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

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

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

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

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

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

Overcoming the limits

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

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

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

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

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

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

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

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

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

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

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

Leveraging magnetism

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

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

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

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

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

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

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

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

Amid the hum of milking equipment and the shuffle of cow hooves, PhD student Audrey Parker and her collaborators pull a wagon through a dusty path of a dairy barn, measuring an invisible greenhouse gas drifting through the air. Most engineering students wouldn’t expect their graduate research to take them to a dairy farm, but for Parker, this is where some of the most impactful climate solutions are hiding in plain sight.

The scene was part of the civil and environmental engineering student’s PhD work exploring advanced yet practical technologies to mitigate methane emissions. Such emissions are much more effective at trapping heat in the atmosphere than carbon dioxide. Dairy farms are a major source of methane, and Parker’s wagon carried sensors to measure methane concentrations.

Now in her fourth year in the lab of Professor Desirée Plata, Parker looks forward to visiting such farms. When she’s not taking measurements, she can look across the rolling fields and think of home.

Parker grew up in Boise, Idaho. Her childhood was filled with backpacking trips, skiing, horseback riding, and otherwise enjoying what her natural surroundings had to offer.

“Growing up, we were always outside,” she says. “I knew how to cast a fly rod before I knew how to ride a bike.”

That experience motivated Parker to pursue studies related to preserving the environment she loved. She attended Boise State University as an undergraduate, where she studied sustainable materials development under the mentorship of Assistant Dean Paul Davis. In the summer before her senior year, she was accepted to the MIT Summer Research Program (MSRP), which equips students for graduate school by bringing them to MIT to conduct cutting-edge research. That’s where she began working with Plata, MIT’s Distinguished Climate and Energy Professor.

“They do a great job bringing in people of different backgrounds,” Parker says. “It wasn’t until I started working with Desirée that I started applying materials science as a tool to reduce greenhouse gas emissions. That was a profound insight.”

Parker graduated Boise State University as a Top Ten Scholar, the highest academic honor granted to graduating seniors, before driving across the country to begin her studies at MIT. She decided to devote her PhD to exploring methane mitigation strategies, building on her experience from MSRP.

Her focus is on methane emissions from two sources: air being vented from coal mines, and dairy farms. Those two areas alone account for a large portion of human-driven methane emissions. Both sources are dilute compared to the average oil or gas well, which makes the methane challenging to capture and convert into less environmentally harmful molecules.

Parker also wanted to work with community members in the field during her PhD to ensure whatever technical solutions she developed are practical enough to implement at scale.

“Desirée’s approach is to make sure industry is aware of affordable and sustainable ways to remove methane from their operations, while also incorporating the nuanced expertise stakeholders offer,” Parker says. “I appreciate that she is focused on not just doing work for the chapter of a PhD thesis, but also making our work lead to real-world change.”

Parker’s research explores both quantifying methane at emission sources and designing technologies that could be used to convert methane into carbon dioxide, a molecule with significantly less climate warming potential.

“Methane naturally converts into carbon dioxide over the course of about 12 years in the atmosphere,” Parker explains. “The technology we work on simply speeds up this natural process to achieve near-term climate benefits.”

The main technology Parker studies is a catalyst made from zeolites, an abundant and inexpensive mineral with complex internal structures like honeycombs. Parker dopes the zeolites with copper and explores ways to apply external heat to facilitate complete methane conversion.

Parker and her collaborators assess the durability of the material and its performance under different conditions. Recognizing that real-world deployment environments can often be difficult to replicate in lab, they test catalyst performance in operating dairy farms. In a 2025 paper, she analyzed the use of thermal energy to sustain methane combustion in catalyst materials, detailing when the approach actually brings net-climate benefits.

“If your methane concentrations are low and you’re having to provide so much energy into your system, you could become climate-harmful, but there’s also a context where it’s beneficial,” Parker explains. “Understanding where that trade-off occurs is critical to making sure your mitigation technologies are having the benefits you’re anticipating.”

That kind of systems-level thinking is necessary to understand the long-term impacts of interconnected climate systems.

“It lays a framework that other people can use for their mitigation technologies,” Parker says. “There are trade-offs with every technology, and being transparent about that is important. I think as academics it’s easy to get tunnel vision based on our research. There’s such limited funding for mitigation technologies overall and so making sure those few funding dollars are allocated appropriately is critical for achieving our climate goals.”

Some of Parker’s research findings have informed the design of a pilot-scale methane mitigation system in a coal mine, although she hasn’t gotten a chance to visit it just yet.

Outside of her research, Parker co-chairs the MIT Congressional Visit Days, a program run by the Science Policy Initiative that sends MIT students to Washington to meet with lawmakers and advocate for science-based policies.

“On-the-Hill advocacy teaches you about the policy landscape in unparalleled ways,” Parker says. “Those conversations you have with lawmakers can drive transformational change to bridge the gap between science and policy. It is our job as scientists to communicate our findings clearly so policymakers can design regulations that enable effective solutions.”

This spring, Parker is also leading a workshop for the MIT Climate and Sustainability Consortium around financing the voluntary carbon market. Here, she plans to leverage industry insights to catalyze private capital at the scale needed to meet our climate goals.

Parker also still gets plenty of outdoor time, hiking outside Boston and skiing a bit, though she says the New England ski mountains don’t compare to those out west.

Parker, who expects to complete her PhD next year, says it’s gratifying to be able to devote her research to protecting the environment she loves so much.

“For me it’s about preserving the world I grew up in,” Parker says. “Especially in Idaho, where communities are experiencing more frequent wildfires and more intense droughts. As a child, the natural world provided so much wonder. Today, that same sense of wonder is what drives me to protect it.”

The Financial Times has placed MIT Sloan School of Management at the top of its recently released 2026 Global MBA Ranking. It is the school’s first time gaining the No. 1 spot in the list.

In its announcement of the rankings, the publication noted MIT’s school of management tops the list “at a time of sharpening focus from students on the importance of technology, including artificial intelligence, as they prepare for disruptions in the workplace.”

Global education editor Andrew Jack said in the Financial Times News Briefing podcast that MIT is “very much at the center of the tech revolution that we are seeing.” He added, “there’s no question that we’re talking more and more about artificial intelligence and expertise around some of the technical skills related and notably how you might apply AI in the workplace. That certainly reflects both its technical and engineering computer science skills historically. And [MIT Sloan] is doing a lot with those other departments in the university. So I think that says something very much about how the wider job market and the aspirations of students are evolving.”

“MIT Sloan operates at the intersection of management and technology,” says Richard Locke, the John C Head III Dean of the MIT Sloan School of Management. “Our students and alumni are employing artificial intelligence to solve complex problems in the world and across industries. At MIT Sloan, we focus on doing that work in a way that centers human capabilities, ensuring artificial intelligence extends what humans can do to improve organizations and the world.”

To determine its rankings, the Financial Times considers 21 criteria. Eight of those — accounting for 56 percent of the ranking’s weight — are determined by surveying alumni three years after they have completed their MBA program. School data are used for 34 percent percent of the rank. The remaining 10 percent measures how often full-time faculty publish in top journals.

MIT Sloan ranked fourth for its alumni network, which measures how effectively alumni support one another through career advice, internships, job opportunities, and recruiting efforts. 

“This ranking underscores the strength of our global alumni community,” says Kathy Hawkes, senior associate dean of external engagement. “'Sloanies Helping Sloanies' isn’t just a phrase — it’s a lived experience. Our 31,000 alumni actively open doors, share expertise, and invest in each other’s success.”

MIT researchers have discovered that two common genetic mutations that cause Rett syndrome each set off a molecular chain of events that compromises the structural integrity of developing brain blood vessels, making them leaky. The study traces the problem to overexpression of a particular microRNA (miRNA-126-3p), and shows that tamping down the miRNA’s levels helps to rescue the vascular defect.

Rett syndrome is a severe developmental disorder affecting both the brain and body. It is caused by various mutations in the widely expressed MECP2 gene, but the first symptoms don’t become apparent until affected children (mostly girls) reach 2-3 years of age. Because that’s a critical time in development for the brain’s blood vessels, neuroscientists in The Picower Institute for Learning and Memory at MIT embarked on a study to model how two common but distinct MeCP2 mutations may affect vascular development and contribute to the disease’s profound neurological pathology.

To conduct the research published recently in Molecular Psychiatry, lead author Tatsuya Osaki and senior author Mriganka Sur developed advanced human tissue cultures to model vessel development, with and without the MeCP2 mutations. The cultures not only enabled them to model and closely observe how the mutations affected the vessels, but also allowed them to molecularly dissect the problems they observed and then to test an intervention that helped.

“A role for microRNAs in Rett syndrome has been shown, but now demonstrating that miRNA-126-3p is actually downstream of MeCP2 and directly implicated in the endothelial cell dysfunction is an important piece of the Rett syndrome puzzle,” says Sur, the Newton Professor of Neuroscience in the Picower Institute and MIT’s Department of Brain and Cognitive Sciences.

Building vessels and spotting leaks

Building on years of tissue engineering experience, including time as a postdoc in the lab of co-author and MIT mechanical engineering and biological engineering Professor Roger D. Kamm, Osaki built “3-dimensional microvascular networks” using human induced pluripotent stem cells (iPS cells) donated by patients with Rett syndrome. The donated cells were induced to become stem cells, and then endothelial cells (the backbone of blood vessels). Embedded in a gel and mixed with fibroblast cells, the endothelial cells self-assembled into networks of tubes, which Osaki then hooked up to microfluidics to provide circulation.

One set of the cultures harbored the mutation R306C. Osaki created a control microvasculature that was genetically identical except that it did not have the mutation. Another set of the cultures had the R168X mutation. And again, Osaki paired that with control culture that was identical except for the mutation using CRISPR.

The research team chose these two mutations because they are each relatively common but affect the MeCP2 gene differently, Sur says. The finding that each of these distinct Rett-causing mutations ultimately led to upregulating miRNA-126-3p and undermining blood vessel integrity suggests that vascular problems are indeed a central feature of the disease.

“There is something common across these mutations,” Sur says.

In particular, lab tests showed that the vessels harboring either mutation showed reduced expression of a protein called ZO-1, which is critical for ensuring that the junctions among endothelial cells in blood vessels form a tight seal (like the grout in a tile floor). ZO-1 also didn’t localize to those junctions as well. Sure enough, further tests showed that the Rett-mutation vessel cultures were relatively leaky compared to the controls.

Similar deficiencies were evident in another cell culture the team created, in which they added astrocyte cells to even more closely simulate the blood-brain-barrier (BBB), which tightly regulates what can go in or out of blood vessels and into the brain. BBB problems are widely suspected of contributing to neurodegenerative diseases such as Alzheimer’s, Huntington’s, and ALS and frontotemporal dementia.

To gain some insight into how the vascular problems might undermine neural function in Rett syndrome, the researchers exposed neurons to medium from their Rett vasculature cultures. Those nerve cells showed reduced electrical activity, a possible sign that secretions from the Rett endothelial cells disrupted the neurons.

Catching a culprit

Generally speaking, the role of MeCP2 is to repress the expression of other genes. The scientists’ expectation, therefore, was that when MeCP2 is compromised by mutations the result would be overexpression of many genes. Yet ZO-1 was downregulated. Something had to account for that and miRNAs were a suspect, Osaki says, because they function as regulators of gene expression.

“That’s why we hypothesized that we should have some mediator between the MeCP2 mutation and ZO-1 downregulation and the BBB permeability increase,” Osaki says. “We focused on the microRNAs.”

Indeed, by profiling miRNAs in the Rett cultures and the controls, the scientists found that miRNA-126-3p was overexpressed. And by sequencing RNA, the team identified more molecular pathways needed to support vascular integrity that were dysregulated in the Rett cultures.

While the sequencing and profile associated miRNA-126-3p upregulation with the altered molecular chain of events, Osaki and Sur sought more definitive proof. To obtain it, they treated the Rett-mutation cultures with an “antisense” — a molecule that reduces miRNA-126-3p levels. Doing that resulted in an increase in ZO-1 expression and a partial restoration of endothelial cell barrier function — meaning less leakiness — in the vessel cultures. Knocking down the miRNA’s expression also restored the molecular pathways the scientists were tracking to more healthy states.

It turns out that there is a drug that inhibits miR-126 called miRisten that is undergoing clinical testing for leukemia. Osaki and Sur say they are planning on administering it to mice modeling Rett syndrome to see if it helps them.

In addition to Osaki, Sur, and Kamm, the paper’s co-authors are Zhengpeng Wan, Koji Haratani, Ylliah Jin, Marco Campisi, and David Barbie.

Funding for the study came from sources including the National Institutes of Health, a MURI grant, The Freedom Together Foundation, and the Simons Center for the Social Brain.

Geothermal energy, a clean, continuous energy source accessible in many locations, has been slow to catch on. Nearly 2,000 years ago, the Romans made extensive use of geothermal energy — heat from the Earth — including at the spa complex at present-day Bath, England. Electricity was first produced from geothermal sources in the early 1900s in Italy. In the United States, the Geysers geothermal field in California began generating electricity at scale in 1960, and routinely produces more than 725 megawatts of baseload power today. 

According to the International Energy Agency (IEA), geothermal energy still supplies less than 1 percent of global electricity demand, although countries like Kenya (more than 40 percent of electricity generation) and Iceland (nearly 30 percent of electricity and 90 percent of the heating) have seen widespread adoption.

In recent years, technological advances, an influx of private capital, and shifting energy and environmental policies have driven renewed interest in expanding development of geothermal energy. If project costs continue to decline, the IEA predicts that geothermal energy could meet 15 percent of the growth in global electricity demand between 2024 and 2050. Many countries, including the United States, Indonesia, New Zealand, and Turkey, are prioritizing an expansion of geothermal energy as part of their broader energy strategies.

Achieving large-scale electricity generation from geothermal sources will depend on a significant expansion of so-called next-generation geothermal. This refers to tapping heat from source rocks at temperatures of 100 degrees Celsius to more than 400 C, often at depths of several kilometers below the surface. Last month, U.S. Congressional Rep. Jake Auchincloss (D-MA) and Rep. Mark Amodei (R-NV) introduced bipartisan legislation to promote research, testing, and development of one type of next-generation geothermal energy known as superhot rock.

Geothermal energy at MIT

Through its leadership in producing the influential 2006 “The Future of Geothermal Energy” report led by former MIT professor Jeff Tester, MIT and the predecessor of the MIT Energy Initiative (MITEI) played an important role in national geothermal strategy two decades ago. In 2008, researchers at the Plasma Science and Fusion Center (PSFC) invented millimeter-wave drilling with support from one of the first MITEI seed innovation grants. The technology, which could be particularly useful for geothermal installations in superhot and deep rock, is being commercialized by MIT spinout Quaise Energy.

MITEI is sponsoring next-generation geothermal projects through its Future Energy Systems Center. A project led by MITEI Research Scientist Pablo Duenas-Martinez focuses on the techno-economics of electricity generation from a geothermal plant co-located with a data center, a timely topic given the proliferation of data center power purchase agreements for electricity generated by geothermal energy. MITEI’s March 4 Spring Symposium focused on next-geothermal energy for the generation of firm power, and many of the leading exploration, drilling, reservoir development, and advanced technology companies working in this area sent panelists and speakers. On March 5, MITEI collaborated with the Clean Air Task Force (CATF) to co-host the GeoTech Summit, which explored accelerating technology development for and investment in next-generation geothermal.

To prepare for the recent symposium, MITEI organized a geothermal bootcamp during MIT’s Independent Activities Period (IAP) that introduced more than 40 members of the MIT community to geothermal basics, key technologies, and related MIT research. Carolyn Ruppel, MITEI’s deputy director of science and technology and the organizer of the IAP bootcamp and Spring Symposium, says, “MITEI’s member companies, which represent leading voices on energy, power generation, infrastructure, heavy industry, and digital technology, are increasingly approaching us about their interest in next-generation geothermal. There is also good momentum building across MIT, ranging from projects at the Earth Resources Laboratory to the millimeter-wave testbed being developed by PSFC and its MIT collaborators, individual projects in academic departments, and of course the work MITEI has been funding.”

Geothermal basics

Temperatures a few tens of meters below the ground are typically stable year-round. In some locations, these temperatures are warmer than the surface in winter and cooler in summer, making it possible to use geothermal heat pumps to moderate temperatures in buildings throughout the year. Overlooking the Charles River, Boston University’s 19-story Center for Computing and Data Science meets an estimated 90 percent of its heating and cooling needs using this kind of geothermal system. At the scale of large institutions or whole towns, thermal networks, district heating, and other approaches can efficiently supply heat from shallow geothermal sources without producing greenhouse gas emissions.

Tapping hotter and usually deeper geothermal sources could generate large amounts of electricity for decades at a single site. Next-generation geothermal is the term applied to these higher-temperature systems developed using enhanced, advanced, and superhot technologies. Enhanced geothermal refers to circulating fluids through engineered fracture systems in deep, dry rock with relatively low native permeability. Advanced geothermal adopts a closed loop approach, in which a working fluid is heated by circulating it through pipes embedded in the subsurface. Superhot geothermal, which is in its infancy, will likely use enhanced geothermal technology to circulate supercritical water through rock at almost 400 C.   

Next-generation geothermal

Drill deep enough and higher-temperature resources are nearly ubiquitous beneath the continents, but early-stage development must focus on the most promising sites, where the methods and technologies to routinely reach these hotter rocks can be tested and refined. Locations like Iceland and the southwestern U.S. state of Nevada, where tectonic plates are separating or the Earth’s outer layer is thinning, have hotter temperatures closer to the surface than areas like the northeastern United States, where the Earth’s crust is old, thick, and cooler. Even in the southwestern United States, though, reaching the high temperatures required for generating electricity via geothermal systems will require routinely drilling to depths of greater than 4 kilometers in crystalline rock. This is significantly more challenging than drilling in the sedimentary basins that host most of the world’s oil and gas reserves. 

For a location to be suitable for a next-generation geothermal installation requires not only heat, but also a fluid (usually water) to carry the heat. Water circulated through the rock formation to extract heat can be present naturally or brought from elsewhere and injected into the reservoir. This type of system also requires connected permeability such as an engineered fracture network oriented to prevent significant fluid losses and to channel fluid toward the extraction well. Closed-loop (advanced) systems replace the freely circulating water with a working fluid that has favorable thermal characteristics and that is confined in piping.

Various geophysical methods are used to find sites with sufficient heat within a few kilometers of the surface, a prerequisite for their development as next-generation geothermal installations. Apart from direct measurements of temperatures in test boreholes, electrical resistivity and magnetotelluric surveys are among the most useful for inferring subsurface temperature regimes. Both techniques infer the electrical conductivity structure beneath the ground, permitting the identification of relatively warmer and more permeable rocks.

Drilling is often the most time-consuming and expensive part of preparing a site for a geothermal plant. This is particularly true for next-generation geothermal, where the targets can be deep, or the system design may require large-scale horizontal drilling. Over the past few years, numerous innovations have increased drilling rates and attainable depths and temperatures and also lowered costs. Nonetheless, even with high-quality geophysical surveys, “you may spend $10 million on an exploratory well and find no heat,” says Andrew Inglis, the geothermal channel venture builder at MIT Proto Ventures. 

Superhot geothermal, a next-generation geothermal approach that is advancing rapidly, presents special challenges. The metal drilling tools, the rocks in the formation, and circulating fluids all behave differently at temperatures of several hundred degrees, and standard practices, materials, and sensors must be significantly modified to tolerate the tough conditions. Once temperatures exceed 374 C in a borehole even ~1 km deep, water reaches a supercritical state. This presents substantial advantages for extracting heat from the formation, but introduces the specter of rapid metal corrosion and precipitation of salts and silica that can quickly foul a borehole. Researchers are investigating substitution of supercritical carbon dioxide for water as a working fluid for superhot geothermal.

MIT innovations advancing next-generation geothermal

The millimeter-wave drilling technology invented at PSFC and being commercialized by Quaise Energy is the highest-profile next-generation geothermal innovation to emerge from MIT so far. Millimeter-wave technology uses microwave energy to vaporize rock and could prove to be several times faster than conventional drilling. PSFC and a multidisciplinary MIT team are devising a dedicated laboratory to study how millimeter-wave drilling interacts with crystalline rock at realistic pressure and temperature conditions, and to test improvements to the existing technology. Steve Wukitch, interim director and principal research scientist at PSFC, notes that “the facility we are building at MIT will allow us to test samples 500 times larger than is currently possible. This is an important step for investigating technologies that could unlock superhot geothermal energy."

MIT Proto Ventures, which focuses on creating startups based on technology invented at MIT, currently hosts a dedicated geothermal energy channel led by Inglis. Since arriving at MIT in late 2024, Inglis has identified inventions and research that could advance next-generation geothermal from disciplines as disparate as mechanical and materials engineering, earth sciences, and chemistry. Examples of technologies originating with MIT researchers include sensors that measure micro-cracking in high-temperature rock, advanced metal alloys that could handle superhot fluids at a fraction of the cost of titanium, and anti-fouling coatings to protect pipes from the caustic geofluids common in hot, deep systems.

MITEI Spring Symposium

At the recent MITEI Spring Symposium, these MIT innovators introduced their technology to MITEI member companies in a session led by Inglis. Wukitch, who moderated a panel on advanced drilling, described the planned millimeter-wave testbed, and Duenas-Martinez led a panel on power generation and storage. Terra Rogers, director for superhot rock geothermal energy at the CATF and the organizer of the joint CATF-MITEI GeoTech Summit on March 5, led a discussion of international and U.S. policies and the regulatory environment for expansion of next-generation geothermal. 

Poster presenters included MIT graduate students and researchers, MIT’s D-Lab, and the Geo@MIT geothermal-focused MIT student group, which was recognized with a 2024 bonus award by the U.S. Department of Energy’s Geothermal Technologies Office in the nationwide EnergyTech University Prize competition.  

MIT neuroscientists have figured out how the brain is able to focus on a single voice among a cacophony of many voices, shedding light on a longstanding neuroscientific phenomenon known as the cocktail party problem.

This attentional focus becomes necessary when you’re in any crowded environment, such as a cocktail party, with many conversations going on at once. Somehow, your brain is able to follow the voice of the person you’re talking to, despite all the other voices that you’re hearing in the background.

Using a computational model of the auditory system, the MIT team found that amplifying the activity of the neural processing units that respond to features of a target voice, such as its pitch, allows that voice to be boosted to the forefront of attention.

“That simple motif is enough to cause much of the phenotype of human auditory attention to emerge, and the model ends up reproducing a very wide range of human attentional behaviors for sound,” says Josh McDermott, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines, and the senior author of the study.

The findings are consistent with previous studies showing that when people or animals focus on a specific auditory input, neurons in the auditory cortex that respond to features of the target stimulus amplify their activity. This is the first study to show that extra boost is enough to explain how the brain solves the cocktail party problem.

Ian Griffith, a graduate student in the Harvard Program in Speech and Hearing Biosciences and Technology, who is advised by McDermott, is the lead author of the paper. MIT graduate student R. Preston Hess is also an author of the paper, which appears today in Nature Human Behavior.

Modeling attention

Neuroscientists have been studying the phenomenon of selective attention for decades. Many studies in people and animals have shown that when focusing on a particular stimulus like the sound of someone’s voice, neurons that are tuned to features of that voice — for example, high pitch — amplify their activity.

When this amplification occurs, neurons’ firing rates are scaled upward, as though multiplied by a number greater than one. It has been proposed that these “multiplicative gains” allow the brain to focus its attention on certain stimuli. Neurons that aren’t tuned to the target feature exhibit a corresponding reduction in activity.

“The responses of neurons tuned to features that are in the target of attention get scaled up,” Griffith says. “Those effects have been known for a very long time, but what’s been unclear is whether that effect is sufficient to explain what happens when you’re trying to pay attention to a voice or selectively attend to one object.”

This question has remained unanswered because computational models of perception haven’t been able to perform attentional tasks such as picking one voice out of many. Such models can readily perform auditory tasks when there is an unambiguous target sound to identify, but they haven’t been able to perform those tasks when other stimuli are competing for their attention.

“None of our models has had the ability that humans have, to be cued to a particular object or a particular sound and then to base their response on that object or that sound. That’s been a real limitation,” McDermott says.

In this study, the MIT team wanted to see if they could train models to perform those types of tasks by enabling the model to produce neuronal activity boosts like those seen in the human brain.

To do that, they began with a neural network that they and other researchers have used to model audition, and then modified the model to allow each of its stages to implement multiplicative gains. Under this architecture, the activation of processing units within the model can be boosted up or down depending on the specific features they represent, such as pitch.

To train the model, on each trial the researchers first fed it a “cue”: an audio clip of the voice that they wanted the model to pay attention to. The unit activations produced by the cue then determined the multiplicative gains that were applied when the model heard a subsequent stimulus.

“Imagine the cue is an excerpt of a voice that has a low pitch. Then, the units in the model that represent low pitch would get multiplied by a large gain, whereas the units that represent high pitch would get attenuated,” Griffith says.

Then, the model was given clips featuring a mix of voices, including the target voice, and asked to identify the second word said by the target voice. The model activations to this mixture were multiplied by the gains that resulted from the previous cue stimulus. This was expected to cause the target voice to be “amplified” within the model, but it was not clear whether this effect would be enough to yield human-like attentional behavior.

The researchers found that under a variety of conditions, the model performed very similarly to humans, and it tended to make errors similar to those that humans make. For example, like humans, it sometimes made mistakes when trying to focus on one of two male voices or one of two female voices, which are more likely to have similar pitches.

“We did experiments measuring how well people can select voices across a pretty wide range of conditions, and the model reproduces the pattern of behavior pretty well,” Griffith says.

Effects of location

Previous research has shown that in addition to pitch, spatial location is a key factor that helps people focus on a particular voice or sound. The MIT team found that the model also learned to use spatial location for attentional selection, performing better when the target voice was at a different location from distractor voices.

The researchers then used the model to discover new properties of human spatial attention. Using their computational model, the researchers were able to test all possible combinations of target locations and distractor locations, an undertaking that would be hugely time-consuming with human subjects.

“You can use the model as a way to screen large numbers of conditions to look for interesting patterns, and then once you find something interesting, you can go and do the experiment in humans,” McDermott says.

These experiments revealed that the model was much better at correctly selecting the target voice when the target and distractor were at different locations in the horizontal plane. When the sounds were instead separated in the vertical plane, this task became much more difficult. When the researchers ran a similar experiment with human subjects, they observed the same result.

“That was just one example where we were able to use the model as an engine for discovery, which I think is an exciting application for this kind of model,” McDermott says.

Another application the researchers are pursuing is using this kind of model to simulate listening through a cochlear implant. These studies, they hope, could lead to improvements in cochlear implants that could help people with such implants focus their attention more successfully in noisy environments.

The research was funded by the National Institutes of Health.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Q: What role can MIT play in this new era of planetary defense?

De Wit: The reality is that, given the occurrence rate of these smaller asteroids and the new observational capabilities now coming online — from the Rubin Observatory to space-based facilities like JWST — we expect that within the next decade we will identify a handful of decameter-scale objects whose trajectories place them on course to impact the Earth-Moon system within this century. At that point, society will face a very practical question: whether, and how, to respond. Because these are much smaller objects than the dinosaur-killing asteroids, the types of mitigation strategies that we may envision are different. This is also where I think MIT might have an important role to play in the development, design, and potentially even construction of cost-effective, rapid-response asteroid-mitigation strategies. To help organize that effort, we have begun bringing together researchers across the Institute through the Planetary Defense at MIT project, working closely with colleagues on the engineering side.

Teague: What I’m particularly excited about is the way we’ve managed to engage students at MIT in this research as well. We’ve really focused on the impactful research and the way we’re bridging departments and labs within MIT, and this has been a fantastic way to engage students with practical astronomy and research. Saverio has run an IAP [Independent Activities Period] course, and we’re also running a student observing lab with the Wallace Observatory, where we hire a cohort of students every semester, and they’re taught how to use these observatories remotely. They take the data, do the analysis, and this semester, we've got on the order of 10 undergraduate students that are going to be working throughout the semester to take these observations and help us build this observation pipeline.

It's great that here at MIT we’re not only pushing the forefront of the research, but we’re also training the next generation of astronomers that is going to come in and carry this project through and into the future.

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

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

Amos Winter: Bringing excitement to the classroom

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

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

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

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

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

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

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

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

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

Nickolai Zeldovich: Inspiring independent thinkers and future teachers

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Professor Arvind Satyanarayan, a computer scientist whose research develops tools for interactive data visualization and user interfaces, and Professor Graham Jones, an anthropologist whose research focuses on communication, created Humane UXD last summer with a grant from the MIT Morningside Academy for Design (MAD). The MIT MAD Design Curriculum Program provides funding for faculty to develop new classes or enhance existing classes using innovative pedagogical approaches that transcend departmental boundaries.

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

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

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

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

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

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

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

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

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

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

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

Team Pond

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

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

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

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

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

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

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

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

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

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

Team News Nest

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

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

Team M^3 (Multi-Agent Murder Mystery)

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

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

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

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

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

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

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

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

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

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

A scalable platform

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

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

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

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

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

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

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

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

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

Painting with light

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Tackling visual tasks

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

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

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

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

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

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

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

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

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

A flexible approach

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

20 years on: From classrooms to convention halls

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

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

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

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

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

Addressing tanking and gambling

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

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

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

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

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

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

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

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

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

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

The minnows got big

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

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

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

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

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

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

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

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

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

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

The limits of knowledge

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Q: What excited you about joining the MIT community?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Losing weight

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

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

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

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

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

A sinking sweetspot

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

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

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

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

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

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

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

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

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