design process.

“Lightning is incredible and terrifying at the same time, and I have full confidence in flying on planes at the moment,” Jenkins says. “I want to have that same confidence in 20 years’ time. So, we need a new way to zone aircraft.”

“With physics-based methods like the ones developed with professor Guerra-Garcia’s group we have the opportunity to shape industry standards and as an industry rely on the underlying physics to develop guidelines for aircraft certification through simulation,” says co-author Louisa Michael of Boeing Technology Innovation. Currently, we are engaging with industrial committees to propose these methods to be included in Aerospace Recommended Practices.”

“Zoning unconventional aircraft is not an easy task,” adds co-author Ben Westin of Boeing Technology Innovation. “But these methods will allow us to confidently identify which threat levels each part of the aircraft needs to be protected against and certified for, and they give our design engineers a platform to do their best work to optimize aircraft design.”

Beyond airplanes, Guerra-Garcia is looking at ways to adapt the lightning protection model to other technologies, including wind turbines.

“About 60 percent of blade losses are due to lightning and will become worse as we move offshore because wind turbines will be even bigger and more susceptible to upward lightning,” she says. “They have many of the same challenges of a flowing gas environment. It’s more complex, and we will apply this same sort of methodology to this space.”

This research was funded, in part, by the Boeing Company.

Quantum computers have the potential to model new molecules and weather patterns better than any computer today. They may also one day accelerate artificial intelligence algorithms at a much lower energy footprint. But anyone interested in using quantum computers faces a steep learning curve that starts with getting access to quantum devices and then figuring out one of the many quantum software programs on the market.

Now qBraid, founded by Kanav Setia and Jason Necaise ’20, is providing a gateway to quantum computing with a platform that gives users access to the leading quantum devices and software. Users can log on to qBraid’s cloud-based interface and connect with quantum devices and other computing resources from leading companies like Nvidia, Microsoft, and IBM. In a few clicks, they can start coding or deploy cutting-edge software that works across devices.

“The mission is to take you from not knowing anything about quantum computing to running your first program on these amazing machines in less than 10 minutes,” Setia says. “We’re a one-stop platform that gives access to everything the quantum ecosystem has to offer. Our goal is to enable anyone — whether they’re enterprise customers, academics, or individual users — to build and ultimately deploy applications.”

Since its founding in June of 2020, qBraid has helped more than 20,000 people in more than 120 countries deploy code on quantum devices. That traction is ultimately helping to drive innovation in a nascent industry that’s expected to play a key role in our future.

“This lowers the barrier to entry for a lot of newcomers,” Setia says. “They can be up and running in a few minutes instead of a few weeks. That’s why we’ve gotten so much adoption around the world. We’re one of the most popular platforms for accessing quantum software and hardware.”

A quantum “software sandbox”

Setia met Necaise while the two interned at IBM. At the time, Necaise was an undergraduate at MIT majoring in physics, while Setia was at Dartmouth College. The two enjoyed working together, and Necaise said if Setia ever started a company, he’d be interested in joining.

A few months later, Setia decided to take him up on the offer. At Dartmouth, Setia had taken one of the first applied quantum computing classes, but students spent weeks struggling to install all the necessary software programs before they could even start coding.

“We hadn’t even gotten close to developing any useful algorithms,” Seita said. “The idea for qBraid was, ‘Why don’t we build a software sandbox in the cloud and give people an easy programming setup out of the box?’ Connection with the hardware would already be done.”

The founders received early support from the MIT Sandbox Innovation Fund and took part in the delta v summer startup accelerator run by the Martin Trust Center for MIT Entrepreneurship.

“Both programs provided us with very strong mentorship,” Setia says. “They give you frameworks on what a startup should look like, and they bring in some of the smartest people in the world to mentor you — people you’d never have access to otherwise.”

Necaise left the company in 2021. Setia, meanwhile, continued to find problems with quantum software outside of the classroom.

“This is a massive bottleneck,” Setia says. “I’d worked on several quantum software programs that pushed out updates or changes, and suddenly all hell broke loose on my codebase. I’d spend two to four weeks jostling with these updates that had almost nothing to do with the quantum algorithms I was working on.”

QBraid started as a platform with pre-installed software that let developers start writing code immediately. The company also added support for version-controlled quantum software so developers could build applications on top without worrying about changes. Over time, qBraid added connections to quantum computers and tools that lets quantum programs run across different devices.

“The pitch was you don’t need to manage a bunch of software or a whole bunch of cloud accounts,” Setia says. “We’re a single platform: the quantum cloud.”

QBraid also launched qBook, a learning platform that offers interactive courses in quantum computing.

“If you see a piece of code you like, you just click play and the code runs,” Setia says. “You can run a whole bunch of code, modify it on the fly, and you can understand how it works. It runs on laptops, iPads, and phones. A significant portion of our users are from developing countries, and they’re developing applications from their phones.”

Democratizing quantum computing

Today qBraid’s 20,000 users come from over 400 universities and 100 companies around the world. As qBraid’s user base has grown, the company went from integrating quantum computers onto their platform from the outside to creating a quantum operating system, qBraid-OS, that is currently being used by four leading quantum companies.

“We are productizing these quantum computers,” Setia explains. “Many quantum companies are realizing they want to focus their energy completely on the hardware, with us productizing their infrastructure. We’re like the operating system for quantum computers.”

People are using qBraid to build quantum applications in AI and machine learning, to discover new molecules or develop new drugs, and to develop applications in finance and cybersecurity. With every new use case, Setia says qBraid is democratizing quantum computing to create the quantum workforce that will continue to advance the field.

“[In 2018], an article in The New York Times said there were possibly less than 1,000 people in the world that could be called experts in quantum programming,” Setia says. “A lot of people want to access these cutting-edge machines, but they don’t have the right software backgrounds. They are just getting started and want to play with algorithms. QBraid gives those people an easy programming setup out of the box.”

With the rapid advancement of generative artificial intelligence, teachers and school leaders are looking for answers to complicated questions about successfully integrating technology into lessons, while also ensuring students actually learn what they’re trying to teach. 

Justin Reich, an associate professor in MIT’s Comparative Media Studies/Writing program, hopes a new guidebook published by the MIT Teaching Systems Lab can support K-12 educators as they determine what AI policies or guidelines to craft.

“Throughout my career, I’ve tried to be a person who researches education and technology and translates findings for people who work in the field,” says Reich. “When tricky things come along I try to jump in and be helpful.” 

“A Guide to AI in Schools: Perspectives for the Perplexed,” published this fall, was developed with the support of an expert advisory panel and other researchers. The project includes input from more than 100 students and teachers from around the United States, sharing their experiences teaching and learning with new generative AI tools. 

“We’re trying to advocate for an ethos of humility as we examine AI in schools,” Reich says. “We’re sharing some examples from educators about how they’re using AI in interesting ways, some of which might prove sturdy and some of which might prove faulty. And we won’t know which is which for a long time.”

Finding answers to AI and education questions

The guidebook attempts to help K-12 educators, students, school leaders, policymakers, and others collect and share information, experiences, and resources. AI’s arrival has left schools scrambling to respond to multiple challenges, like how to ensure academic integrity and maintain data privacy. 

Reich cautions that the guidebook is not meant to be prescriptive or definitive, but something that will help spark thought and discussion. 

“Writing a guidebook on generative AI in schools in 2025 is a little bit like writing a guidebook of aviation in 1905,” the guidebook’s authors note. “No one in 2025 can say how best to manage AI in schools.”

Schools are also struggling to measure how student learning loss looks in the age of AI. “How does bypassing productive thinking with AI look in practice?” Reich asks. “If we think teachers provide content and context to support learning and students no longer perform the exercises housing the content and providing the context, that’s a serious problem.”

Reich invites people directly impacted by AI to help develop solutions to the challenges its ubiquity presents. “It’s like observing a conversation in the teacher’s lounge and inviting students, parents, and other people to participate about how teachers think about AI,” he says, “what they are seeing in their classrooms, and what they’ve tried and how it went.”

The guidebook, in Reich’s view, is ultimately a collection of hypotheses expressed in interviews with teachers: well-informed, initial guesses about the paths that schools could follow in the years ahead. 

Producing educator resources in a podcast

In addition to the guidebook, the Teaching Systems Lab also recently produced “The Homework Machine,” a seven-part series from the Teachlab podcast that explores how AI is reshaping K-12 education. 

Reich produced the podcast in collaboration with journalist Jesse Dukes. Each episode tackles a specific area, asking important questions about challenges related to issues like AI adoption, poetry as a tool for student engagement, post-Covid learning loss, pedagogy, and book bans. The podcast allows Reich to share timely information about education-related updates and collaborate with people interested in helping further the work.

“The academic publishing cycle doesn’t lend itself to helping people with near-term challenges like those AI presents,” Reich says. “Peer review takes a long time, and the research produced isn’t always in a form that’s helpful to educators.” Schools and districts are grappling with AI in real time, bypassing time-tested quality control measures. 

The podcast can help reduce the time it takes to share, test, and evaluate AI-related solutions to new challenges, which could prove useful in creating training and resources.  

“We hope the podcast will spark thought and discussion, allowing people to draw from others’ experiences,” Reich says.

The podcast was also produced into an hour-long radio special, which was broadcast by public radio stations across the country.

“We’re fumbling around in the dark”

Reich is direct in his assessment of where we are with understanding AI and its impacts on education. “We’re fumbling around in the dark,” he says, recalling past attempts to quickly integrate new tech into classrooms. These failures, Reich suggests, highlight the importance of patience and humility as AI research continues. “AI bypassed normal procurement processes in education; it just showed up on kids’ phones,” he notes. 

“We’ve been really wrong about tech in the past,” Reich says. Despite districts’ spending on tools like smartboards, for example, research indicates there’s no evidence that they improve learning or outcomes. In a new article for article for The Conversation, he argues that early teacher guidance in areas like web literacy has produced bad advice that still exists in our educational system. “We taught students and educators not to trust Wikipedia,” he recalls, “and to search for website credibility markers, both of which turned out to be incorrect.” Reich wants to avoid a similar rush to judgment on AI, recommending that we avoid guessing at AI-enabled instructional strategies.

These challenges, coupled with potential and observed student impacts, significantly raise the stakes for schools and students’ families in the AI race. “Education technology always provokes teacher anxiety,” Reich notes, “but the breadth of AI-related concerns is much greater than in other tech-related areas.” 

The dawn of the AI age is different from how we’ve previously received tech into our classrooms, Reich says. AI wasn’t adopted like other tech. It simply arrived. It’s now upending educational models and, in some cases, complicating efforts to improve student outcomes.

Reich is quick to point out that there are no clear, definitive answers on effective AI implementation and use in classrooms; those answers don’t currently exist. Each of the resources Reich helped develop invite engagement from the audiences they target, aggregating valuable responses others might find useful.

“We can develop long-term solutions to schools’ AI challenges, but it will take time and work,” he says. “AI isn’t like learning to tie knots; we don’t know what AI is, or is going to be, yet.” 

Reich also recommends learning more about AI implementation from a variety of sources. “Decentralized pockets of learning can help us test ideas, search for themes, and collect evidence on what works,” he says. “We need to know if learning is actually better with AI.” 

While teachers don’t get to choose regarding AI’s existence, Reich believes it’s important that we solicit their input and involve students and other stakeholders to help develop solutions that improve learning and outcomes. 

“Let’s race to answers that are right, not first,” Reich says.

A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.

To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay are developing computer vision algorithms that carefully monitor animal populations. A member of the lab of MIT Department of Electrical Engineering and Computer Science assistant professor and CSAIL principal investigator Sara Beery, Kay is currently working on tracking salmon in the Pacific Northwest, where they provide crucial nutrients to predators like birds and bears, while managing the population of prey, like bugs.

With all that wildlife data, though, researchers have lots of information to sort through and many AI models to choose from to analyze it all. Kay and his colleagues at CSAIL and the University of Massachusetts Amherst are developing AI methods that make this data-crunching process much more efficient, including a new approach called “consensus-driven active model selection” (or “CODA”) that helps conservationists choose which AI model to use. Their work was named a Highlight Paper at the International Conference on Computer Vision (ICCV) in October.

That research was supported, in part, by the National Science Foundation, Natural Sciences and Engineering Research Council of Canada, and Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Here, Kay discusses this project, among other conservation efforts.

Q: In your paper, you pose the question of which AI models will perform the best on a particular dataset. With as many as 1.9 million pre-trained models available in the HuggingFace Models repository alone, how does CODA help us address that challenge?

A: Until recently, using AI for data analysis has typically meant training your own model. This requires significant effort to collect and annotate a representative training dataset, as well as iteratively train and validate models. You also need a certain technical skill set to run and modify AI training code. The way people interact with AI is changing, though — in particular, there are now millions of publicly available pre-trained models that can perform a variety of predictive tasks very well. This potentially enables people to use AI to analyze their data without developing their own model, simply by downloading an existing model with the capabilities they need. But this poses a new challenge: Which model, of the millions available, should they use to analyze their data? 

Typically, answering this model selection question also requires you to spend a lot of time collecting and annotating a large dataset, albeit for testing models rather than training them. This is especially true for real applications where user needs are specific, data distributions are imbalanced and constantly changing, and model performance may be inconsistent across samples. Our goal with CODA was to substantially reduce this effort. We do this by making the data annotation process “active.” Instead of requiring users to bulk-annotate a large test dataset all at once, in active model selection we make the process interactive, guiding users to annotate the most informative data points in their raw data. This is remarkably effective, often requiring users to annotate as few as 25 examples to identify the best model from their set of candidates. 

We’re very excited about CODA offering a new perspective on how to best utilize human effort in the development and deployment of machine-learning (ML) systems. As AI models become more commonplace, our work emphasizes the value of focusing effort on robust evaluation pipelines, rather than solely on training.

Q: You applied the CODA method to classifying wildlife in images. Why did it perform so well, and what role can systems like this have in monitoring ecosystems in the future?

A: One key insight was that when considering a collection of candidate AI models, the consensus of all of their predictions is more informative than any individual model’s predictions. This can be seen as a sort of “wisdom of the crowd:” On average, pooling the votes of all models gives you a decent prior over what the labels of individual data points in your raw dataset should be. Our approach with CODA is based on estimating a “confusion matrix” for each AI model — given the true label for some data point is class X, what is the probability that an individual model predicts class X, Y, or Z? This creates informative dependencies between all of the candidate models, the categories you want to label, and the unlabeled points in your dataset.

Consider an example application where you are a wildlife ecologist who has just collected a dataset containing potentially hundreds of thousands of images from cameras deployed in the wild. You want to know what species are in these images, a time-consuming task that computer vision classifiers can help automate. You are trying to decide which species classification model to run on your data. If you have labeled 50 images of tigers so far, and some model has performed well on those 50 images, you can be pretty confident it will perform well on the remainder of the (currently unlabeled) images of tigers in your raw dataset as well. You also know that when that model predicts some image contains a tiger, it is likely to be correct, and therefore that any model that predicts a different label for that image is more likely to be wrong. You can use all these interdependencies to construct probabilistic estimates of each model’s confusion matrix, as well as a probability distribution over which model has the highest accuracy on the overall dataset. These design choices allow us to make more informed choices over which data points to label and ultimately are the reason why CODA performs model selection much more efficiently than past work.

There are also a lot of exciting possibilities for building on top of our work. We think there may be even better ways of constructing informative priors for model selection based on domain expertise — for instance, if it is already known that one model performs exceptionally well on some subset of classes or poorly on others. There are also opportunities to extend the framework to support more complex machine-learning tasks and more sophisticated probabilistic models of performance. We hope our work can provide inspiration and a starting point for other researchers to keep pushing the state of the art.

Q: You work in the Beerylab, led by Sara Beery, where researchers are combining the pattern-recognition capabilities of machine-learning algorithms with computer vision technology to monitor wildlife. What are some other ways your team is tracking and analyzing the natural world, beyond CODA?

A: The lab is a really exciting place to work, and new projects are emerging all the time. We have ongoing projects monitoring coral reefs with drones, re-identifying individual elephants over time, and fusing multi-modal Earth observation data from satellites and in-situ cameras, just to name a few. Broadly, we look at emerging technologies for biodiversity monitoring and try to understand where the data analysis bottlenecks are, and develop new computer vision and machine-learning approaches that address those problems in a widely applicable way. It’s an exciting way of approaching problems that sort of targets the “meta-questions” underlying particular data challenges we face. 

The computer vision algorithms I’ve worked on that count migrating salmon in underwater sonar video are examples of that work. We often deal with shifting data distributions, even as we try to construct the most diverse training datasets we can. We always encounter something new when we deploy a new camera, and this tends to degrade the performance of computer vision algorithms. This is one instance of a general problem in machine learning called domain adaptation, but when we tried to apply existing domain adaptation algorithms to our fisheries data we realized there were serious limitations in how existing algorithms were trained and evaluated. We were able to develop a new domain adaptation framework, published earlier this year in Transactions on Machine Learning Research, that addressed these limitations and led to advancements in fish counting, and even self-driving and spacecraft analysis.

One line of work that I’m particularly excited about is understanding how to better develop and analyze the performance of predictive ML algorithms in the context of what they are actually used for. Usually, the outputs from some computer vision algorithm — say, bounding boxes around animals in images — are not actually the thing that people care about, but rather a means to an end to answer a larger problem — say, what species live here, and how is that changing over time? We have been working on methods to analyze predictive performance in this context and reconsider the ways that we input human expertise into ML systems with this in mind. CODA was one example of this, where we showed that we could actually consider the ML models themselves as fixed and build a statistical framework to understand their performance very efficiently. We have been working recently on similar integrated analyses combining ML predictions with multi-stage prediction pipelines, as well as ecological statistical models. 

The natural world is changing at unprecedented rates and scales, and being able to quickly move from scientific hypotheses or management questions to data-driven answers is more important than ever for protecting ecosystems and the communities that depend on them. Advancements in AI can play an important role, but we need to think critically about the ways that we design, train, and evaluate algorithms in the context of these very real challenges.

By stimulating cancer cells to produce a molecule that activates a signaling pathway in nearby immune cells, MIT researchers have found a way to force tumors to trigger their own destruction.

Activating this signaling pathway, known as the cGAS-STING pathway, worked even better when combined with existing immunotherapy drugs known as checkpoint blockade inhibitors, in a study of mice. That dual treatment was successfully able to control tumor growth.

The researchers turned on the cGAS-STING pathway in immune cells using messenger RNA delivered to cancer cells. This approach may avoid the side effects of delivering large doses of a STING activator, and takes advantage of a natural process in the body. This could make it easier to develop a treatment for use in patients, the researchers say.

“Our approach harnesses the tumor’s own machinery to produce immune-stimulating molecules, creating a powerful antitumor response,” says Natalie Artzi, a principal research scientist at MIT’s Institute for Medical Engineering and Science, an associate professor of medicine at Harvard Medical School, a core faculty member at the Wyss Institute for Biologically Inspired Engineering at Harvard, and the senior author of the study.

“By increasing cGAS levels inside cancer cells, we can enhance delivery efficiency — compared to targeting the more scarce immune cells in the tumor microenvironment — and stimulate the natural production of cGAMP, which then activates immune cells locally,” she says. “This strategy not only strengthens antitumor immunity but also reduces the toxicity associated with direct STING agonist delivery, bringing us closer to safer and more effective cancer immunotherapies.”

Alexander Cryer, a visiting scholar at IMES, is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences.

Immune activation

STING (short for stimulator of interferon genes) is a protein that helps to trigger immune responses. When STING is activated, it turns on a pathway that initiates production of type one interferons, which are cytokines that stimulate immune cells.

Many research groups, including Artzi’s, have explored the possibility of artificially stimulating this pathway with molecules called STING agonists, which could help immune cells to recognize and attack tumor cells. This approach has worked well in animal models, but it has had limited success in clinical trials, in part because the required doses can cause harmful side effects.

While working on a project exploring new ways to deliver STING agonists, Cryer became intrigued when he learned from previous work that cancer cells can produce a STING activator known as cGAMP. The cells then secrete cGAMP, which can activate nearby immune cells.

“Part of my philosophy of science is that I really enjoy using endogenous processes that the body already has, and trying to utilize them in a slightly different context. Evolution has done all the hard work. We just need to figure out how push it in a different direction,” Cryer says. “Once I saw that cancer cells produce this molecule, I thought: Maybe there’s a way to take this process and supercharge it.”

Within cells, the production of cGAMP is catalyzed by an enzyme called cGAS. To get tumor cells to activate STING in immune cells, the researchers devised a way to deliver messenger RNA that encodes cGAS. When this enzyme detects double-stranded DNA in the cell body, which can be a sign of either infection or cancer-induced damage, it begins producing cGAMP.

“It just so happens that cancer cells, because they’re dividing so fast and not particularly accurately, tend to have more double-stranded DNA fragments than healthy cells,” Cryer says.

The tumor cells then release cGAMP into tumor microenvironment, where it can be taken up by neighboring immune cells and activate their STING pathway.

Targeting tumors

Using a mouse model of melanoma, the researchers evaluated their new strategy’s potential to kill cancer cells. They injected mRNA encoding cGAS, encapsulated in lipid nanoparticles, into tumors. One group of mice received this treatment alone, while another received a checkpoint blockade inhibitor, and a third received both treatments.

Given on their own, cGAS and the checkpoint inhibitor each significantly slowed tumor growth. However, the best results were seen in the mice that received both treatments. In that group, tumors were completely eradicated in 30 percent of the mice, while none of the tumors were fully eliminated in the groups that received just one treatment.

An analysis of the immune response showed that the mRNA treatment stimulated production of interferon as well as many other immune signaling molecules. A variety of immune cells, including macrophages and dendritic cells, were activated. These cells help to stimulate T cells, which can then destroy cancer cells.

The researchers were able to elicit these responses with just a small dose of cancer-cell-produced cGAMP, which could help to overcome one of the potential obstacles to using cGAMP on its own as therapy: Large doses are required to stimulate an immune response, and these doses can lead to widespread inflammation, tissue damage, and autoimmune reactions. When injected on its own, cGAMP tends to spread through the body and is rapidly cleared from the tumor, while in this study, the mRNA nanoparticles and cGAMP remained at the tumor site.

“The side effects of this class of molecule can be pretty severe, and one of the potential advantages of our approach is that you’re able to potentially subvert some toxicity that you might see if you’re giving the free molecules,” Cryer says.

The researchers now hope to work on adapting the delivery system so that it could be given as a systemic injection, rather than injecting it into the tumor. They also plan to test the mRNA therapy in combination with chemotherapy drugs or radiotherapy that damage DNA, which could make the therapy even more effective because there could be even more double-stranded DNA available to help activate the synthesis of cGAMP.

Managing a power grid is like trying to solve an enormous puzzle.

Grid operators must ensure the proper amount of power is flowing to the right areas at the exact time when it is needed, and they must do this in a way that minimizes costs without overloading physical infrastructure. Even more, they must solve this complicated problem repeatedly, as rapidly as possible, to meet constantly changing demand.

To help crack this consistent conundrum, MIT researchers developed a problem-solving tool that finds the optimal solution much faster than traditional approaches while ensuring the solution doesn’t violate any of the system’s constraints. In a power grid, constraints could be things like generator and line capacity.

This new tool incorporates a feasibility-seeking step into a powerful machine-learning model trained to solve the problem. The feasibility-seeking step uses the model’s prediction as a starting point, iteratively refining the solution until it finds the best achievable answer.

The MIT system can unravel complex problems several times faster than traditional solvers, while providing strong guarantees of success. For some extremely complex problems, it could find better solutions than tried-and-true tools. The technique also outperformed pure machine learning approaches, which are fast but can’t always find feasible solutions.

In addition to helping schedule power production in an electric grid, this new tool could be applied to many types of complicated problems, such as designing new products, managing investment portfolios, or planning production to meet consumer demand.

“Solving these especially thorny problems well requires us to combine tools from machine learning, optimization, and electrical engineering to develop methods that hit the right tradeoffs in terms of providing value to the domain, while also meeting its requirements. You have to look at the needs of the application and design methods in a way that actually fulfills those needs,” says Priya Donti, the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the Laboratory for Information and Decision Systems (LIDS).

Donti, senior author of an open-access paper on this new tool, called FSNet, is joined by lead author Hoang Nguyen, an EECS graduate student. The paper will be presented at the Conference on Neural Information Processing Systems.

Combining approaches

Ensuring optimal power flow in an electric grid is an extremely hard problem that is becoming more difficult for operators to solve quickly.

“As we try to integrate more renewables into the grid, operators must deal with the fact that the amount of power generation is going to vary moment to moment. At the same time, there are many more distributed devices to coordinate,” Donti explains.

Grid operators often rely on traditional solvers, which provide mathematical guarantees that the optimal solution doesn’t violate any problem constraints. But these tools can take hours or even days to arrive at that solution if the problem is especially convoluted.

On the other hand, deep-learning models can solve even very hard problems in a fraction of the time, but the solution might ignore some important constraints. For a power grid operator, this could result in issues like unsafe voltage levels or even grid outages.

“Machine-learning models struggle to satisfy all the constraints due to the many errors that occur during the training process,” Nguyen explains.

For FSNet, the researchers combined the best of both approaches into a two-step problem-solving framework.

Focusing on feasibility

In the first step, a neural network predicts a solution to the optimization problem. Very loosely inspired by neurons in the human brain, neural networks are deep learning models that excel at recognizing patterns in data.

Next, a traditional solver that has been incorporated into FSNet performs a feasibility-seeking step. This optimization algorithm iteratively refines the initial prediction while ensuring the solution does not violate any constraints.

Because the feasibility-seeking step is based on a mathematical model of the problem, it can guarantee the solution is deployable.

“This step is very important. In FSNet, we can have the rigorous guarantees that we need in practice,” Hoang says.

The researchers designed FSNet to address both main types of constraints (equality and inequality) at the same time. This makes it easier to use than other approaches that may require customizing the neural network or solving for each type of constraint separately.

“Here, you can just plug and play with different optimization solvers,” Donti says.

By thinking differently about how the neural network solves complex optimization problems, the researchers were able to unlock a new technique that works better, she adds.

They compared FSNet to traditional solvers and pure machine-learning approaches on a range of challenging problems, including power grid optimization. Their system cut solving times by orders of magnitude compared to the baseline approaches, while respecting all problem constraints.

FSNet also found better solutions to some of the trickiest problems.

“While this was surprising to us, it does make sense. Our neural network can figure out by itself some additional structure in the data that the original optimization solver was not designed to exploit,” Donti explains.

In the future, the researchers want to make FSNet less memory-intensive, incorporate more efficient optimization algorithms, and scale it up to tackle more realistic problems.

“Finding solutions to challenging optimization problems that are feasible is paramount to finding ones that are close to optimal. Especially for physical systems like power grids, close to optimal means nothing without feasibility. This work provides an important step toward ensuring that deep-learning models can produce predictions that satisfy constraints, with explicit guarantees on constraint enforcement,” says Kyri Baker, an associate professor at the University of Colorado Boulder, who was not involved with this work.

"A persistent challenge for machine learning-based optimization is feasibility. This work elegantly couples end-to-end learning with an unrolled feasibility-seeking procedure that minimizes equality and inequality violations. The results are very promising and I look forward to see where this research will head," adds Ferdinando Fioretto, an assistant professor at the University of Virginia, who was not involved with this work.

Good management of aid projects in developing countries reduces violence in those areas — but poorly managed projects increase the chances of local violence, according to a new study by an MIT economist.

The research, examining World Bank projects in Africa, illuminates a major question surrounding international aid. Observers have long wondered if aid projects, by bringing new resources into developing countries, lead to conflict over those goods as an unintended consequence. Previously, some scholars have identified an increase in violence attached to aid, while others have found a decrease.

The new study shows those prior results are not necessarily wrong, but not entirely right, either. Instead, aid oversight matters. World Bank programs earning the highest evaluation scores for their implementation reduce the likelihood of conflict by up to 12 percent, compared to the worst-managed programs.

“I find that the management quality of these projects has a really strong effect on whether that project leads to conflict or not,” says MIT economist Jacob Moscona, who conducted the research. “Well-managed aid projects can actually reduce conflict, and poorly managed projects increase conflict, relative to no project. So, the way aid programs are organized is very important.”

The findings also suggest aid projects can work well almost anywhere. At times, observers have suggested the political conditions in some countries prevent aid from being effective. But the new study finds otherwise.

“There are ways these programs can have their positive effects without the negative consequences,” Moscona says. “And it’s not the result of what politics looks like on the receiving end; it’s about the organization itself.”

Moscona’s paper detailing the study, “The Management of Aid and Conflict in Africa,” is published in the November issue of the American Economic Journal: Economic Policy. Moscona, the paper’s sole author, is the 3M Career Development Assistant Professor in MIT’s Department of Economics.

Decisions on the ground

To conduct the study, Moscona examined World Bank data from the 1997-2014 time period, using the information compiled by AidData, a nonprofit group that also studies World Bank programs. Importantly, the World Bank conducts extensive evaluations of its projects and includes the identities of project leaders as part of those reviews.

“There are a lot of decisions on the ground made by managers of aid, and aid organizations themselves, that can have a huge impact on whether or not aid leads to conflict, and how aid resources are used and whether they are misappropriated or captured and get into the wrong hands,” Moscona says.

For instance, diligent daily checks about food distribution programs can and have substantially reduced the amount of food that is stolen or “leaks” out of the program. Other projects have created innovative ways of tagging small devices to ensure those objects are used by program participants, reducing appropriation by others.

Moscona combined the World Bank data with statistics from the Armed Conflict Location and Event Data Project (ACLED), a nonprofit that monitors political violence. That enabled him to evaluate how the quality of aid project implementation — and even the quality of the project leadership — influenced local outcomes.

For instance, by looking at the ratings of World Bank project leaders, Moscona found that shifting from a project leader at the 25th percentile, in terms of how frequently projects are linked with conflict, to one at the 75th percentile, increases the chances of local conflict by 15 percent.

“The magnitudes are pretty large, in terms of the probability that a conflict starts in the vicinity of a project,” Moscona observes.

Moscona’s research identified several other aspects of the interaction between aid and conflict that hold up over the region and time period. The establishment of aid programs does not seem to lead to long-term strategic activity by non-government forces, such as land acquisition or the establishment of rebel bases. The effects are also larger in areas that have had recent political violence. And armed conflict is greater when the resources at stake can be expropriated — such as food or medical devices.

“It matters most if you have more divertable resources, like food and medical devices that can be captured, as opposed to infrastructure projects,” Moscona says.

Reconciling the previous results

Moscona also found a clear trend in the data about the timing of violence in relation to aid. Government and other armed groups do not engage in much armed conflict when aid programs are being established; it is the appearance of desired goods themselves that sets off violent activity.

“You don’t see much conflict when the projects are getting off the ground,” Moscona says.” You really see the conflict start when the money is coming in or when the resources start to flow. Which is consistent with the idea of the relevant mechanism being about aid resources and their misappropriation, rather than groups trying to deligitimize a project.”

All told, Moscona’s study finds a logical mechanism explaining the varying results other scholars have found with regard to aid and conflict. If aid programs are not equally well-administered, it stands to reason that their outcomes will not be identical, either.

“There wasn’t much work trying to make those two sets of results speak to each other,” says Moscona. “I see it less as overturning existing results than providing a way to reconcile different results and experiences.”

Moscona’s findings may also speak to the value of aid in general — and provide actionable ideas for institutions such as the World Bank. If better management makes such a difference, then the potential effectiveness of aid programs may increase.

“One goal is to change the conversation about aid,” Moscona says. The data, he suggests, shows that the public discourse about aid can be “less defeatist about the potential negative consequences of aid, and the idea that it’s out of the control of the people who administer it.” 

Cancer immunotherapy, which uses drugs that stimulate the body’s immune cells to attack tumors, is a promising approach to treating many types of cancer. However, it doesn’t work well for some tumors, including ovarian cancer.

To elicit a better response, MIT researchers have designed new nanoparticles that can deliver an immune-stimulating molecule called IL-12 directly to ovarian tumors. When given along with immunotherapy drugs called checkpoint inhibitors, IL-12 helps the immune system launch an attack on cancer cells.

Studying a mouse model of ovarian cancer, the researchers showed that this combination treatment could eliminate metastatic tumors in more than 80 percent of the mice. When the mice were later injected with more cancer cells, to simulate tumor recurrence, their immune cells remembered the tumor proteins and cleared them again.

“What’s really exciting is that we’re able to deliver IL-12 directly in the tumor space. And because of the way that this nanomaterial is designed to allow IL-12 to be borne on the surfaces of the cancer cells, we have essentially tricked the cancer into stimulating immune cells to arm themselves against that cancer,” says Paula Hammond, an MIT Institute Professor, MIT’s vice provost for faculty, and a member of the Koch Institute for Integrative Cancer Research.

Hammond and Darrell Irvine, a professor of immunology and microbiology at the Scripps Research Institute, are the senior authors of the new study, which appears today in Nature Materials. Ivan Pires PhD ’24, now a postdoc at Brigham and Women’s Hospital, is the lead author of the paper.

“Hitting the gas”

Most tumors express and secrete proteins that suppress immune cells, creating a microenvironment in which the immune response is weakened. One of the main players that can kill tumor cells are T cells, but they get sidelined or blocked by the cancer cells and are unable to attack the tumor. Checkpoint inhibitors are an FDA-approved treatment designed to take those brakes off the immune system by removing the immune-suppressing proteins so that T cells can mount an attack on tumor cells

For some cancers, including some types of melanoma and lung cancer, removing the brakes is enough to provoke the immune system into attacking cancer cells. However, ovarian tumors have many ways to suppress the immune system, so checkpoint inhibitors alone usually aren’t enough to launch an immune response.

“The problem with ovarian cancer is no one is hitting the gas. So, even if you take off the brakes, nothing happens,” Pires says.

IL-12 offers one way to “hit the gas,” by supercharging T cells and other immune cells. However, the large doses of IL-12 required to get a strong response can produce side effects due to generalized inflammation, such as flu-like symptoms (fever, fatigue, GI issues, headaches, and fatigue), as well as more severe complications such as liver toxicity and cytokine release syndrome — which can be so severe they may even lead to death.

In a 2022 study, Hammond’s lab developed nanoparticles that could deliver IL-12 directly to tumor cells, which allows larger doses to be given while avoiding the side effects seen when the drug is injected. However, these particles tended to release their payload all at once after reaching the tumor, which hindered their ability to generate a strong T cell response.

In the new study, the researchers modified the particles so that IL-12 would be released more gradually, over about a week. They achieved this by using a different chemical linker to attach IL-12 to the particles.

“With our current technology, we optimize that chemistry such that there’s a more controlled release rate, and that allowed us to have better efficacy,” Pires says.

The particles consist of tiny, fatty droplets known as liposomes, with IL-12 molecules tethered to the surface. For this study, the researchers used a linker called maleimide to attach IL-12 to the liposomes. This linker is more stable than the one they used in the previous generation of particles, which was susceptible to being cleaved by proteins in the body, leading to premature release.

To make sure that the particles get to the right place, the researchers coat them with a layer of a polymer called poly-L-glutamate (PLE), which helps them directly target ovarian tumor cells. Once they reach the tumors, the particles bind to the cancer cell surfaces, where they gradually release their payload and activate nearby T cells.

Disappearing tumors

In tests in mice, the researchers showed that the IL-12-carrying particles could effectively recruit and stimulate T cells that attack tumors. The cancer models used for these studies are metastatic, so tumors developed not only in the ovaries but throughout the peritoneal cavity, which includes the surface of the intestines, liver, pancreas, and other organs. Tumors could even be seen in the lung tissues.

First, the researchers tested the IL-12 nanoparticles on their own, and they showed that this treatment eliminated tumors in about 30 percent of the mice. They also found a significant increase in the number of T cells that accumulated in the tumor environment.

Then, the researchers gave the particles to mice along with checkpoint inhibitors. More than 80 percent of the mice that received this dual treatment were cured. This happened even when the researchers used models of ovarian cancer that are highly resistant to immunotherapy or to the chemotherapy drugs usually used for ovarian cancer.

Patients with ovarian cancer are usually treated with surgery followed by chemotherapy. While this may be initially effective, cancer cells that remain after surgery are often able to grow into new tumors. Establishing an immune memory of the tumor proteins could help to prevent that kind of recurrence.

In this study, when the researchers injected tumor cells into the cured mice five months after the initial treatment, the immune system was still able to recognize and kill the cells.

“We don’t see the cancer cells being able to develop again in that same mouse, meaning that we do have an immune memory developed in those animals,” Pires says.

The researchers are now working with MIT’s Deshpande Center for Technological Innovation to spin out a company that they hope could further develop the nanoparticle technology. In a study published earlier this year, Hammond’s lab reported a new manufacturing approach that should enable large-scale production of this type of nanoparticle.

The research was funded by the National Institutes of Health, the Marble Center for Nanomedicine, the Deshpande Center for Technological Innovation, the Ragon Institute of MGH, MIT, and Harvard, and the Koch Institute Support (core) Grant from the National Cancer Institute.

Quenching, a powerful heat transfer mechanism, is remarkably effective at transporting heat away. But in extreme environments, like nuclear power plants and aboard spaceships, a lot rides on the efficiency and speed of the process.

It’s why Marco Graffiedi, a fifth-year doctoral student at MIT’s Department of Nuclear Science and Engineering (NSE), is researching the phenomenon to help develop the next generation of spaceships and nuclear plants.

Growing up in small-town Italy

Graffiedi’s parents encouraged a sense of exploration, giving him responsibilities for family projects even at a young age. When they restored a countryside cabin in a small town near Palazzolo, in the hills between Florence and Bologna, the then-14-year-old Marco got a project of his own. He had to ensure the animals on the property had enough accessible water without overfilling the storage tank. Marco designed and built a passive hydraulic system that effectively solved the problem and is still functional today.

His proclivity for science continued in high school in Lugo, where Graffiedi enjoyed recreating classical physics phenomena, through experiments. Incidentally, the high school is named after Gregorio Ricci-Curbastro, a mathematician who laid the foundation for the theory of relativity — history that is not lost on Graffiedi. After high school, Graffiedi attended the International Physics Olympiad in Bangkok, a formative event that cemented his love for physics.

A gradual shift toward engineering

A passion for physics and basic sciences notwithstanding, Graffiedi wondered if he’d be a better fit for engineering, where he could use the study of physics, chemistry, and math as tools to build something.

Following that path, he completed a bachelor’s and master’s in mechanical engineering — because an undergraduate degree in Italy takes only three years, pretty much everyone does a master’s, Graffiedi laughs — at the Università di Pisa and the Scuola Superiore Sant’Anna (School of Engineering). The Sant’Anna is a highly selective institution that most students attend to complement their university studies.

Graffiedi’s university studies gradually moved him toward the field of environmental engineering. He researched concentrated solar power in order to reduce the cost of solar power by studying the associated thermal cycle and trying to improve solar power collection. While the project was not very successful, it reinforced Graffiedi’s impression of the necessity of alternative energies. Still firmly planted in energy studies, Graffiedi worked on fracture mechanics for his master’s thesis, in collaboration with (what was then) GE Oil and Gas, researching how to improve the effectiveness of centrifugal compressors. And a summer internship at Fermilab had Graffiedi working on the thermal characterization of superconductive coatings.

With his studies behind him, Graffiedi was still unsure about this professional path. Through the Edison Program from GE Oil and Gas, where he worked shortly after graduation, Graffiedi got to test drive many fields — from mechanical and thermal engineering to exploring gas turbines and combustion. He eventually became a test engineer, coordinating a team of engineers to test a new upgrade to the company’s gas turbines. “I set up the test bench, understanding how to instrument the machine, collect data, and run the test,” Graffiedi remembers, “there was a lot you need to think about, from a little turbine blade with sensors on it to the location of safety exits on the test bench.”

The move toward nuclear engineering

As fun as the test engineering job was, Graffiedi started to crave more technical knowledge and wanted to pivot to science. As part of his exploration, he came across nuclear energy and, understanding it to be the future, decided to lean on his engineering background to apply to MIT NSE.

He found a fit in Professor Matteo Bucci’s group and decided to explore boiling and quenching. The move from science to engineering, and back to science, was now complete.

NASA, the primary sponsor of the research, is interested in preventing boiling of cryogenic fuels, because boiling leads to loss of fuel and the resulting vapor will need to be vented to avoid overpressurizing a fuel tank.

Graffiedi’s primary focus is on quenching, which will play an important role in refueling in space — and in the cooling of nuclear cores. When a cryogen is used to cool down a surface, it undergoes what is known as the Leidenfrost effect, which means it first forms a thin vapor film that acts as an insulator and prevents further cooling. To facilitate rapid cooling, it’s important to accelerate the collapse of the vapor film. Graffiedi is exploring the mechanics of the quenching process on a microscopic level, studies that are important for land and space applications.

Boiling can be used for yet another modern application: to improve the efficiency of cooling systems for data centers. The growth of data centers and electric transportation systems needs effective heat transfer mechanisms to avoid overheating. Immersion cooling using dielectric fluids — fluids that do not conduct electricity — is one way to do so. These fluids remove heat from a surface by leaning on the principle of boiling. For effective boiling, the fluid must overcome the Leidenfrost effect and break the vapor film that forms. The fluid must also have high critical heat flux (CHF), which is the maximum value of the heat flux at which boiling can effectively be used to transfer heat from a heated surface to a liquid. Because dielectric fluids have lower CHF than water, Graffiedi is exploring solutions to enhance these limits. In particular, he is investigating how high electric fields can be used to enhance CHF and even to use boiling as a way to cool electronic components in the absence of gravity. He published this research in Applied Thermal Engineering in June.

Beyond boiling

Graffiedi’s love of science and engineering shows in his commitment to teaching as well. He has been a teaching assistant for four classes at NSE, winning awards for his contributions. His many additional achievements include winning the Manson Benedict Award presented to an NSE graduate student for excellence in academic performance and professional promise in nuclear science and engineering, and a service award for his role as past president of the MIT Division of the American Nuclear Society.

Boston has a fervent Italian community, Graffiedi says, and he enjoys being a part of it. Fittingly, the MIT Italian club is called MITaly. When he’s not at work or otherwise engaged, Graffiedi loves Latin dancing, something he makes time for at least a couple of times a week. While he has his favorite Italian restaurants in the city, Graffiedi is grateful for another set of skills his parents gave him when was just 11: making perfect pizza and pasta.

The MIT Human Insight Collaborative (MITHIC) is a presidential initiative with a mission of elevating human-centered research and teaching and connecting scholars in the humanities, arts, and social sciences with colleagues across the Institute.

Since its launch in 2024, MITHIC has funded 31 projects led by teaching and research staff representing 22 different units across MIT. The collaborative is holding its annual event on Nov. 17. 

In this Q&A, Keeril Makan, associate dean in the MIT School of Humanities, Arts, and Social Sciences, and Maria Yang, interim dean of the MIT School of Engineering, discuss the value of MITHIC and the ways it’s accelerating new research and collaborations across the Institute. Makan is the Michael (1949) Sonja Koerner Music Composition Professor and faculty lead for MITHIC. Yang is the William E. Leonhard (1940) Professor in the Department of Mechanical Engineering and co-chair of MITHIC’s SHASS+ Connectivity Fund.

Q: You each come from different areas of MIT. Looking at MITHIC from your respective roles, why is this initiative so important for the Institute?

Makan: The world is counting on MIT to develop solutions to some of the world’s greatest challenges, such as artificial intelligence, poverty, and health care. These are all issues that arise from human activity, a thread that runs through much of the research we’re focused on in SHASS. Through MITHIC, we’re embedding human-centered thinking and connecting the Institute’s top scholars in the work needed to find innovative ways of addressing these problems.

Yang: MITHIC is very important to MIT, and I think of this from the point of view as an engineer, which is my background. Engineers often think about the technology first, which is absolutely important. But for that technology to have real impact, you have to think about the human insights that make that technology relevant and can be deployed in the world. So really having a deep understanding of that is core to MITHIC and MIT’s engineering enterprise.

Q: How does MITHIC fit into MIT’s broader mission? 

Makan: MITHIC highlights how the work we do in the School of Humanities, Arts, and Social Sciences is aligned with MIT’s mission, which is to address the world’s great problems. But MITHIC has also connected all of MIT in this endeavor. We have faculty from all five schools and the MIT Schwarzman College of Computing involved in evaluating MITHIC project proposals. Each of them represent a different point of view and are engaging with these projects that originate in SHASS, but actually cut across many different fields. Seeing their perspectives on these projects has been inspiring.

Yang: I think of MIT’s main mission as using technology and many other things to make impact in the world, especially social impact. The kind of interdisciplinary work that MITHIC catalyzes really enables all of that work to happen in a new and profound way. The SHASS+ Connectivity Fund, which connects SHASS faculty and researchers with colleagues outside of SHASS, has resulted in collaborations that were not possible before. One example is a project being led by professors Mark Rau, who has a shared appointment between Music and Electrical Engineering and Computer Science, and Antoine Allanore in Materials Science and Engineering. The two of them are looking at how they can take ancient unplayable instruments and recreate them using new technologies for scanning and fabrication. They’re also working with the Museum of Fine Arts, so it’s a whole new type of collaboration that exemplifies MITHIC. 

Q: What has been the community response to MITHIC in its first year?

Makan: It’s been very strong. We found a lot of pent-up demand, both from faculty in SHASS and faculty in the sciences and engineering. Either there were preexisting collaborations that they could take to the next level through MITHIC, or there was the opportunity to meet someone new and talk to someone about a problem and how they could collaborate. MITHIC also hosted a series of Meeting of the Minds events, which are a chance to have faculty and members of the community get to know one another on a certain topic. This community building has been exciting, and led to an overwhelming number of applications last year. There has also been significant student involvement, with several projects bringing on UROPs [Undergraduate Research Opportunities Program projects] and PhD students to help with their research. MITHIC gives a real morale boost and a lot of hope that there is a focus upon building collaborations at MIT and on not forgetting that the world needs humanists, artists, and social scientists.

Yang: One faculty member told me the SHASS+ Connectivity Fund has given them hope for the kind of research that we do because of the cross collaboration. There’s a lot of excitement and enthusiasm for this type of work.

Q: The SHASS+ Connectivity Fund is designed to support interdisciplinary collaborations at MIT. What’s an example of a SHASS+ project that’s worked particularly well? 

Makan: One exciting collaboration is between professors Jörn Dunkel in Mathematics and In Song Kim in Political science. In Song is someone who has done a lot of work on studying lobbying and its effect upon the legislative process. He met Jörn, I believe, at one of MIT’s daycare centers, so it’s a relationship that started in a very informal fashion. But they found they actually had ways of looking at math and quantitative analysis that could complement one another. Their work is creating a new subfield and taking the research in a direction that would not be possible without this funding.

Yang: One of the SHASS+ projects that I think is really interesting is between professors Marzyeh Ghassemi in Electrical Engineering and Computer Science and Esther Duflo in Economics. The two of them are looking at how they can use AI to help health diagnostics in low-resource global settings, where there isn’t a lot of equipment or technology to do basic health diagnostics. They can use handheld, low-cost equipment to do things like predict if someone is going to have a heart attack. And they are not only developing the diagnostic tool, but evaluating the fairness of the algorithm. The project is an excellent example of using a MITHIC grant to make impact in the world.

Q: What has been MITHIC’s impact in terms of elevating research and teaching within SHASS?

Makan: In addition to the SHASS+ Connectivity Fund, there are two other possibilities to help support both SHASS research as well as educational initiatives: the Humanities Cultivation Fund and the SHASS Education Innovation Fund. And both of these are providing funding in excess of what we normally see within SHASS. It both recognizes the importance of the work of our faculty and it also gives them the means to actually take ideas to a much further place. 

One of the projects that MITHIC is helping to support is the Compass Initiative. Compass was started by Lily Tsai, one of our professors in Political Science, along with other faculty in SHASS to create essentially an introductory class to the different methodologies within SHASS. So we have philosophers, music historians, etc., all teaching together, all addressing how we interact with one another, what it means to be a good citizen, what it means to be socially aware and civically engaged. This is a class that is very timely for MIT and for the world. And we were able to give it robust funding so they can take this and develop it even further. 

MITHIC has also been able to take local initiatives in SHASS and elevate them. There has been a group of anthropologists, historians, and urban planners that have been working together on a project called the Living Climate Futures Lab. This is a group interested in working with frontline communities around climate change and sustainability. They work to build trust with local communities and start to work with them on thinking about how climate change affects them and what solutions might look like. This is a powerful and uniquely SHASS approach to climate change, and through MITHIC, we’re able to take this seed effort, robustly fund it, and help connect it to the larger climate project at MIT. 

Q: What excites you most about the future of MITHIC at MIT?

Yang: We have a lot of MIT efforts that are trying to break people out of their disciplinary silos, and MITHIC really is a big push on that front. It’s a presidential initiative, so it’s high on the priority list of what people are thinking about. We’ve already done our first round, and the second round is going to be even more exciting, so it’s only going to gain in force. In SHASS+, we’re actually having two calls for proposals this academic year instead of just one. I feel like there’s still so much possibility to bring together interdisciplinary research across the Institute.

Makan: I’m excited about how MITHIC is changing the culture of MIT. MIT thinks of itself in terms of engineering, science, and technology, and this is an opportunity to think about those STEM fields within the context of human activity and humanistic thinking. Having this shift at MIT in how we approach solving problems bodes well for the world, and it places SHASS as this connective tissue at the Institute. It connects the schools and it can also connect the other initiatives, such as manufacturing and health and life sciences. There’s an opportunity for MITHIC to seed all these other initiatives with the work that goes on in SHASS.

As batteries have gotten cheaper and more powerful, they have enabled the electrification of everything from vehicles to lawn equipment, power tools, and scooters. But electrifying homes has been a slower process. That’s because switching from gas appliances often requires ripping out drywall, running new wires, and upgrading the electrical box.

Now the startup Copper, founded by Sam Calisch SM ’14, PhD ’19, has developed a battery-equipped kitchen range that can plug into a standard 120-volt wall outlet. The induction range features a lithium iron phosphate battery that charges when energy is cheapest and cleanest, then delivers power when you’re ready to cook.

“We’re making ‘going electric’ like an appliance swap instead of a construction project,” says Calisch. “If you have a gas stove today, there is almost certainly an outlet within reach because the stove has an oven light, clock, or electric igniters. That’s big if you’re in a single-family home, but in apartments it’s an existential factor. Rewiring a 100-unit apartment building is such an expensive proposition that basically no one’s doing it.”

Copper has shipped about 1,000 of its battery-powered ranges to date, often to developers and owners of large apartment complexes. The company also has an agreement with the New York City Housing Authority for at least 10,000 units.

Once installed, the ranges can contribute to a distributed, cleaner, and more resilient energy network. In fact, Copper recently piloted a program in California to offer cheap, clean power to the grid from its home batteries when it would otherwise need to fire up a gas-powered plant to meet spiking electricity demand.

“After these appliances are installed, they become a grid asset,” Calisch says. “We can manage the fleet of batteries to help provide firm power and help grids deliver more clean electricity. We use that revenue, in turn, to further drive down the cost of electrification.”

Finding a mission

Calisch has been working on climate technologies his entire career. It all started at the clean technology incubator Otherlab that was founded by Saul Griffith SM ’01, PhD ’04.

“That’s where I caught the bug for technology and product development for climate impact,” Calisch says. “But I realized I needed to up my game, so I went to grad school in [MIT Professor] Neil Gershenfeld’s lab, the Center for Bits and Atoms. I got to dabble in software engineering, mechanical engineering, electrical engineering, mathematical modeling, all with the lens of building and iterating quickly.”

Calisch stayed at MIT for his PhD, where he worked on approaches in manufacturing that used fewer materials and less energy. After finishing his PhD in 2019, Calisch helped start a nonprofit called Rewiring America focused on advocating for electrification. Through that work, he collaborated with U.S. Senate offices on the Inflation Reduction Act.

The cost of lithium ion batteries has decreased by about 97 percent since their commercial debut in 1991. As more products have gone electric, the manufacturing process for everything from phones to drones, robots, and electric vehicles has converged around an electric tech stack of batteries, electric motors, power electronics, and chips. The countries that master the electric tech stack will be at a distinct manufacturing advantage.

Calisch started Copper to boost the supply chain for batteries while contributing to the electrification movement.

“Appliances can help deploy batteries, and batteries help deploy appliances,” Calisch says. “Appliances can also drive down the installed cost of batteries.”

The company is starting with the kitchen range because its peak power draw is among the highest in the home. Flattening that peak brings big benefits. Ranges are also meaningful: It’s where people gather around and cook each night. People take pride in their kitchen ranges more than, say, a water heater.

Copper’s 30-inch induction range heats up more quickly and reaches more precise temperatures than its gas counterpart. Installing it is as easy as swapping a fridge or dishwasher. Thanks to its 5-kilowatt-hour battery, the range even works when the power goes out.

“Batteries have become 10 times cheaper and are now both affordable and create tangible improvements in quality of life,” Calisch says. “It’s a new notion of climate impact that isn’t about turning down thermostats and suffering for the planet, it’s about adopting new technologies that are better.”

Scaling impact

Calisch says there’s no way for the U.S. to maintain resilient energy systems in the future without a lot of batteries. Because of power transmission and regulatory limitations, those batteries can’t all be located out on the grid.

“We see an analog to the internet,” Calisch says. “In order to deliver millions of times more information across the internet, we didn’t add millions of times more wires. We added local storage and caching across the network. That’s what increased throughput. We’re doing the same thing for the electric grid.”

This summer, Copper raised $28 million to scale its production to meet growing demand for its battery equipped appliances. Copper is also working to license its technology to other appliance manufacturers to help speed the electric transition.

“These electric technologies have the potential to improve people’s lives and, as a byproduct, take us off of fossil fuels,” Calisch says. “We’re in the business of identifying points of friction for that transition. We are not an appliance company; we’re an energy company.”

Looking back, Calisch credits MIT with equipping him with the knowledge needed to run a technical business.

“My time at MIT gave me hands-on experience with a variety of engineering systems,” Calisch. “I can talk to our embedded engineering team or electrical engineering team or mechanical engineering team and understand what they’re saying. That’s been enormously useful for running a company.”

He adds: “I also developed an expansive view of infrastructure at MIT, which has been instrumental in launching Copper and thinking about the electrical grid not just as wires on the street, but all of the loads in our buildings. It’s about making homes not just consumers of electricity, but participants in this broader network.”