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.

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Nature Climate Change, Published online: 19 March 2026; doi:10.1038/s41558-026-02596-y

Food loss and waste (FLW) is a major source of global GHG emissions, yet its drivers and mitigation potential remain understudied. By attributing FLW to techno-economic and misbehavioural drivers, this study shows misbehaviour dominates FLW emissions and offers substantial mitigation potential.

MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items.

Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot’s ability to reliably grasp and manipulate objects that are blocked from view.

This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

The researchers also introduced an expanded system that uses generative AI to accurately reconstruct an entire room, including all the furniture. The system utilizes wireless signals sent from one stationary radar, which reflect off humans moving in the space.  

This overcomes one key challenge of many existing methods, which require a wireless sensor to be mounted on a mobile robot to scan the environment. And unlike some popular camera-based techniques, their method preserves the privacy of people in the environment.

These innovations could enable warehouse robots to verify packed items before shipping, eliminating waste from product returns. They could also allow smart home robots to understand someone’s location in a room, improving the safety and efficiency of human-robot interaction.

“What we’ve done now is develop generative AI models that help us understand wireless reflections. This opens up a lot of interesting new applications, but technically it is also a qualitative leap in capabilities, from being able to fill in gaps we were not able to see before to being able to interpret reflections and reconstruct entire scenes,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of two papers on these techniques. “We are using AI to finally unlock wireless vision.”

Adib is joined on the first paper by lead author and research assistant Laura Dodds; as well as research assistants Maisy Lam, Waleed Akbar, and Yibo Cheng; and on the second paper by lead author and former postdoc Kaichen Zhou; Dodds; and research assistant Sayed Saad Afzal. Both papers will be presented at the IEEE Conference on Computer Vision and Pattern Recognition.

Surmounting specularity

The Adib Group previously demonstrated the use of millimeter wave (mmWave) signals to create accurate reconstructions of 3D objects that are hidden from view, like a lost wallet buried under a pile.

These waves, which are the same type of signals used in Wi-Fi, can pass through common obstructions like drywall, plastic, and cardboard, and reflect off hidden objects.

But mmWaves usually reflect in a specular manner, which means a wave reflects in a single direction after striking a surface. So large portions of the surface will reflect signals away from the mmWave sensor, making those areas effectively invisible.

“When we want to reconstruct an object, we are only able to see the top surface and we can’t see any of the bottom or sides,” Dodds explains.

The researchers previously used principles from physics to interpret reflected signals, but this limits the accuracy of the reconstructed 3D shape.

In the new papers, they overcame that limitation by using a generative AI model to fill in parts that are missing from a partial reconstruction.

“But the challenge then becomes: How do you train these models to fill in these gaps?” Adib says.

Usually, researchers use extremely large datasets to train a generative AI model, which is one reason models like Claude and Llama exhibit such impressive performance. But no mmWave datasets are large enough for training.

Instead, the researchers adapted the images in large computer vision datasets to mimic the properties in mmWave reflections.

“We were simulating the property of specularity and the noise we get from these reflections so we can apply existing datasets to our domain. It would have taken years for us to collect enough new data to do this,” Lam says.

The researchers embed the physics of mmWave reflections directly into these adapted data, creating a synthetic dataset they use to teach a generative AI model to perform plausible shape reconstructions.

The complete system, called Wave-Former, proposes a set of potential object surfaces based on mmWave reflections, feeds them to the generative AI model to complete the shape, and then refines the surfaces until it achieves a full reconstruction.

Wave-Former was able to generate faithful reconstructions of about 70 everyday objects, such as cans, boxes, utensils, and fruit, boosting accuracy by nearly 20 percent over state-of-the-art baselines. The objects were hidden behind or under cardboard, wood, drywall, plastic, and fabric.

Seeing “ghosts”

The team used this same approach to build an expanded system that fully reconstructs entire indoor scenes by leveraging mmWave reflections off humans moving in a room.

Human motion generates multipath reflections. Some mmWaves reflect off the human, then reflect again off a wall or object, and then arrive back at the sensor, Dodds explains.

These secondary reflections create so-called “ghost signals,” which are reflected copies of the original signal that change location as a human moves. These ghost signals are usually discarded as noise, but they also hold information about the layout of the room.

“By analyzing how these reflections change over time, we can start to get a coarse understanding of the environment around us. But trying to directly interpret these signals is going to be limited in accuracy and resolution.” Dodds says.

They used a similar training method to teach a generative AI model to interpret those coarse scene reconstructions and understand the behavior of multipath mmWave reflections. This model fills in the gaps, refining the initial reconstruction until it completes the scene.

They tested their scene reconstruction system, called RISE, using more than 100 human trajectories captured by a single mmWave radar. On average, RISE generated reconstructions that were about twice as precise than existing techniques.

In the future, the researchers want to improve the granularity and detail in their reconstructions. They also want to build large foundation models for wireless signals, like the foundation models GPT, Claude, and Gemini for language and vision, which could open new applications.

This work is supported, in part, by the National Science Foundation (NSF), the MIT Media Lab, and Amazon.

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.

But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.   

To address this shortcoming, MIT researchers introduced a new method for measuring a different type of uncertainty that more reliably identifies confident but incorrect LLM responses.

Their method involves comparing a target model’s response to responses from a group of similar LLMs. They found that measuring cross-model disagreement more accurately captures this type of uncertainty than traditional approaches.

They combined their approach with a measure of LLM self-consistency to create a total uncertainty metric, and evaluated it on 10 realistic tasks, such as question-answering and math reasoning. This total uncertainty metric consistently outperformed other measures and was better at identifying unreliable predictions.

“Self-consistency is being used in a lot of different approaches for uncertainty quantification, but if your estimate of uncertainty only relies on a single model’s outcome, it is not necessarily trustable. We went back to the beginning to understand the limitations of current approaches and used those as a starting point to design a complementary method that can empirically improve the results,” says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate student at MIT and lead author of a paper on this technique.

She is joined on the paper by Veronika Thost, a research scientist at the MIT-IBM Watson AI Lab; Walter Gerych, a former MIT postdoc who is now an assistant professor at Worcester Polytechnic Institute; Mikhail Yurochkin, a staff research scientist at the MIT-IBM Watson AI Lab; and senior author Marzyeh Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems.

Understanding overconfidence

Many popular methods for uncertainty quantification involve asking a model for a confidence score or testing the consistency of its responses to the same prompt. These methods estimate aleatoric uncertainty, or how internally confident a model is in its own prediction.

However, LLMs can be confident when they are completely wrong. Research has shown that epistemic uncertainty, or uncertainty about whether one is using the right model, can be a better way to assess true uncertainty when a model is overconfident.

The MIT researchers estimate epistemic uncertainty by measuring disagreement across a similar group of LLMs.    

“If I ask ChatGPT the same question multiple times and it gives me the same answer over and over again, that doesn’t mean the answer is necessarily correct. If I switch to Claude or Gemini and ask them the same question, and I get a different answer, that is going to give me a sense of the epistemic uncertainty,” Hamidieh explains.

Epistemic uncertainty attempts to capture how far a target model diverges from the ideal model for that task. But since it is impossible to build an ideal model, researchers use surrogates or approximations that often rely on faulty assumptions.

To improve uncertainty quantification, the MIT researchers needed a more accurate way to estimate epistemic uncertainty.

An ensemble approach

The method they developed involves measuring the divergence between the target model and a small ensemble of models with similar size and architecture. They found that comparing semantic similarity, or how closely the meanings of the responses match, could provide a better estimate of epistemic uncertainty.

To achieve the most accurate estimate, the researchers needed a set of LLMs that covered diverse responses, weren’t too similar to the target model, and were weighted based on credibility.

“We found that the easiest way to satisfy all these properties is to take models that are trained by different companies. We tried many different approaches that were more complex, but this very simple approach ended up working best,” Hamidieh says.

Once they had developed this method for estimating epistemic uncertainty, they combined it with a standard approach that measures aleatoric uncertainty. This total uncertainty metric (TU) offered the most accurate reflection of whether a model’s confidence level is trustworthy.

“Uncertainty depends on the uncertainty of the given prompt as well as how close our model is to the optimal model. This is why summing up these two uncertainty metrics is going to give us the best estimate,” Hamidieh says.

TU could more effectively identify situations where an LLM is hallucinating, since epistemic uncertainty can flag confidently wrong outputs that aleatoric uncertainty might miss. It could also enable researchers to reinforce an LLM’s confidently correct answers during training, which may improve performance.

They tested TU using multiple LLMs on 10 common tasks, such as question-answering, summarization, translation, and math reasoning. Their method more effectively identified unreliable predictions than either measure on its own.

Measuring total uncertainty often required fewer queries than calculating aleatoric uncertainty, which could reduce computational costs and save energy.

Their experiments also revealed that epistemic uncertainty is most effective on tasks with a unique correct answer, like factual question-answering, but may underperform on more open-ended tasks.

In the future, the researchers could adapt their technique to improve its performance on open-ended queries. They may also build on this work by exploring other forms of aleatoric uncertainty.

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

A mosquito finds its target with the help of certain cues in its environment, such as a person’s silhouette and the carbon dioxide they exhale.

Now researchers at MIT and Georgia Tech have found that these visual and chemical cues help determine the insects’ flight paths. The team has developed the first three-dimensional model of mosquito flight, based on experiments with mosquitoes flying in the presence of different sensory cues.

Their model, reported today in the journal Science Advances, identifies three flight patterns that mosquitoes exhibit in response to sensory stimuli.

When they can only see a potential target, mosquitoes take a “fly-by” approach, quickly diving in toward the target, then flying back out if they do not detect any other host-confirming cues.

When they can’t see a target but can smell a chemical cue such as carbon dioxide, mosquitoes will do “double-takes,” slowing down and flitting back and forth to keep close to the source.

Interestingly, when mosquitoes receive both visual and chemical cues, such as seeing a silhouette and smelling carbon dioxide, they switch to an “orbiting” pattern, flying around a target at a steady speed as they prepare to land, much like a shark circling its prey.

The researchers say the new model can be used to predict how mosquitoes will fly in response to other cues, such as heat, humidity, and certain odors. Such predictions could help to design more effective traps and mosquito control strategies.

“Our work suggests that mosquito traps need specifically calibrated, multisensory lures to keep mosquitoes engaged long enough to be captured,” says study author Jörn Dunkel, MathWorks Professor of Mathematics at MIT. “We hope this establishes a new paradigm for studying pest behavior by using 3D tracking and data-driven modeling to decode their movement and solve major public health challenges.”

The study’s MIT co-authors are Chenyi Fei, a postdoc in MIT’s Department of Mathematics, and Alexander Cohen PhD ’26, a recent MIT chemical engineering PhD student advised by Dunkel and Professor Martin Bazant, along with Christopher Zuo, Soohwan Kim, and David L. Hu ’01, PhD ’06 of Georgia Tech, and Ring Carde of the University of California at Riverside.

Flight by numbers

Mosquitoes are considered to be the most dangerous animals in the world, given their collective impact on human health. The blood-sucking insects transmit malaria, dengue fever, West Nile virus, and other deadly diseases that together cause over 770,000 deaths each year.

Of the 3,500 known species of mosquitoes, around 100 have evolved to specifically target humans, including Aedes aegypti, a species that uses a variety of cues to seek out human hosts. Scientists have studied how certain cues attract mosquitoes, mainly by setting up experiments in wind tunnels, where they can waft cues such as carbon dioxide and study how mosquitoes respond. Such experiments have mainly recorded data such as where and when the insects land. The researchers say no study has explored how mosquitoes fly as they hunt for a host.

“The big question was: How do mosquitoes find a human target?” says Fei. “There were previous experimental studies on what kind of cues might be important. But nothing has been especially quantitative.”

At MIT, Dunkel’s group develops mathematical models to describe and predict the behavior of complex living systems, such as how worms untangle, how starfish embryos develop and swim, and how microbes evolve their community structure over time.

Dunkel looked to apply similar quantitative techniques to predict flight patterns of mosquitoes after giving a talk at Georgia Tech. David Hu, a former MIT graduate student who is now a professor of mechanical engineering at Georgia Tech, proposed a collaboration; Hu’s lab was carrying out experiments with mosquitoes at a facility at the Centers of Disease Control and Prevention in Atlanta, where they were studying the insects’ behavior in response to sensory cues. Could Dunkel’s group use the collected data to identify significant flight behavior that could ultimately help scientists control mosquito populations?

“One of the original motivations was designing better traps for mosquitoes,” says Cohen. “Figuring out how they fly around a human gives insights on how we can avoid them.”

Taking cues

For their new study, Hu and his colleagues at Georgia Tech carried out experiments with 50 to 100 mosquitoes of the Aedes aegypti species. The insects flew around inside a long, white, slightly angled rectangular room as cameras around the room captured detailed three-dimensional trajectories of each mosquito as it flew around. In the center of the room, they placed an object to represent a certain visual or chemical cue.

In some trials, they placed a black Styrofoam sphere on a stand to represent a simple visual cue. (Mosquitoes would be able to see the black sphere against the room’s white background). In other trials, they set up a white sphere with a tube running through to pump out carbon dioxide at rates similar to what humans breathe out. These trials represented the presence of a chemical cue, but not a visual cue.

The researchers also studied the mosquitoes’ response to both visual and chemical cues, using a black sphere that emitted carbon dioxide. Finally, they observed how mosquitoes behaved around a human volunteer who wore protective clothing that was black on one side and white on the other.

Across 20 experiments, the team generated more than 53 million data points and over 477,220 mosquito flight paths. Hu shared the data with Dunkel, whose group used the measurements to develop a model for mosquito flight behavior.

“We are proposing a very broad range of dynamical equations, and when you start out, the equation to predict a mosquito’s flight path is very complicated, with a lot of terms, including the relative importance of a visual versus a chemical cue,” Dunkel explains. “Then through iteration against data, we reduce the complexity of that equation until we get the simplest model that still agrees with the data.”

In the end, the group whittled down a simple model that accurately predicts how a mosquito will fly, given the presence of a visual cue, a chemical cue, or both. The flight paths in response to one or the other cue are markedly different. And interestingly, when both cues are present, the researchers noted that the resulting path is not “additive.” In other words, a mosquito does not simply combine the paths that it would separately take when it can both see and smell a target. Instead, the insects take a distinct path, circling, rather than diving or darting around their target.

“Our work suggests that mosquito traps need specifically calibrated ‘multisensory’ lures to keep mosquitoes engaged long enough to be captured,” Dunkel says.

“Obviously there are additional cues that humans emit, like odor, heat, and humidity,” Cohen notes. “For the species we study, visual and carbon dioxide cues are the most important. But we can apply this model to study different species and how they respond to other sensory cues.”

The researchers have developed an interactive app that incorporates the new mosquito flight model. Users can experiment with different objects and set parameters such as the number of mosquitoes around the object and the type of sensory cue that is present. The model then visualizes how the mosquitoes would fly in response.

“The original hope was to have a quantitative model that can simulate mosquito behavior around various trap designs,” Cohen says. “Now that we have a model, we can start to design more intelligent traps.”

This work was supported, in part, by the National Science Foundation, Schmidt Sciences, LLC, the NDSEG Fellowship Program, and the MIT MathWorks Professorship Fund. 

MIT senior Srihitha Dasari never imagined she would be speaking in front of the United Nations about health care, technology, and the power of co-designing public health interventions in collaboration with impacted communities. 

But when she stepped up to the podium to speak about digital well-being and community-centered health care design, she carried with her more than research findings. She brought several years of experiential learning in public health environments, ranging from visiting exam rooms of New England’s largest safety net hospital to collaborating with nurses in rural Argentina and working on maternal health in India and Nepal. 

Dasari arrived at MIT intending to major in brain and cognitive sciences and follow a pre-med track. Like many aspiring physicians, she pictured her MIT years filled with lab work, shadowing doctors, and preparing for medical school. Instead, during her first Independent Activities Period (IAP), she enrolled in the PKG Center for Social Impact’s IAP Health Program and began to broaden her understanding of practicing medicine. 

“What was really incredible about IAP Health,” says Dasari, is that “I did it so early in not only my academic career, but just in the beginning of when I was actually formulating a lot of my career aspirations, [and] it really immersed me into what public health looks like.”

Through IAP Health, Dasari worked as an intern at the Boston Medical Center Autism Program. There, she provided in-clinic support to children with autism and their families, helping guide them through appointments and collaborating with physicians to adapt exam techniques to meet patients’ needs.

“When you think about how medicine is delivered, it can feel very systematic — like there are boxes you have to check,” she says. “But working in that clinic showed me … you can modify the experience to truly care for the whole person.”

The program exposed her not only to clinical care, but to the broader forces that shape health outcomes. “I didn’t envision myself doing public health when I entered college,” Dasari says. “But looking back, public health is the through line of everything I’ve done.”

She remained at Boston Medical Center as an intern for over a year with continued support and funding from the PKG Center’s Federal Work-Study and Social Impact Internship programs. The sustained engagement deepened her understanding of how health-care systems can either reinforce or reduce disparities — a systems-level perspective that carried into her global work.

During her second-year IAP, Dasari received a PKG Fellowship to develop an electronic health record system for a maternal ward in a rural hospital in Argentina. The project grew out of a relationship she developed through the student group MIT Global Health Alliance, which supports co-designing public health interventions with impacted communities.  

Dasari’s collaboration with the hospital evolved into a social enterprise that she co-founded: PuntoSalud, an AI-powered chatbot designed to bridge health information gaps in rural Argentina. Dasari and her co-founders received a $5,000 award and seed funding to prototype and develop PuntoSalud through the PKG IDEAS Social Innovation Incubator, MIT’s only entrepreneurship program focused solely on social impact. 

Speaking at the United Nations underscored a lesson she absorbed throughout her varied experience: Meaningful health innovation begins with relationships.

“I’ve been able to meet people from so many different facets of the health-care pipeline that I didn’t envision myself meeting,” Dasari says.

The mindset she developed through PKG programming has informed her experience beyond the center. Through MIT D-Lab, Dasari conducted maternal and neonatal health needs assessments in rural Nepal, interviewing community members to better understand gaps in care. The findings informed efforts to retrofit birthing centers with improved heating systems in cold climates. Later, supported by the MIT International Science and Technology Initiatives, she traveled to India to interview health-care providers about strategies to reduce non-medical cesarean section rates, with the goal of developing policy recommendations for other health systems.

“I came in thinking I would practice medicine one-on-one,” Dasari says. “Now I want to increase my impact in the health care field. I see that as clinical medicine intersected with public health, relieving health disparities for a wider population.”

As Dasari prepares to leave MIT for a year in clinical research, she does so with a systems lens on science and health care, and a commitment to social impact. 

“The path I’ve taken in health care as an undergrad student has given me both a sense of purpose and fulfillment as I prepare to leave MIT,” she says. “It’s shown me that meaningful impact can begin long before medical school, and that I want to carry forward the values these experiences instilled in me.”

For Dasari, experiential learning didn’t redirect her ambitions, but enhanced them. 

“I feel like the PKG Center … it’s not changing your goals,” she says. “It’s shaping them into their fullest potential.”

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