To make large language models (LLMs) more accurate when answering harder questions, researchers can let the model spend more time thinking about potential solutions.

But common approaches that give LLMs this capability set a fixed computational budget for every problem, regardless of how complex it is. This means the LLM might waste computational resources on simpler questions or be unable to tackle intricate problems that require more reasoning.

To address this, MIT researchers developed a smarter way to allocate computational effort as the LLM solves a problem. Their method enables the model to dynamically adjust its computational budget based on the difficulty of the question and the likelihood that each partial solution will lead to the correct answer.

The researchers found that their new approach enabled LLMs to use as little as one-half the computation as existing methods, while achieving comparable accuracy on a range of questions with varying difficulties. In addition, their method allows smaller, less resource-intensive LLMs to perform as well as or even better than larger models on complex problems.

By improving the reliability and efficiency of LLMs, especially when they tackle complex reasoning tasks, this technique could reduce the energy consumption of generative AI systems and enable the use of LLMs in more high-stakes and time-sensitive applications.

“The computational cost of inference has quickly become a major bottleneck for frontier model providers, and they are actively trying to find ways to improve computational efficiency per user queries. For instance, the recent GPT-5.1 release highlights the efficacy of the ‘adaptive reasoning’ approach our paper proposes. By endowing the models with the ability to know what they don’t know, we can enable them to spend more compute on the hardest problems and most promising solution paths, and use far fewer tokens on easy ones. That makes reasoning both more reliable and far more efficient,” says Navid Azizan, the Alfred H. and Jean M. Hayes Career Development Assistant Professor in the Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), a principal investigator of the Laboratory for Information and Decision Systems (LIDS), and the senior author of a paper on this technique.

Azizan is joined on the paper by lead author Young-Jin Park, a LIDS/MechE graduate student; Kristjan Greenewald, a research scientist in the MIT-IBM Watson AI Lab; Kaveh Alim, an IDSS graduate student; and Hao Wang, a research scientist at the MIT-IBM Watson AI Lab and the Red Hat AI Innovation Team. The research is being presented this week at the Conference on Neural Information Processing Systems.

Computation for contemplation

A recent approach called inference-time scaling lets a large language model take more time to reason about difficult problems.

Using inference-time scaling, the LLM might generate multiple solution attempts at once or explore different reasoning paths, then choose the best ones to pursue from those candidates.

A separate model, known as a process reward model (PRM), scores each potential solution or reasoning path. The LLM uses these scores to identify the most promising ones.     

Typical inference-time scaling approaches assign a fixed amount of computation for the LLM to break the problem down and reason about the steps.

Instead, the researchers’ method, known as instance-adaptive scaling, dynamically adjusts the number of potential solutions or reasoning steps based on how likely they are to succeed, as the model wrestles with the problem.

“This is how humans solve problems. We come up with some partial solutions and then decide, should I go further with any of these, or stop and revise, or even go back to my previous step and continue solving the problem from there?” Wang explains.

To do this, the framework uses the PRM to estimate the difficulty of the question, helping the LLM assess how much computational budget to utilize for generating and reasoning about potential solutions.

At every step in the model’s reasoning process, the PRM looks at the question and partial answers and evaluates how promising each one is for getting to the right solution. If the LLM is more confident, it can reduce the number of potential solutions or reasoning trajectories to pursue, saving computational resources.

But the researchers found that existing PRMs often overestimate the model’s probability of success.

Overcoming overconfidence

“If we were to just trust current PRMs, which often overestimate the chance of success, our system would reduce the computational budget too aggressively. So we first had to find a way to better calibrate PRMs to make inference-time scaling more efficient and reliable,” Park says.

The researchers introduced a calibration method that enables PRMs to generate a range of probability scores rather than a single value. In this way, the PRM creates more reliable uncertainty estimates that better reflect the true probability of success.

With a well-calibrated PRM, their instance-adaptive scaling framework can use the probability scores to effectively reduce computation while maintaining the accuracy of the model’s outputs.

When they compared their method to standard inference-time scaling approaches on a series of mathematical reasoning tasks, it utilized less computation to solve each problem while achieving similar accuracy.

“The beauty of our approach is that this adaptation happens on the fly, as the problem is being solved, rather than happening all at once at the beginning of the process,” says Greenewald.

In the future, the researchers are interested in applying this technique to other applications, such as code generation and AI agents. They are also planning to explore additional uses for their PRM calibration method, like for reinforcement learning and fine-tuning.

“Human employees learn on the job — some CEOs even started as interns — but today’s agents remain largely static pieces of probabilistic software. Work like this paper is an important step toward changing that: helping agents understand what they don’t know and building mechanisms for continual self-improvement. These capabilities are essential if we want agents that can operate safely, adapt to new situations, and deliver consistent results at scale,” says Akash Srivastava, director and chief architect of Core AI at IBM Software, who was not involved with this work.

This work was funded, in part, by the MIT-IBM Watson AI Lab, the MIT-Amazon Science Hub, the MIT-Google Program for Computing Innovation, and MathWorks. 

In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble.

So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects — until now.

MIT researchers have demonstrated aerial microrobots that can fly with speed and agility that is comparable to their biological counterparts. A collaborative team designed a new AI-based controller for the robotic bug that enabled it to follow gymnastic flight paths, such as executing continuous body flips.

With a two-part control scheme that combines high performance with computational efficiency, the robot’s speed and acceleration increased by about 450 percent and 250 percent, respectively, compared to the researchers’ best previous demonstrations.

The speedy robot was agile enough to complete 10 consecutive somersaults in 11 seconds, even when wind disturbances threatened to push it off course.

“We want to be able to use these robots in scenarios that more traditional quad copter robots would have trouble flying into, but that insects could navigate. Now, with our bioinspired control framework, the flight performance of our robot is comparable to insects in terms of speed, acceleration, and the pitching angle. This is quite an exciting step toward that future goal,” says Kevin Chen, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), head of the Soft and Micro Robotics Laboratory within the Research Laboratory of Electronics (RLE), and co-senior author of a paper on the robot.

Chen is joined on the paper by co-lead authors Yi-Hsuan Hsiao, an EECS MIT graduate student; Andrea Tagliabue PhD ’24; and Owen Matteson, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro); as well as EECS graduate student Suhan Kim; Tong Zhao MEng ’23; and co-senior author Jonathan P. How, the Ford Professor of Engineering in the Department of Aeronautics and Astronautics and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). The research appears today in Science Advances.

An AI controller

Chen’s group has been building robotic insects for more than five years.

They recently developed a more durable version of their tiny robot, a microcassette-sized device that weighs less than a paperclip. The new version utilizes larger, flapping wings that enable more agile movements. They are powered by a set of squishy artificial muscles that flap the wings at an extremely fast rate.

But the controller — the “brain” of the robot that determines its position and tells it where to fly — was hand-tuned by a human, limiting the robot’s performance.

For the robot to fly quickly and aggressively like a real insect, it needed a more robust controller that could account for uncertainty and perform complex optimizations quickly.

Such a controller would be too computationally intensive to be deployed in real time, especially with the complicated aerodynamics of the lightweight robot.

To overcome this challenge, Chen’s group joined forces with How’s team and, together, they crafted a two-step, AI-driven control scheme that provides the robustness necessary for complex, rapid maneuvers, and the computational efficiency needed for real-time deployment.

“The hardware advances pushed the controller so there was more we could do on the software side, but at the same time, as the controller developed, there was more they could do with the hardware. As Kevin’s team demonstrates new capabilities, we demonstrate that we can utilize them,” How says.

For the first step, the team built what is known as a model-predictive controller. This type of powerful controller uses a dynamic, mathematical model to predict the behavior of the robot and plan the optimal series of actions to safely follow a trajectory.

While computationally intensive, it can plan challenging maneuvers like aerial somersaults, rapid turns, and aggressive body tilting. This high-performance planner is also designed to consider constraints on the force and torque the robot could apply, which is essential for avoiding collisions.

For instance, to perform multiple flips in a row, the robot would need to decelerate in such a way that its initial conditions are exactly right for doing the flip again.

“If small errors creep in, and you try to repeat that flip 10 times with those small errors, the robot will just crash. We need to have robust flight control,” How says.

They use this expert planner to train a “policy” based on a deep-learning model, to control the robot in real time, through a process called imitation learning. A policy is the robot’s decision-making engine, which tells the robot where and how to fly.

Essentially, the imitation-learning process compresses the powerful controller into a computationally efficient AI model that can run very fast.

The key was having a smart way to create just enough training data, which would teach the policy everything it needs to know for aggressive maneuvers.

“The robust training method is the secret sauce of this technique,” How explains.

The AI-driven policy takes robot positions as inputs and outputs control commands in real time, such as thrust force and torques.

Insect-like performance

In their experiments, this two-step approach enabled the insect-scale robot to fly 447 percent faster while exhibiting a 255 percent increase in acceleration. The robot was able to complete 10 somersaults in 11 seconds, and the tiny robot never strayed more than 4 or 5 centimeters off its planned trajectory.

“This work demonstrates that soft and microrobots, traditionally limited in speed, can now leverage advanced control algorithms to achieve agility approaching that of natural insects and larger robots, opening up new opportunities for multimodal locomotion,” says Hsiao.

The researchers were also able to demonstrate saccade movement, which occurs when insects pitch very aggressively, fly rapidly to a certain position, and then pitch the other way to stop. This rapid acceleration and deceleration help insects localize themselves and see clearly.

“This bio-mimicking flight behavior could help us in the future when we start putting cameras and sensors on board the robot,” Chen says.

Adding sensors and cameras so the microrobots can fly outdoors, without being attached to a complex motion capture system, will be a major area of future work.

The researchers also want to study how onboard sensors could help the robots avoid colliding with one another or coordinate navigation.

“For the micro-robotics community, I hope this paper signals a paradigm shift by showing that we can develop a new control architecture that is high-performing and efficient at the same time,” says Chen.

“This work is especially impressive because these robots still perform precise flips and fast turns despite the large uncertainties that come from relatively large fabrication tolerances in small-scale manufacturing, wind gusts of more than 1 meter per second, and even its power tether wrapping around the robot as it performs repeated flips,” says Sarah Bergbreiter, a professor of mechanical engineering at Carnegie Mellon University, who was not involved with this work.

“Although the controller currently runs on an external computer rather than onboard the robot, the authors demonstrate that similar, but less precise, control policies may be feasible even with the more limited computation available on an insect-scale robot. This is exciting because it points toward future insect-scale robots with agility approaching that of their biological counterparts,” she adds.

This research is funded, in part, by the National Science Foundation (NSF), the Office of Naval Research, Air Force Office of Scientific Research, MathWorks, and the Zakhartchenko Fellowship.

With every step we take, our brains are already thinking about the next one. If a bump in the terrain or a minor misstep has thrown us off balance, our stride may need to be altered to prevent a fall. Our two-legged posture makes maintaining stability particularly complex, which our brains solve in part by continually monitoring our bodies and adjusting where we place our feet.

Now, scientists at MIT have determined that animals with very different bodies likely use a shared strategy to balance themselves when they walk.

Nidhi Seethapathi, the Frederick A. and Carole J. Middleton Career Development Assistant Professor in Brain and Cognitive Sciences and Electrical Engineering and Computer Science at MIT, and K. Lisa Yang ICoN Center Fellow Antoine De Comite found that humans, mice, and fruit flies all use an error-correction process to guide foot placement and maintain stability while walking. Their findings, published Oct. 21 in the journal PNAS, could inform future studies exploring how the brain achieves stability during locomotion — bridging the gap between animal models and human balance.

Corrective action

Information must be integrated by the brain to keep us upright when we walk or run. Our steps must be continually adjusted according to the terrain, our desired speed, and our body’s current velocity and position in space.

“We rely on a combination of vestibular, proprioceptive, and visual information to build an estimate of our body’s state, determining if we are about to fall. Once we know the body’s state, we can decide which corrective actions to take,” explains Seethapathi, who is also an associate investigator at the McGovern Institute for Brain Research.

While humans are known to adjust where they place their feet to correct for errors, it is not known whether animals whose bodies are more stable do this, too.

To find out, Seethapathi and De Comite, who is a postdoc in Seethapathi’s and Guoping Feng's lab at the McGovern Institute, turned to locomotion data from mice, fruit flies, and humans shared by other labs, enabling an analysis across species that is otherwise challenging. Importantly, Seethapathi notes, all the animals they studied were walking in everyday natural environments, such as around a room — not on a treadmill or over unusual terrain.

Even in these ordinary circumstances, missteps and minor imbalances are common, and the team’s analysis showed that these errors predicted where all of the animals placed their feet in subsequent steps, regardless of whether they had two, four, or six legs.

One foot in front of another

By tracking the animals’ bodies and the step-by-step placement of their feet, Seethapathi and De Comite were able to find a measure of error that informs each animal’s next step. “By taking this comparative approach, we’ve forced ourselves to come up with a definition of error that generalizes across species,” Seethapathi says. “An animal moves with an expected body state for a particular speed. If it deviates from that ideal state, that deviation — at any given moment — is the error.”

“It was surprising to find similarities across these three species, which, at first sight, look very different,” says DeComite. “The methods themselves are surprising because we now have a pipeline to analyze foot placement and locomotion stability in any legged species,” explains DeComite, “which could lead similar analyses in even more species in the future.”

The team’s data suggest that in all of the species in the study, placement of the feet is guided both by an error-correction process and the speed at which an animal is traveling. Steps tend to lengthen and feet spend less time on the ground as animals pick up their pace, while the width of each step seems to change largely to compensate for body-state errors.

Now, Seethapathi says, we can look forward to future studies to explore how the dual control systems might be generated and integrated in the brain to keep moving bodies stable.

Studying how brains help animals move stably may also guide the development of more-targeted strategies to help people improve their balance and, ultimately, prevent falls.

“In elderly individuals and individuals with sensorimotor disorders, minimizing fall risk is one of the major functional targets of rehabilitation,” says Seethapathi. “A fundamental understanding of the error correction process that helps us remain stable will provide insight into why this process falls short in populations with neural deficits,” she says. 

Peripheral nerves — the network connecting the brain, spinal cord, and central nervous system to the rest of the body — transmit sensory information, control muscle movements, and regulate automatic bodily functions. Bioelectronic devices implanted on these nerves offer remarkable potential for the treatment and rehabilitation of neurological and systemic diseases. However, because the body perceives these implants as foreign objects, they often trigger the formation of dense fibrotic tissue at bioelectronic device–tissue interfaces, which can significantly compromise device performance and longevity.

New research published in the journal Science Advances presents a robust bioadhesive strategy that establishes non-fibrotic bioelectronic interfaces on diverse peripheral nerves — including the occipital, vagus, deep peroneal, sciatic, tibial, and common peroneal nerves — for up to 12 weeks.

“We discovered that adhering the bioelectrodes to peripheral nerves can fully prevent the formation of fibrosis on the interfaces,” says Xuanhe Zhao, the Uncas and Helen Whitaker Professor, and professor of mechanical engineering and civil engineering at MIT. “We further demonstrated long-term, drug-free hypertension mitigation using non-fibrotic bioelectronics over four weeks, and ongoing.”

The approach inhibits immune cell infiltration at the device-tissue interface, thereby preventing the formation of fibrous capsules within the inflammatory microenvironment. In preclinical rodent models, the team demonstrated that the non-fibrotic, adhesive bioelectronic device maintained stable, long-term regulation of blood pressure.

“Our long-term blood pressure regulation approach was inspired by traditional acupuncture,” says Hyunmin Moon, lead author of the study and a postdoc in the Department of Mechanical Engineering. “The lower leg has long been used in hypertension treatment, and the deep peroneal nerve lies precisely at an acupuncture point. We were thrilled to see that stimulating this nerve achieved blood pressure regulation for the first time. The convergence of our non-fibrotic, adhesive bioelectronic device with this long-term regulation capability holds exciting promise for translational medicine.”

Importantly, after 12 weeks of implantation with continuous nerve stimulation, only minimal macrophage activity and limited deposition of smooth muscle actin and collagen were detected, underscoring the device’s potential to deliver long-term neuromodulation without triggering fibrosis. “The contrast between the immune response of the adhered device and that of the non-adhered control is striking,” says Bastien Aymon, a study co-author and a PhD candidate in mechanical engineering. “The fact that we can observe immunologically pristine interfaces after three months of adhesive implantation is extremely encouraging for future clinical translation.”

This work offers a broadly applicable strategy for all implantable bioelectronic systems by preventing fibrosis at the device interface, paving the way for more effective and long-lasting therapies such as hypertension mitigation.

Hypertension is a major contributor to cardiovascular diseases, the leading cause of death worldwide. Although medications are effective in many cases, more than 50 percent of patients remain hypertensive despite treatment — a condition known as resistant hypertension. Traditional carotid sinus or vagus nerve stimulation methods are often accompanied by side effects including apnea, bradycardia, cough, and paresthesia.

“In contrast, our non-fibrotic, adhesive bioelectronic device targeting the deep peroneal nerve enables long-term blood pressure regulation in resistant hypertensive patients without metabolic side effects,” says Moon.

A noninvasive method for measuring blood glucose levels, developed at MIT, could save diabetes patients from having to prick their fingers several times a day.

The MIT team used Raman spectroscopy — a technique that reveals the chemical composition of tissues by shining near-infrared or visible light on them — to develop a shoebox-sized device that can measure blood glucose levels without any needles.

In tests in a healthy volunteer, the researchers found that the measurements from their device were similar to those obtained by commercial continuous glucose monitoring sensors that require a wire to be implanted under the skin. While the device presented in this study is too large to be used as a wearable sensor, the researchers have since developed a wearable version that they are now testing in a small clinical study.

“For a long time, the finger stick has been the standard method for measuring blood sugar, but nobody wants to prick their finger every day, multiple times a day. Naturally, many diabetic patients are under-testing their blood glucose levels, which can cause serious complications,” says Jeon Woong Kang, an MIT research scientist and the senior author of the study. “If we can make a noninvasive glucose monitor with high accuracy, then almost everyone with diabetes will benefit from this new technology.”

MIT postdoc Arianna Bresci is the lead author of the new study, which appears today in the journal Analytical Chemistry. Other authors include Peter So, director of the MIT Laser Biomedical Research Center (LBRC) and an MIT professor of biological engineering and mechanical engineering; and Youngkyu Kim and Miyeon Jue of Apollon Inc., a biotechnology company based in South Korea.

Noninvasive glucose measurement

While most diabetes patients measure their blood glucose levels by drawing blood and testing it with a glucometer, some use wearable monitors, which have a sensor that is inserted just under the skin. These sensors provide continuous glucose measurements from the interstitial fluid, but they can cause skin irritation and they need to be replaced every 10 to 15 days.

In hopes of creating wearable glucose monitors that would be more comfortable for patients, researchers in MIT’s LBRC have been pursuing noninvasive sensors based on Raman spectroscopy. This type of spectroscopy reveals the chemical composition of tissue or cells by analyzing how near-infrared light is scattered, or deflected, as it encounters different kinds of molecules.

In 2010, researchers at the LBRC showed that they could indirectly calculate glucose levels based on a comparison between Raman signals from the interstitial fluid that bathes skin cells and a reference measurement of blood glucose levels. While this approach produced reliable measurements, it wasn’t practical for translating to a glucose monitor.

More recently, the researchers reported a breakthrough that allowed them to directly measure glucose Raman signals from the skin. Normally, this glucose signal is too small to pick out from all of the other signals generated by molecules in tissue. The MIT team found a way to filter out much of the unwanted signal by shining near-infrared light onto the skin at a different angle from which they collected the resulting Raman signal.

The researchers obtained those measurements using equipment that was around the size of a desktop printer, and since then, they have been working on further shrinking the footprint of the device.

In their new study, they were able to create a smaller device by analyzing just three bands — spectral regions that correspond to specific molecular features — in the Raman spectrum.

Typically, a Raman spectrum may contain about 1,000 bands. However, the MIT team found that they could determine blood glucose levels by measuring just three bands — one from the glucose plus two background measurements. This approach allowed the researchers to reduce the amount and cost of equipment needed, allowing them to perform the measurement with a cost-effective device about the size of a shoebox.

“By refraining from acquiring the whole spectrum, which has a lot of redundant information, we go down to three bands selected from about 1,000,” Bresci says. “With this new approach, we can change the components commonly used in Raman-based devices, and save space, time, and cost.”

Toward a wearable sensor

In a clinical study performed at the MIT Center for Clinical Translation Research (CCTR), the researchers used the new device to take readings from a healthy volunteer over a four-hour period. As the subject rested their arm on top of the device, a near-infrared beam shone through a small glass window onto the skin to perform the measurement.

Each measurement takes a little more than 30 seconds, and the researchers took a new reading every five minutes.

During the study, the subject consumed two 75-gram glucose drinks, allowing the researchers to monitor significant changes in blood glucose concentration. They found that the Raman-based device showed accuracy levels similar to those of two commercially available, invasive glucose monitors worn by the subject.

Since finishing that study, the researchers have developed a smaller prototype, about the size of a cellphone, that they’re currently testing at the MIT CCTR as a wearable monitor in healthy and prediabetic volunteers. Next year, they plan to run a larger study working with a local hospital, which will include people with diabetes.

The researchers are also working on making the device even smaller, about the size of a watch. Additionally, they are exploring ways to ensure that the device can obtain accurate readings from people with different skin tones.

The research was funded by the National Institutes of Health, the Korean Technology and Information Promotion Agency for SMEs, and Apollon Inc.

For the first time, MIT chemists have synthesized a fungal compound known as verticillin A, which was discovered more than 50 years ago and has shown potential as an anticancer agent.

The compound has a complex structure that made it more difficult to synthesize than related compounds, even though it differed by only a couple of atoms.

“We have a much better appreciation for how those subtle structural changes can significantly increase the synthetic challenge,” says Mohammad Movassaghi, an MIT professor of chemistry. “Now we have the technology where we can not only access them for the first time, more than 50 years after they were isolated, but also we can make many designed variants, which can enable further detailed studies.”

In tests in human cancer cells, a derivative of verticillin A showed particular promise against a type of pediatric brain cancer called diffuse midline glioma. More tests will be needed to evaluate its potential for clinical use, the researchers say.

Movassaghi and Jun Qi, an associate professor of medicine at Dana-Farber Cancer Institute/Boston Children’s Cancer and Blood Disorders Center and Harvard Medical School, are the senior authors of the study, which appears today in the Journal of the American Chemical Society. Walker Knauss PhD ’24 is the lead author of the paper. Xiuqi Wang, a medicinal chemist and chemical biologist at Dana-Farber, and Mariella Filbin, research director in the Pediatric Neurology-Oncology Program at Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, are also authors of the study.

A complex synthesis

Researchers first reported the isolation of verticillin A from fungi, which use it for protection against pathogens, in 1970. Verticillin A and related fungal compounds have drawn interest for their potential anticancer and antimicrobial activity, but their complexity has made them difficult to synthesize.

In 2009, Movassaghi’s lab reported the synthesis of (+)-11,11'-dideoxyverticillin A, a fungal compound similar to verticillin A. That molecule has 10 rings and eight stereogenic centers, or carbon atoms that have four different chemical groups attached to them. These groups have to be attached in a way that ensures they have the correct orientation, or stereochemistry, with respect to the rest of the molecule.

Once that synthesis was achieved, however, synthesis of verticillin A remained challenging, even though the only difference between verticillin A and (+)-11,11'-dideoxyverticillin A is the presence of two oxygen atoms.

“Those two oxygens greatly limit the window of opportunity that you have in terms of doing chemical transformations,” Movassaghi says. “It makes the compound so much more fragile, so much more sensitive, so that even though we had had years of methodological advances, the compound continued to pose a challenge for us.”

Both of the verticillin A compounds consist of two identical fragments that must be joined together to form a molecule called a dimer. To create (+)-11,11'-dideoxyverticillin A, the researchers had performed the dimerization reaction near the end of the synthesis, then added four critical carbon-sulfur bonds.

Yet when trying to synthesize verticillin A, the researchers found that waiting to add those carbon-sulfur bonds at the end did not result in the correct stereochemistry. As a result, the researchers had to rethink their approach and ended up creating a very different synthetic sequence.

“What we learned was the timing of the events is absolutely critical. We had to significantly change the order of the bond-forming events,” Movassaghi says.

The verticillin A synthesis begins with an amino acid derivative known as beta-hydroxytryptophan, and then step-by-step, the researchers add a variety of chemical functional groups, including alcohols, ketones, and amides, in a way that ensures the correct stereochemistry.

A functional group containing two carbon-sulfur bonds and a disulfide bond were introduced early on, to help control the stereochemistry of the molecule, but the sensitive disulfides had to be “masked” and protected as a pair of sulfides to prevent them from breakdown under subsequent chemical reactions. The disulfide-containing groups were then regenerated after the dimerization reaction.

“This particular dimerization really stands out in terms of the complexity of the substrates that we’re bringing together, which have such a dense array of functional groups and stereochemistry,” Movassaghi says.

The overall synthesis requires 16 steps from the beta-hydroxytryptophan starting material to verticillin A.

Killing cancer cells

Once the researchers had successfully completed the synthesis, they were also able to tweak it to generate derivates of verticillin A. Researchers at Dana-Farber then tested these compounds against several types of diffuse midline glioma (DMG), a rare brain tumor that has few treatment options.

The researchers found that the DMG cell lines most susceptible to these compounds were those that have high levels of a protein called EZHIP. This protein, which plays a role in the methylation of DNA, has been previously identified as a potential drug target for DMG.

“Identifying the potential targets of these compounds will play a critical role in further understanding their mechanism of action, and more importantly, will help optimize the compounds from the Movassaghi lab to be more target specific for novel therapy development,” Qi says.

The verticillin derivatives appear to interact with EZHIP in a way that increases DNA methylation, which induces the cancer cells to under programmed cell death. The compounds that were most successful at killing these cells were N-sulfonylated (+)-11,11'-dideoxyverticillin A and N-sulfonylated verticillin A. N-sulfonylation — the addition of a functional group containing sulfur and oxygen — makes the molecules more stable.

“The natural product itself is not the most potent, but it’s the natural product synthesis that brought us to a point where we can make these derivatives and study them,” Movassaghi says.

The Dana-Farber team is now working on further validating the mechanism of action of the verticillin derivatives, and they also hope to begin testing the compounds in animal models of pediatric brain cancers.

“Natural compounds have been valuable resources for drug discovery, and we will fully evaluate the therapeutic potential of these molecules by integrating our expertise in chemistry, chemical biology, cancer biology, and patient care. We have also profiled our lead molecules in more than 800 cancer cell lines, and will be able to understand their functions more broadly in other cancers,” Qi says.

The research was funded by the National Institute of General Medical Sciences, the Ependymoma Research Foundation, and the Curing Kids Cancer Foundation.

More than 600 undergraduate students crowded into the Stratton Student Center on Oct. 28, for MIT’s first-ever Institute-wide Undergraduate Research Opportunities Program (UROP) mixer.

“At MIT, we believe in the transformative power of learning by doing, and there’s no better example than UROP,” says MIT President Sally Kornbluth, who attended the mixer with Provost Anantha Chandrakasan and Chancellor Melissa Nobles. “The energy at the inaugural UROP mixer was exhilarating, and I’m delighted that students now have this easy way to explore different paths to the frontiers of research.”

The event gave students the chance to explore internships and undergraduate research opportunities — in fields ranging from artificial intelligence to the life sciences to the arts, and beyond — all in one place, with approximately 150 researchers from labs available to discuss the projects and answer questions in real time. The offices of the Chancellor and Provost co-hosted the event, which the UROP office helped coordinate. 

First-year student Isabell Luo recently began a UROP project in the Living Matter lab led by Professor Rafael Gómez-Bombarelli, where she is benchmarking machine-learned interatomic potentials that simulate chemical reactions at the molecular level and exploring fine-tuning strategies to improve their accuracy. She’s passionate about AI and machine learning, eco-friendly design, and entrepreneurship, and was attending the UROP mixer to find more “real-world” projects to work on.

“I’m trying to dip my toes into different areas, which is why I’m at the mixer,” said Luo. “On the internet it would be so hard to find the right opportunities. It’s nice to have a physical space and speak to people from so many disciplines.”

More than nine out of every 10 members of MIT’s class of 2025 took part in a UROP before graduating. In recent years, approximately 3,200 undergraduates have participated in a UROP project each year. To meet the strong demand for UROPs, the Institute will commit up to $1 million in funding this year to create more of them. The funding will come from MIT’s schools and Office of the Provost. 

“UROPs have become an indispensable part of the MIT undergraduate education, providing hands-on experience that really helps students learn new ways to problem-solve and innovate,” says Chandrakasan. “I was thrilled to see so many students at the mixer — it was a testament to their willingness to roll up their sleeves and get to work on really tough challenges.”

Arielle Berman, a postdoc in the Raman Lab, was looking to recruit an undergraduate researcher for a project on sensor integration for muscle actuators for biohybrid robots — robots that include living parts. She spoke about how her own research experience as an undergraduate had shaped her career.

“It’s a really important event because we’re able to expose undergraduates to research,” says Berman. “I’m the first PhD in my family, so I wasn’t aware that research existed, or could be a career. Working in a research lab as an undergraduate student changed my life trajectory, and I’m happy to pass it forward and help students have experiences they wouldn’t have otherwise.”

The event drew students with interests as varied as the projects available. For first-year Nate Black, who plans to major in mechanical engineering, “I just wanted something to develop my interest in 3D printing and additive manufacturing.” First-year Akpandu Ekezie, who expects to major in Course 6-5 (Electrical Engineering with Computing), was interested in photonic circuits. “I’m looking mainly for EE-related things that are more hands-on,” he explained. “I want to get more physical experience.”

Nobles has a message for students considering a UROP project: Just go for it. “There’s a UROP for every student, regardless of experience,” she says. “Find something that excites you and give it a try.” She encourages students who weren’t able to attend the mixer, as well as those who did attend but still have questions, to get in touch with the UROP office.

First-year students Ruby Mykkanen and Aditi Deshpande attended the mixer together. Both were searching for UROP projects they could work on during Independent Activities Period in January. Deshpande also noted that the mixer was helpful for understanding “what research is being done at MIT.”

Said Mykkanen, “It’s fun to have it all in one place!”

Imagine having a continuum soft robotic arm bend around a bunch of grapes or broccoli, adjusting its grip in real time as it lifts the object. Unlike traditional rigid robots that generally aim to avoid contact with the environment as much as possible and stay far away from humans for safety reasons, this arm senses subtle forces, stretching and flexing in ways that mimic more of the compliance of a human hand. Its every motion is calculated to avoid excessive force while achieving the task efficiently. In MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Laboratory for Information and Decisions Systems (LIDS) labs, these seemingly simple movements are the culmination of complex mathematics, careful engineering, and a vision for robots that can safely interact with humans and delicate objects.

Soft robots, with their deformable bodies, promise a future where machines move more seamlessly alongside people, assist in caregiving, or handle delicate items in industrial settings. Yet that very flexibility makes them difficult to control. Small bends or twists can produce unpredictable forces, raising the risk of damage or injury. This motivates the need for safe control strategies for soft robots. 

“Inspired by advances in safe control and formal methods for rigid robots, we aim to adapt these ideas to soft robotics — modeling their complex behavior and embracing, rather than avoiding, contact — to enable higher-performance designs (e.g., greater payload and precision) without sacrificing safety or embodied intelligence,” says lead senior author and MIT Assistant Professor Gioele Zardini, who is a principal investigator in LIDS and the Department of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems, and Society (IDSS). “This vision is shared by recent and parallel work from other groups.”

Safety first

The team developed a new framework that blends nonlinear control theory (controlling systems that involve highly complex dynamics) with advanced physical modeling techniques and efficient real-time optimization to produce what they call “contact-aware safety.” At the heart of the approach are high-order control barrier functions (HOCBFs) and high-order control Lyapunov functions (HOCLFs). HOCBFs define safe operating boundaries, ensuring the robot doesn’t exert unsafe forces. HOCLFs guide the robot efficiently toward its task objectives, balancing safety with performance.

“Essentially, we’re teaching the robot to know its own limits when interacting with the environment while still achieving its goals,” says MIT Department of Mechanical Engineering PhD student Kiwan Wong, the lead author of a new paper describing the framework. “The approach involves some complex derivation of soft robot dynamics, contact models, and control constraints, but the specification of control objectives and safety barriers is rather straightforward for the practitioner, and the outcomes are very tangible, as you see the robot moving smoothly, reacting to contact, and never causing unsafe situations.”

“Compared with traditional kinematic CBFs — where forward-invariant safe sets are hard to specify — the HOCBF framework simplifies barrier design, and its optimization formulation accounts for system dynamics (e.g., inertia), ensuring the soft robot stops early enough to avoid unsafe contact forces,” says Worcester Polytechnic Institute Assistant Professor and former CSAIL postdoc Wei Xiao.

“Since soft robots emerged, the field has highlighted their embodied intelligence and greater inherent safety relative to rigid robots, thanks to passive material and structural compliance. Yet their “cognitive” intelligence — especially safety systems — has lagged behind that of rigid serial-link manipulators,” says co-lead author Maximilian Stölzle, a research intern at Disney Research and formerly a Delft University of Technology PhD student and visiting researcher at MIT LIDS and CSAIL. “This work helps close that gap by adapting proven algorithms to soft robots and tailoring them for safe contact and soft-continuum dynamics.”

The LIDS and CSAIL team tested the system on a series of experiments designed to challenge the robot’s safety and adaptability. In one test, the arm pressed gently against a compliant surface, maintaining a precise force without overshooting. In another, it traced the contours of a curved object, adjusting its grip to avoid slippage. In yet another demonstration, the robot manipulated fragile items alongside a human operator, reacting in real time to unexpected nudges or shifts. “These experiments show that our framework is able to generalize to diverse tasks and objectives, and the robot can sense, adapt, and act in complex scenarios while always respecting clearly defined safety limits,” says Zardini.

Soft robots with contact-aware safety could be a real value-add in high-stakes places, of course. In health care, they could assist in surgeries, providing precise manipulation while reducing risk to patients. In industry, they might handle fragile goods without constant supervision. In domestic settings, robots could help with chores or caregiving tasks, interacting safely with children or the elderly — a key step toward making soft robots reliable partners in real-world environments. 

“Soft robots have incredible potential,” says co-lead senior author Daniela Rus, director of CSAIL and a professor in the Department of Electrical Engineering and Computer Science. “But ensuring safety and encoding motion tasks via relatively simple objectives has always been a central challenge. We wanted to create a system where the robot can remain flexible and responsive while mathematically guaranteeing it won’t exceed safe force limits.”

Combining soft robot models, differentiable simulation, and control theory

Underlying the control strategy is a differentiable implementation of something called the Piecewise Cosserat-Segment (PCS) dynamics model, which predicts how a soft robot deforms and where forces accumulate. This model allows the system to anticipate how the robot’s body will respond to actuation and complex interactions with the environment. “The aspect that I most like about this work is the blend of integration of new and old tools coming from different fields like advanced soft robot models, differentiable simulation, Lyapunov theory, convex optimization, and injury-severity–based safety constraints. All of this is nicely blended into a real-time controller fully grounded in first principles,” says co-author Cosimo Della Santina, who is an associate professor at Delft University of Technology. 

Complementing this is the Differentiable Conservative Separating Axis Theorem (DCSAT), which estimates distances between the soft robot and obstacles in the environment that can be approximated with a chain of convex polygons in a differentiable manner. “Earlier differentiable distance metrics for convex polygons either couldn’t compute penetration depth — essential for estimating contact forces — or yielded non-conservative estimates that could compromise safety,” says Wong. “Instead, the DCSAT metric returns strictly conservative, and therefore safe, estimates while simultaneously allowing for fast and differentiable computation.” Together, PCS and DCSAT give the robot a predictive sense of its environment for more proactive, safe interactions.

Looking ahead, the team plans to extend their methods to three-dimensional soft robots and explore integration with learning-based strategies. By combining contact-aware safety with adaptive learning, soft robots could handle even more complex, unpredictable environments. 

“This is what makes our work exciting,” says Rus. “You can see the robot behaving in a human-like, careful manner, but behind that grace is a rigorous control framework ensuring it never oversteps its bounds.”

“Soft robots are generally safer to interact with than rigid-bodied robots by design, due to the compliance and energy-absorbing properties of their bodies,” says University of Michigan Assistant Professor Daniel Bruder, who wasn’t involved in the research. “However, as soft robots become faster, stronger, and more capable, that may no longer be enough to ensure safety. This work takes a crucial step towards ensuring soft robots can operate safely by offering a method to limit contact forces across their entire bodies.”

The team’s work was supported, in part, by The Hong Kong Jockey Club Scholarships, the European Union’s Horizon Europe Program, Cultuurfonds Wetenschapsbeurzen, and the Rudge (1948) and Nancy Allen Chair. Their work was published earlier this month in the Institute of Electrical and Electronics Engineers’ Robotics and Automation Letters.

Researchers at MIT have demonstrated that wedge-shaped vortex generators attached to a ship’s hull can reduce drag by up to 7.5 percent, which reduces overall ship emissions and fuel expenses. The paper, “Net Drag Reduction in High Block Coefficient Ships and Vehicles Using Vortex Generators,” was presented at the Society of Naval Architects and Marine Engineers 2025 Maritime Convention in Norfolk, Virginia.

The work offers a promising path toward decarbonization, addressing the pressing need to meet the International Maritime Organization (IMO) goal to reduce carbon intensity of international shipping by at least 40 percent by 2030, compared to 2008 levels. Achieving such ambitious emissions reduction will require a coordinated approach, employing multiple methods, from redesigning ship hulls, propellers, and engines to using novel fuels and operational methods.

The researchers — José del Águila Ferrandis, Jack Kimmeth, and Michael Triantafyllou of MIT Sea Grant and the Department of Mechanical Engineering, along with Alfonso Parra Rubio and Neil Gershenfeld of the Center for Bits and Atoms — determined the optimized vortex generator shape and size using a combination of computational fluid dynamics (CFD) and experimental methods guided by AI optimization methods. 

The team first established parametric trends through extensive CFD analysis, and then tested multiple hulls through rapid prototyping to validate the results experimentally. Scale models of an axisymmetric hull with a bare tail, a tail with delta wing vortex generators, and a tail with wedge vortex generators were produced and tested. The team identified wedge-like vortex generators as the key shape that could achieve this level of drag reduction. 

Through flow visualization, the researchers could see that drag was reduced by delaying turbulent flow separation, helping water flow more smoothly along the ship’s hull, shrinking the wake behind the vessel. This also allows the propeller and rudder to work more efficiently in a uniform flow. “We document for the first time experimentally a reduction in fuel required by ships using vortex generators, relatively small structures in the shape of a wedge attached at a specific point of the ship’s hull,” explains Michael Triantafyllou, professor of mechanical engineering and director of MIT Sea Grant. 

Vortex generators have long been used in aircraft-wing design to maintain lift and delay stalling. This study is the first to show that the vortex generators can be applied for drag reduction in commercial ships.

The modular adaptability of the wedge vortex generators would allow integration into a broad range of hull forms, including bulk carriers and tankers, and the devices can synergize with, or even replace, existing technologies like pre-swirl stators (fixed fins mounted in front of propellers), improving overall system performance. As an example case, the researchers estimate that installing the vortex generators on a 300-meter Newcastlemax bulk carrier operating at 14.5 knots over a cross-Pacific route would result in significantly reduced emissions and approximately $750,000 in fuel savings per year.

The findings offer a practical, cost-effective solution that could be implemented efficiently across existing fleets. This study was supported through the CBA Consortium, working with Oldendorff Carriers, which operates about 700 bulk carriers around the world. An extension of this research is supported by the MIT Maritime Consortium, led by MIT professors Themis Sapsis and Fotini Christia. The Maritime Consortium was formed in 2025 to address critical gaps in the modernization of the commercial fleet through interdisciplinary research and collaboration across academia, industry, and regulatory agencies.

Norway is the world’s largest producer of farmed Atlantic salmon and a top exporter of seafood, while the United States remains the largest importer of these products, according to the Food and Agriculture Organization. Two MIT students recently traveled to Trondheim, Norway to explore the cutting-edge technologies being developed and deployed in offshore aquaculture. 

Beckett Devoe, a senior in artificial intelligence and decision-making, and Tony Tang, a junior in mechanical engineering, first worked with MIT Sea Grant through the Undergraduate Research Opportunities Program (UROP). They contributed to projects focusing on wave generator design and machine learning applications for analyzing oyster larvae health in hatcheries. While near-shore aquaculture is a well-established industry across Massachusetts and the United States, open-ocean farming is still a nascent field here, facing unique and complex challenges. 

To help better understand this emerging industry, MIT Sea Grant created a collaborative initiative, AquaCulture Shock, with funding from an Aquaculture Technologies and Education Travel Grant through the National Sea Grant College Program. Collaborating with the MIT-Scandinavia MISTI (MIT International Science and Technology Initiatives) program, MIT Sea Grant matched Devoe and Tang with aquaculture-related summer internships at SINTEF Ocean, one of the largest research institutes in Europe. 

“The opportunity to work on this hands-on aquaculture project, under a world-renowned research institution, in an area of the world known for its innovation in marine technology — this is what MISTI is all about,” says Madeline Smith, managing director for MIT-Scandinavia. “Not only are students gaining valuable experience in their fields of study, but they’re developing cultural understanding and skills that equip them to be future global leaders.” Both students worked within SINTEF Ocean’s Aquaculture Robotics and Autonomous Systems Laboratory (ACE-Robotic Lab), a facility designed to develop and test new aquaculture technologies. 

“Norway has this unique geography where it has all of these fjords,” says Sveinung Ohrem, research manager for the Aquaculture Robotics and Automation Group at SINTEF Ocean. “So you have a lot of sheltered waters, which makes it ideal to do sea-based aquaculture.” He estimates that there are about a thousand fish farms along Norway’s coast, and walks through some of the tools being used in the industry: decision-making systems to gather and visualize data for the farmers and operators; robots for inspection and cleaning; environmental sensors to measure oxygen, temperature, and currents; echosounders that send out acoustic signals to track where the fish are; and cameras to help estimate biomass and fine-tune feeding. “Feeding is a huge challenge,” he notes. “Feed is the largest cost, by far, so optimizing feeding leads to a very significant decrease in your cost.”

During the internship, Devoe focused on a project that uses AI for fish feeding optimization. “I try to look at the different features of the farm — so maybe how big the fish are, or how cold the water is ... and use that to try to give the farmers an optimal feeding amount for the best outcomes, while also saving money on feed,” he explains. “It was good to learn some more machine learning techniques and just get better at that on a real-world project.” 

In the same lab, Tang worked on the simulation of an underwater vehicle-manipulator system to navigate farms and repair damage on cage nets with a robotic arm. Ohrem says there are thousands of aquaculture robots operating in Norway today. “The scale is huge,” he says. “You can’t have 8,000 people controlling 8,000 robots — that’s not economically or practically feasible. So the level of autonomy in all of these robots needs to be increased.”

The collaboration between MIT and SINTEF Ocean began in 2023 when MIT Sea Grant hosted Eleni Kelasidi, a visiting research scientist from the ACE-Robotic Lab. Kelasidi collaborated with MIT Sea Grant director Michael Triantafyllou and professor of mechanical engineering Themistoklis Sapsis developing controllers, models, and underwater vehicles for aquaculture, while also investigating fish-machine interactions. 

“We have had a long and fruitful collaboration with the Norwegian University of Science and Technology (NTNU) and SINTEF, which continues with important efforts such as the aquaculture project with Dr. Kelasidi,” Triantafyllou says. “Norway is at the forefront of offshore aquaculture and MIT Sea Grant is investing in this field, so we anticipate great results from the collaboration.”

Kelasidi, who is now a professor at NTNU, also leads the Field Robotics Lab, focusing on developing resilient robotic systems to operate in very complex and harsh environments. “Aquaculture is one of the most challenging field domains we can demonstrate any autonomous solutions, because everything is moving,” she says. Kelasidi describes aquaculture as a deeply interdisciplinary field, requiring more students with backgrounds both in biology and technology. “We cannot develop technologies that are applied for industries where we don’t have biological components,” she explains, “and then apply them somewhere where we have a live fish or other live organisms.” 

Ohrem affirms that maintaining fish welfare is the primary driver for researchers and companies operating in aquaculture, especially as the industry continues to grow. “So the big question is,” he says, “how can you ensure that?” SINTEF Ocean has four research licenses for farming fish, which they operate through a collaboration with SalMar, the second-largest salmon farmer in the world. The students had the opportunity to visit one of the industrial-scale farms, Singsholmen, on the island of Hitra. The farm has 10 large, round net pens about 50 meters across that extend deep below the surface, each holding up to 200,000 salmon. “I got to physically touch the nets and see how the [robotic] arm might be able to fix the net,” says Tang. 

Kelasidi emphasizes that the information gained in the field cannot be learned from the office or lab. “That opens up and makes you realize, what is the scale of the challenges, or the scale of the facilities,” she says. She also highlights the importance of international and institutional collaboration to advance this field of research and develop more resilient robotic systems. “We need to try to target that problem, and let’s solve it together.”

MIT Sea Grant and the MIT-Scandinavia MISTI program are currently recruiting a new cohort of four MIT students to intern in Norway this summer with institutes advancing offshore farming technologies, including NTNU’s Field Robotics Lab in Trondheim. Students interested in autonomy, deep learning, simulation modeling, underwater robotic systems, and other aquaculture-related areas are encouraged to reach out to Lily Keyes at MIT Sea Grant.

Advancements in battery innovation are transforming both mobility and energy systems alike, according to Kurt Kelty, vice president of battery, propulsion, and sustainability at General Motors (GM). At the MIT Energy Initiative (MITEI) Fall Colloquium, Kelty explored how GM is bringing next-generation battery technologies from lab to commercialization, driving American battery innovation forward. The colloquium is part of the ongoing MITEI Presents: Advancing the Energy Transition speaker series.

At GM, Kelty’s team is primarily focused on three things: first, improving affordability to get more electric vehicles (EVs) on the road. “How do you drive down the cost?” Kelty asked the audience. “It's the batteries. The batteries make up about 30 percent of the cost of the vehicle.” Second, his team strives to improve battery performance, including charging speed and energy density. Third, they are working on localizing the supply chain. “We've got to build up our resilience and our independence here in North America, so we're not relying on materials coming from China,” Kelty explained.

To aid their efforts, resources are being poured into the virtualization space, significantly cutting down on time dedicated to research and development. Now, Kelty’s team can do modeling up front using artificial intelligence, reducing what previously would have taken months to a couple of days.

“If you want to modify … the nickel content ever so slightly, we can very quickly model: ‘OK, how’s that going to affect the energy density? The safety? How’s that going to affect the charge capability?’” said Kelty. “We can look at that at the cell level, then the pack level, then the vehicle level.”

Kelty revealed that they have found a solution that addresses affordability, accessibility, and commercialization: lithium manganese-rich (LMR) batteries. Previously, the industry looked to reduce costs by lowering the amount of cobalt in batteries by adding greater amounts of nickel. These high-nickel batteries are in most cars on the road in the United States due to their high range. LMR batteries, though, take things a step further by reducing the amount of nickel and adding more manganese, which drives the cost of batteries down even further while maintaining range.

Lithium-iron-phosphate (LFP) batteries are the chemistry of choice in China, known for low cost, high cycle life, and high safety. With LMR batteries, the cost is comparable to LFP with a range that is closer to high-nickel. “That’s what’s really a breakthrough,” said Kelty.

LMR batteries are not new, but there have been challenges to adopting them, according to Kelty. “People knew about it, but they didn’t know how to commercialize it. They didn’t know how to make it work in an EV,” he explained. Now that GM has figured out commercialization, they will be the first to market these batteries in their EVs in 2028.

Kelty also expressed excitement over the use of vehicle-to-grid technologies in the future. Using a bidirectional charger with a two-way flow of energy, EVs could charge, but also send power from their batteries back to the electrical grid. This would allow customers to charge “their vehicles at night when the electricity prices are really low, and they can discharge it during the day when electricity rates are really high,” he said.

In addition to working in the transportation sector, GM is exploring ways to extend their battery expertise into applications in grid-scale energy storage. “It’s a big market right now, but it’s growing very quickly because of the data center growth,” said Kelty.

When looking to the future of battery manufacturing and EVs in the United States, Kelty remains optimistic: “we’ve got the technology here to make it happen. We’ve always had the innovation here. Now, we’re getting more and more of the manufacturing. We’re getting that all together. We’ve got just tremendous opportunity here that I’m hopeful we’re going to be able to take advantage of and really build a massive battery industry here.”

This speaker series highlights energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. Visit MITEI’s Events page for more information on this and additional events.

“MIT hasn’t just prepared me for the future of work — it’s pushed me to study it. As AI systems become more capable, more of our online activity will be carried out by artificial agents. That raises big questions: How should we design these systems to understand our preferences? What happens when AI begins making many of our decisions?”

These are some of the questions MIT Sloan School of Management PhD candidate Benjamin Manning is researching. Part of his work investigates how to design and evaluate artificial intelligence agents that act on behalf of people, and how their behavior shapes markets and institutions. 

Previously, he received a master’s degree in public policy from the Harvard Kennedy School and a bachelor’s in mathematics from Washington University in St. Louis. After working as a research assistant, Manning knew he wanted to pursue an academic career.

“There’s no better place in the world to study economics and computer science than MIT,” he says. “Nobel and Turing award winners are everywhere, and the IT group lets me explore both fields freely. It was my top choice — when I was accepted, the decision was clear.” 

After receiving his PhD, Manning hopes to secure a faculty position at a business school and do the same type of work that MIT Sloan professors — his mentors — do every day.

“Even in my fourth year, it still feels surreal to be an MIT student. I don’t think that feeling will ever fade. My mom definitely won’t ever get over telling people about it.”

Of his MIT Sloan experience, Manning says he didn’t know it was possible to learn so much so quickly. “It’s no exaggeration to say I learned more in my first year as a PhD candidate than in all four years of undergrad. While the pace can be intense, wrestling with so many new ideas has been incredibly rewarding. It’s given me the tools to do novel research in economics and AI — something I never imagined I’d be capable of.”

As an economist studying AI simulations of humans, for Manning, the future of work not only means understanding how AI acts on our behalf, but also radically improving and accelerating social scientific discovery.

“Another part of my research agenda explores how well AI systems can simulate human responses. I envision a future where researchers test millions of behavioral simulations in minutes, rapidly prototyping experimental designs, and identifying promising research directions before investing in costly human studies. This isn’t about replacing human insight, but amplifying it: Scientists can focus on asking better questions, developing theory, and interpreting results while AI handles the computational heavy lifting.”

He’s excited by the prospect: “We are possibly moving toward a world where the pace of understanding may get much closer to the speed of economic change.”

Our muscles are nature’s actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate “biohybrid robots” made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers.

But for the most part, these designs are limited in the amount of motion and power they can produce. Now, MIT engineers are aiming to give bio-bots a power lift with artificial tendons.

In a study appearing today in the journal Advanced Science, the researchers developed artificial tendons made from tough and flexible hydrogel. They attached the rubber band-like tendons to either end of a small piece of lab-grown muscle, forming a “muscle-tendon unit.” Then they connected the ends of each artificial tendon to the fingers of a robotic gripper.

When they stimulated the central muscle to contract, the tendons pulled the gripper’s fingers together. The robot pinched its fingers together three times faster, and with 30 times greater force, compared with the same design without the connecting tendons.

The researchers envision the new muscle-tendon unit can be fit to a wide range of biohybrid robot designs, much like a universal engineering element.

“We are introducing artificial tendons as interchangeable connectors between muscle actuators and robotic skeletons,” says lead author Ritu Raman, an assistant professor of mechanical engineering (MechE) at MIT. “Such modularity could make it easier to design a wide range of robotic applications, from microscale surgical tools to adaptive, autonomous exploratory machines.”

The study’s MIT co-authors include graduate students Nicolas Castro, Maheera Bawa, Bastien Aymon, Sonika Kohli, and Angel Bu; undergraduate Annika Marschner; postdoc Ronald Heisser; alumni Sarah J. Wu ’19, SM ’21, PhD ’24 and Laura Rosado ’22, SM ’25; and MechE professors Martin Culpepper and Xuanhe Zhao.

Muscle’s gains

Raman and her colleagues at MIT are at the forefront of biohybrid robotics, a relatively new field that has emerged in the last decade. They focus on combining synthetic, structural robotic parts with living muscle tissue as natural actuators.

“Most actuators that engineers typically work with are really hard to make small,” Raman says. “Past a certain size, the basic physics doesn’t work. The nice thing about muscle is, each cell is an independent actuator that generates force and produces motion. So you could, in principle, make robots that are really small.”

Muscle actuators also come with other advantages, which Raman’s team has already demonstrated: The tissue can grow stronger as it works out, and can naturally heal when injured. For these reasons, Raman and others envision that muscly droids could one day be sent out to explore environments that are too remote or dangerous for humans. Such muscle-bound bots could build up their strength for unforeseen traverses or heal themselves when help is unavailable. Biohybrid bots could also serve as small, surgical assistants that perform delicate, microscale procedures inside the body.

All these future scenarios are motivating Raman and others to find ways to pair living muscles with synthetic skeletons. Designs to date have involved growing a band of muscle and attaching either end to a synthetic skeleton, similar to looping a rubber band around two posts. When the muscle is stimulated to contract, it can pull the parts of a skeleton together to generate a desired motion.

But Raman says this method produces a lot of wasted muscle that is used to attach the tissue to the skeleton rather than to make it move. And that connection isn’t always secure. Muscle is quite soft compared with skeletal structures, and the difference can cause muscle to tear or detach. What’s more, it is often only the contractions in the central part of the muscle that end up doing any work — an amount that’s relatively small and generates little force.

“We thought, how do we stop wasting muscle material, make it more modular so it can attach to anything, and make it work more efficiently?” Raman says. “The solution the body has come up with is to have tendons that are halfway in stiffness between muscle and bone, that allow you to bridge this mechanical mismatch between soft muscle and rigid skeleton. They’re like thin cables that wrap around joints efficiently.”

“Smartly connected”

In their new work, Raman and her colleagues designed artificial tendons to connect natural muscle tissue with a synthetic gripper skeleton. Their material of choice was hydrogel — a squishy yet sturdy polymer-based gel. Raman obtained hydrogel samples from her colleague and co-author Xuanhe Zhao, who has pioneered the development of hydrogels at MIT. Zhao’s group has derived recipes for hydrogels of varying toughness and stretch that can stick to many surfaces, including synthetic and biological materials.

To figure out how tough and stretchy artificial tendons should be in order to work in their gripper design, Raman’s team first modeled the design as a simple system of three types of springs, each representing the central muscle, the two connecting tendons, and the gripper skeleton. They assigned a certain stiffness to the muscle and skeleton, which were previously known, and used this to calculate the stiffness of the connecting tendons that would be required in order to move the gripper by a desired amount.

From this modeling, the team derived a recipe for hydrogel of a certain stiffness. Once the gel was made, the researchers carefully etched the gel into thin cables to form artificial tendons. They attached two tendons to either end of a small sample of muscle tissue, which they grew using lab-standard techniques. They then wrapped each tendon around a small post at the end of each finger of the robotic gripper — a skeleton design that was developed by MechE professor Martin Culpepper, an expert in designing and building precision machines.

When the team stimulated the muscle to contract, the tendons in turn pulled on the gripper to pinch its fingers together. Over multiple experiments, the researchers found that the muscle-tendon gripper worked three times faster and produced 30 times more force compared to when the gripper is actuated just with a band of muscle tissue (and without any artificial tendons). The new tendon-based design also was able to keep up this performance over 7,000 cycles, or muscle contractions.

Overall, Raman saw that the addition of artificial tendons increased the robot’s power-to-weight ratio by 11 times, meaning that the system required far less muscle to do just as much work.

“You just need a small piece of actuator that’s smartly connected to the skeleton,” Raman says. “Normally, if a muscle is really soft and attached to something with high resistance, it will just tear itself before moving anything. But if you attach it to something like a tendon that can resist tearing, it can really transmit its force through the tendon, and it can move a skeleton that it wouldn’t have been able to move otherwise.”

The team’s new muscle-tendon design successfully merges biology with robotics, says biomedical engineer Simone Schürle-Finke, associate professor of health sciences and technology at ETH Zürich.

“The tough-hydrogel tendons create a more physiological muscle–tendon–bone architecture, which greatly improves force transmission, durability, and modularity,” says Schürle-Finke, who was not involved with the study. “This moves the field toward biohybrid systems that can operate repeatably and eventually function outside the lab.”

With the new artificial tendons in place, Raman’s group is moving forward to develop other elements, such as skin-like protective casings, to enable muscle-powered robots in practical, real-world settings.

This research was supported, in part, by the U.S. Department of Defense Army Research Office, the MIT Research Support Committee, and the National Science Foundation.

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study.

Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.

The researchers found that models can mistakenly link certain sentence patterns to specific topics, so an LLM might give a convincing answer by recognizing familiar phrasing instead of understanding the question.

Their experiments showed that even the most powerful LLMs can make this mistake.

This shortcoming could reduce the reliability of LLMs that perform tasks like handling customer inquiries, summarizing clinical notes, and generating financial reports.

It could also have safety risks. A nefarious actor could exploit this to trick LLMs into producing harmful content, even when the models have safeguards to prevent such responses.

After identifying this phenomenon and exploring its implications, the researchers developed a benchmarking procedure to evaluate a model’s reliance on these incorrect correlations. The procedure could help developers mitigate the problem before deploying LLMs.

“This is a byproduct of how we train models, but models are now used in practice in safety-critical domains far beyond the tasks that created these syntactic failure modes. If you’re not familiar with model training as an end-user, this is likely to be unexpected,” says Marzyeh Ghassemi, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the MIT Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems, and the senior author of the study.

Ghassemi is joined by co-lead authors Chantal Shaib, a graduate student at Northeastern University and visiting student at MIT; and Vinith Suriyakumar, an MIT graduate student; as well as Levent Sagun, a research scientist at Meta; and Byron Wallace, the Sy and Laurie Sternberg Interdisciplinary Associate Professor and associate dean of research at Northeastern University’s Khoury College of Computer Sciences. A paper describing the work will be presented at the Conference on Neural Information Processing Systems.

Stuck on syntax

LLMs are trained on a massive amount of text from the internet. During this training process, the model learns to understand the relationships between words and phrases — knowledge it uses later when responding to queries.

In prior work, the researchers found that LLMs pick up patterns in the parts of speech that frequently appear together in training data. They call these part-of-speech patterns “syntactic templates.”

LLMs need this understanding of syntax, along with semantic knowledge, to answer questions in a particular domain.

“In the news domain, for instance, there is a particular style of writing. So, not only is the model learning the semantics, it is also learning the underlying structure of how sentences should be put together to follow a specific style for that domain,” Shaib explains.   

But in this research, they determined that LLMs learn to associate these syntactic templates with specific domains. The model may incorrectly rely solely on this learned association when answering questions, rather than on an understanding of the query and subject matter.

For instance, an LLM might learn that a question like “Where is Paris located?” is structured as adverb/verb/proper noun/verb. If there are many examples of sentence construction in the model’s training data, the LLM may associate that syntactic template with questions about countries.

So, if the model is given a new question with the same grammatical structure but nonsense words, like “Quickly sit Paris clouded?” it might answer “France” even though that answer makes no sense.

“This is an overlooked type of association that the model learns in order to answer questions correctly. We should be paying closer attention to not only the semantics but the syntax of the data we use to train our models,” Shaib says.

Missing the meaning

The researchers tested this phenomenon by designing synthetic experiments in which only one syntactic template appeared in the model’s training data for each domain. They tested the models by substituting words with synonyms, antonyms, or random words, but kept the underlying syntax the same.

In each instance, they found that LLMs often still responded with the correct answer, even when the question was complete nonsense.

When they restructured the same question using a new part-of-speech pattern, the LLMs often failed to give the correct response, even though the underlying meaning of the question remained the same.

They used this approach to test pre-trained LLMs like GPT-4 and Llama, and found that this same learned behavior significantly lowered their performance.

Curious about the broader implications of these findings, the researchers studied whether someone could exploit this phenomenon to elicit harmful responses from an LLM that has been deliberately trained to refuse such requests.

They found that, by phrasing the question using a syntactic template the model associates with a “safe” dataset (one that doesn’t contain harmful information), they could trick the model into overriding its refusal policy and generating harmful content.

“From this work, it is clear to me that we need more robust defenses to address security vulnerabilities in LLMs. In this paper, we identified a new vulnerability that arises due to the way LLMs learn. So, we need to figure out new defenses based on how LLMs learn language, rather than just ad hoc solutions to different vulnerabilities,” Suriyakumar says.

While the researchers didn’t explore mitigation strategies in this work, they developed an automatic benchmarking technique one could use to evaluate an LLM’s reliance on this incorrect syntax-domain correlation. This new test could help developers proactively address this shortcoming in their models, reducing safety risks and improving performance.

In the future, the researchers want to study potential mitigation strategies, which could involve augmenting training data to provide a wider variety of syntactic templates. They are also interested in exploring this phenomenon in reasoning models, special types of LLMs designed to tackle multi-step tasks.

“I think this is a really creative angle to study failure modes of LLMs. This work highlights the importance of linguistic knowledge and analysis in LLM safety research, an aspect that hasn’t been at the center stage but clearly should be,” says Jessy Li, an associate professor at the University of Texas at Austin, who was not involved with this work.

This work is funded, in part, by a Bridgewater AIA Labs Fellowship, the National Science Foundation, the Gordon and Betty Moore Foundation, a Google Research Award, and Schmidt Sciences.

More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct. 30, hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic). Headlining the event was MIT PhD student and BoltzGen’s first author Hannes Stärk, who had announced BoltzGen just a few days prior.

Building upon Boltz-2, an open-source biomolecular structure prediction model predicting protein binding affinity that made waves over the summer, BoltzGen (officially released on Sunday, Oct. 26.) is the first model of its kind to go a step further by generating novel protein binders that are ready to enter the drug discovery pipeline.

Three key innovations make this possible: first, BoltzGen’s ability to carry out a variety of tasks, unifying protein design and structure prediction while maintaining state-of-the-art performance. Next, BoltzGen’s built-in constraints are designed with feedback from wetlab collaborators to ensure the model creates functional proteins that don’t defy the laws of physics or chemistry. Lastly, a rigorous evaluation process tests the model on “undruggable” disease targets, pushing the limits of BoltzGen’s binder generation capabilities.

Most models used in industry or academia are capable of either structure prediction or protein design. Moreover, they’re limited to generating certain types of proteins that bind successfully to easy “targets.” Much like students responding to a test question that looks like their homework, as long as the training data looks similar to the target during binder design, the models often work. But existing methods are nearly always evaluated on targets for which structures with binders already exist, and end up faltering in performance when used on more challenging targets.

“There have been models trying to tackle binder design, but the problem is that these models are modality-specific,” Stärk points out. “A general model does not only mean that we can address more tasks. Additionally, we obtain a better model for the individual task since emulating physics is learned by example, and with a more general training scheme, we provide more such examples containing generalizable physical patterns.”

The BoltzGen researchers went out of their way to test BoltzGen on 26 targets, ranging from therapeutically relevant cases to ones explicitly chosen for their dissimilarity to the training data. 

This comprehensive validation process, which took place in eight wetlabs across academia and industry, demonstrates the model’s breadth and potential for breakthrough drug development.

Parabilis Medicines, one of the industry collaborators that tested BoltzGen in a wetlab setting, praised BoltzGen’s potential: “we feel that adopting BoltzGen into our existing Helicon peptide computational platform capabilities promises to accelerate our progress to deliver transformational drugs against major human diseases.”

While the open-source releases of Boltz-1, Boltz-2, and now BoltzGen (which was previewed at the 7th Molecular Machine Learning Conference on Oct. 22) bring new opportunities and transparency in drug development, they also signal that biotech and pharmaceutical industries may need to reevaluate their offerings. 

Amid the buzz for BoltzGen on the social media platform X, Justin Grace, a principal machine learning scientist at LabGenius, raised a question. “The private-to-open performance time lag for chat AI systems is [seven] months and falling,” Grace wrote in a post. “It looks to be even shorter in the protein space. How will binder-as-a-service co’s be able to [recoup] investment when we can just wait a few months for the free version?” 

For those in academia, BoltzGen represents an expansion and acceleration of scientific possibility. “A question that my students often ask me is, ‘where can AI change the therapeutics game?’” says senior co-author and MIT Professor Regina Barzilay, AI faculty lead for the Jameel Clinic and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Unless we identify undruggable targets and propose a solution, we won’t be changing the game,” she adds. “The emphasis here is on unsolved problems, which distinguishes Hannes’ work from others in the field.” 

Senior co-author Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science who is affiliated with the Jameel Clinic and CSAIL, notes that "models such as BoltzGen that are released fully open-source enable broader community-wide efforts to accelerate drug design capabilities.”

Looking ahead, Stärk believes that the future of biomolecular design will be upended by AI models. “I want to build tools that help us manipulate biology to solve disease, or perform tasks with molecular machines that we have not even imagined yet,” he says. “I want to provide these tools and enable biologists to imagine things that they have not even thought of before.”

The U.S. military is mighty, formidable, and singular in influence, stationed in at least 128 overseas bases across 51 countries. Concealed beneath the United States’ biggest investment is a surprise: The military was responsible for the birth of an industry. Today, that industry is essential for its operations.

“If you think about it, logistics started as a military function,” says Chris Caplice, executive director of the MIT Center for Transportation and Logistics (CTL). “The idea of getting supplies, ammunition, food, all the material you need to the front line was the core of logistics, and really supply chain came out of that over the last decades or centuries.”

For Caplice and the leadership at MIT CTL, a collaboration with the U.S. military seemed inevitable. In 2006, MIT CTL launched the Military Fellows program, wherein three military logistics officers participate in the MIT Supply Chain Management master’s program. “The education goes two ways: One is that these people who have been in the service for more than 20 years step out of their silo and see all the research we’re doing that’s more focused on the private sector, and is cutting-edge. On the other side, you have students who may have never interacted with the military are able to learn from them,” reflects Caplice.

This year’s cohort holds 80 years of combined military service. It comprises Lukas Toth from the Army Reserve, Duston Mullen from the South Dakota Army National Guard, and Charles Greene from the Active Army. Though they work in different components of the U.S. Army, they all agree that their experience in the program so far has been humbling. 

“All of the MIT SCM students have strong academic backgrounds and are exceptionally better at math than us,” Toth laughs. “If you’re coming to this program, you’re sharp and you want to make a difference, not just in your life, but you want to make a difference in the world. Getting to sit in a room with 40 young people who want to make change happen and want to solve complex problems has been super rewarding.”

No strangers to being challenged, adversity is what called the fellows to become logistics officers in the first place. “It comes down to a quote I heard: Operations is easy, fighting the war is easy, but logistics is hard,” says Mullen. 

As logistics officers are responsible for everything from feeding soldiers to fixing trucks to warehousing and distribution, they must perform these functions at varying scales, and with varying threats to their operations. “Our work is: How do we enable the war fighter to be able to deliver when the nation requires? We’re looking at the supply chain and ensuring that we can deliver at the right time, right place, and in the right quantity with precision and accuracy,” says Greene. 

Although companies focus extensively on optimizing their supply chains for cost and efficiency, logistics officers in the military have an additional obstacle. “That last mile could be a contested mile, and the enemy gets a vote,” adds Toth. “At the end of the day, the civilian industry’s consumer has a product they want, and at the end of the day, our war fighters have a product that they want, but we have the added challenge of having to overcome a competitor who may go so far as to destroy us.”

Despite the fellows’ rich practical experience in the military, their academic experience still brings applicable use in terms of introduction to new technologies with which they hope to engage senior military leaders, insight into industry problem-solving to reduce overall military spending and influence decision-making, and, above all, communication. In the military, the stakes are higher than in the private sector, making communication rife with consequence. 

“This experience is helping us better communicate with industry and build an industry and logistics network so that if a challenge does come our country's way, we can better communicate with everybody to solve those challenges,” reflects Toth. 

In 1965, after completing his PhD in civil engineering at MIT, Professor Richard de Neufville joined the first class of White House Fellows, one of the nation’s most prestigious programs for leadership and public service, through which he spent an intensive year working full-time at the highest levels of government. Soon after, de Neufville joined the MIT faculty and led a steering committee that developed what would become the MIT Technology and Policy Program (TPP). TPP was approved in 1975 and launched in 1976 as an Institute-wide hub of education and research, and included a two-year, research-based master’s degree, with de Neufville serving as its founding chair.

This October, TPP held a symposium and celebration at MIT, marking TPP’s 50th year as an interdisciplinary effort focused on advancing the responsible leadership of technology through the integration of technical expertise and rigorous policy analysis in critical areas such as energy, the environment, security, innovation, and beyond.

As the 1988 “TPP Fact Book” stated: “The Technology and Policy Program educates men and women for leadership on the important technological issues confronting society. We prepare our graduates to excel in their technical fields, and to develop and implement effective strategies for dealing with the risks and opportunities associated with those technologies. This kind of education is vital to the future of our society.”

Now in its 50th year, TPP’s legacy of education, research, and impact has shaped more than 1,500 alumni who are among the most distinguished technology policy leaders across the world. TPP alumni often describe the program as life-changing and transformative — an educational experience that shaped their understanding of purpose, systems, and leadership in ways that continue to guide their careers throughout their lives. Today, over 50 TPP graduate students conduct research across the Institute on topics such as energy grid modeling, environmental protection, nuclear safety, industrial decarbonization, space system engineering and public policy, technoeconomic modeling of materials value chains, and governance of global digital systems and artificial intelligence.

Working to bring technically-informed and scientifically robust insights to technology policy is as urgent today as it was 50 years ago, says Christine Ortiz, Morris Cohen Professor of Materials Science and Engineering and the current director of TPP. “The role of technology policy is more essential than ever, helping to shape national and international priorities and underpinning societal and planetary well-being,” said Ortiz in her opening remarks. “Today’s symposium is convened with urgency amid a rapidly shifting landscape. We are situated here today at the epicenter of profound technological advancement, reaffirming our collective responsibility to ensure that innovation advances the well-being of humanity and the health of our planet.”

North stars and new routes

The TPP 50th Anniversary Symposium — North Stars and New Routes — held on Oct. 11, convened more than 630 participants from 30 countries, both in-person and virtually. The gathering brought together alumni, faculty, students, and global leaders to celebrate five decades of impact while exploring bold new directions for the future of technology and policy.

Over the course of seven thematic sessions and 45 speakers, the symposium offered a sweeping view of the current issues shaping the next era of technology policy. Discussions spanned a wide range of topics, including energy systems modeling, global environmental governance, ecologically neutral manufacturing, design of global digital systems, trust as national security infrastructure, the future of technology policy as a domain of scholarship, and the role of technology policy in the future of the research university.

The day opened with a dynamic panel examining the technical frontiers and possibilities of interactive energy systems modeling. Speakers highlighted the dual role of simulation tools as both advanced instruments for understanding decision outcomes and uncertainties, as well participatory platforms for engaging policymakers and stakeholders.

The next session, focused on global environmental governance, explored new approaches to planetary cooperation and emphasized how data-driven policy, equitable technology transfer, and accountability mechanisms can strengthen international climate action. Panelists called for adaptive and integrated governance frameworks that mirror the interconnectedness and complexity of the environmental systems they aim to protect.

In a session on ecologically neutral manufacturing, participants discussed advances in circular materials design and life-cycle modeling that reduce industrial emissions and resource intensity. Speakers underscored the importance of policies promoting reuse, recycling, and cleaner production — linking manufacturing innovation with both economic competitiveness and ecological resilience.

Turning to the design and governance of global digital systems, keynote speaker David Clark, senior research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory and a pioneering architect of the internet, examined how the architecture of digital networks both reflects and shapes societal values, power, and accountability. He noted that the internet’s original open design — built for innovation and resilience — now faces pressing challenges of trust, privacy, and control. The next generation of digital infrastructure, he argued, must embed trust and accountability into its very foundations. The subsequent panel expanded on these themes, exploring how global digital ecosystems are influenced by the competing incentives of governments, corporations, and users. Speakers called for governance models that integrate technical, economic, and ethical considerations — emphasizing that true accountability depends not only on external regulation, but on embedding human values directly into the design of technology.

The theme of trust carried into the next discussion, with the focus on trust as infrastructure for security policy, where experts emphasized that national and global security must evolve to encompass cyber-trust, space governance, and technological resilience as essential infrastructures for stability in an era defined by AI and geopolitical complexity and uncertainty.

In the final session, which explored the role of technology policy in the future of the research university, panelists discussed how research institutions can strengthen their societal role by embedding technology policy and interdisciplinary scholarship into the institutional structure. Speakers emphasized the need for universities to evolve into more cohesive, outward-looking engines of policy innovation — coordinating existing centers of excellence, improving communication between research and government, and expanding educational pathways that integrate engineering, social science, and civic engagement.

Technology, policy, and power

In a keynote address, Senator Edward J. Markey, U.S. senator for Massachusetts, delivered a compelling call for moral and democratic leadership in governing the technologies shaping modern life. He warned that the rapid expansion of artificial intelligence and digital systems has outpaced the ethical and policy frameworks needed to protect society, declaring that “the privacy protections of all preceding generations have broken down.” Markey called for a renewed commitment to AI civil rights and accountability in the digital age, urging that technology must be harnessed as “a tool for connection, not addiction,” and developed to advance human dignity, fairness, and shared prosperity.

Framing technology as both a source of immense potential and a concentration of power, Markey argued that the defining question of our era is who controls that power, and to what end. He urged policymakers, researchers, and citizens alike to ensure that innovation strengthens democracy rather than undermines it. Closing on a note of determination and hope, Markey reminded the audience that technology policy is inseparable from human and planetary well-being: “Technology is power … the question is, who wields it and for what purpose. We must ensure it serves democracy, equality, and the future of our planet.”

New Institute-wide policy initiative announced

The symposium concluded with the announcement of an exciting new Institute-wide initiative, Policy@MIT, introduced by Maria Zuber, E.A. Griswold Professor of Geophysics and Presidential Advisor for Science and Technology Policy. Zuber described the effort as a bold and unifying step to synergize and amplify policy initiatives across MIT, strengthening the Institute’s capacity to inform evidence-based policymaking. 

Building upon the foundational work of TPP — within which the program will serve as a core pillar — Policy@MIT aims to connect MIT’s deep technical expertise with real-world policy challenges, foster collaboration across schools and disciplines, and train the next generation of leaders to ensure that science and technology continue to serve humanity and the planet.

Extending MIT TPP’s legacy of technology and policy leadership 

As MIT charts the next half-century of leadership at the intersection of technology, policy, and society, TPP continues to serve as a cornerstone of this mission. Operating within the MIT Institute for Data, Systems, and Society (IDSS), the MIT School of Engineering, and the MIT Schwarzman College of Computing, TPP distinctively engages and integrates state-of-the-art modeling, simulation, and analytical methods in information and decision systems, statistics and data science, and the computational social sciences, with a diverse range of foundational, emerging, and cross-disciplinary policy analysis methods. Sitting at the confluence of engineering, computer science, and the social sciences, TPP equips students and researchers to study some of the most important and complex emerging issues related to technology through systems thinking, technical rigor, and policy analysis.

Founding IDSS director Munther Dahleh, the William A. Coolidge Professor in Electrical Engineering and Computer Science, described this integration as cultivating the “trilingual student” — someone fluent in data and information, social reasoning, and a technical domain. “What we’re trying to produce in the TPP program,” he explained, “is the person who can navigate all three dimensions of a problem.”

Reflecting on TPP’s enduring mission, Ortiz concluded the symposium, “As we look ahead to the next 50 years, this is a pivotal moment for the Technology and Policy Program — both at MIT and globally. TPP holds tremendous potential for growth, translation, and impact as a leader in technology policy for the nation and the world.”

On a sunny, warm Sunday MIT students, staff, and faculty spread out across the fields of Hannan Healthy Foods in Lincoln, Massachusetts. Some of these volunteers pluck tomatoes from their vines in a patch a few hundred feet from the cars whizzing by on Route 117. Others squat in the shade cast by the greenhouse to snip chives. Still others slice heads of Napa cabbage from their roots in a bed nearer the woods. Everything being harvested today will wind up in Harvest Boxes, which will be sold at a pop-up farm stand the next day in the lobby of the Stata Center back on the MIT campus.

This initiative — a pilot collaboration between MIT’s Office of Sustainability (MITOS), MIT Anthropology, Hannan Healthy Foods, and the nascent MIT Farm student organization — sold six-pound boxes of fresh, organic produce to the MIT community for $10 per box — half off the typical wholesale price. The weekly farm stands ran from Sept. 15 through Oct. 27.

“There is a documented need for accessible, affordable, fresh food on college campuses,” says Heather Paxson, William R. Kenan, Jr. Professor of Anthropology and one of the organizers of the program. “The problems for a small farmer in finding a sufficient market … are connected to the challenges of food insecurity in even wealthy areas. And so, it really is about connecting those dots.”

Through the six weeks of the project, farm stand shoppers purchased more than 2,000 pounds of fresh produce that they wouldn’t otherwise have had access to. Hannan, Paxson, and the team hope that this year’s pilot was successful enough to continue into future growing seasons, either in this farm stand form or as something else that can equally serve the campus community.

“This year we decided to pour our heart, soul, and resources into this vision and prove what’s possible,” says Susy Jones, senior sustainability project manager at MITOS. “How can we do it in a way that is robust and goes through the official MIT channels, and yet pushes the boundaries of what’s possible at MIT?”

A growing idea

Mohammed Hannan, founder of Hannan Healthy Foods, first met Paxson and Jones in 2022. Jones was looking for someone local who grew vegetables common in Asian cuisine in response to a student request. Paxson wanted a small farm to host a field trip for her subject 21A.155 (Food, Culture and Politics). In July, Paxson and Jones learned about an article in the Boston Globe featuring Hannan as an example of a small farmer hit hard by federal budget cuts.

They knew right away they wanted to help. They pulled in Zachary Rapaport and Aleks Banas, architecture master’s students and the co-founders of MIT Farm, an organization dedicated to getting the MIT community off campus and onto local farms. This MIT contingent connected with Hannan to come up with a plan.

“These projects — when they flow, they flow,” says Jones. “There was so much common ground and excitement that we were all willing to jump on calls at 7 p.m. many nights to figure it out.”

After a series of rapid-fire brainstorming sessions, the group decided to host weekly volunteer sessions at Hannan’s farm during the autumn growing season and sell the harvest at a farm stand on campus.

“It fits in seamlessly with the MIT motto, ‘mind and hand,’ ‘mens et manus,’ learning by doing, as well as the heart, which has been added unofficially — mind, hand, heart,” says Paxson.

Jones tapped into the MITOS network for financial, operational, student, and city partners. Rapaport and Banas put out calls for volunteers. Paxson incorporated a volunteer trip into her syllabus and allocated discretionary project funding to subsidize the cost of the produce, allowing the food to be sold at 50 percent of the wholesale price that Hannan was paid for it.

“The fact that MIT students, faculty, and staff could come out to the farm, and that our harvest would circulate back to campus and into the broader community — there’s an energy around it that’s very different from academics. It feels essential to be part of something so tangible,” says Rapaport.

The volunteer sessions proved to be popular. Throughout the pilot, about 75 students and half a dozen faculty and staff trekked out to Lincoln from MIT’s Cambridge, Massachusetts, campus at least once to clear fields and harvest vegetables. Hannan hopes the experience will change the way they think about their food.

“Harvesting the produce, knowing the operation, knowing how hard it is, it’ll stick in their brain,” he says.

On that September Sunday, second-year electrical engineering and computer science major Abrianna Zhang had come out with a friend after seeing a notification on the dormspam email lists. Zhang grew up in a California suburb big on supporting local farmers, but volunteering showed her a different side of the job.

“There’s a lot of work that goes into raising all these crops and then getting all this manual labor,” says Zhang. “It makes me think about the economy of things. How is this even possible … for us to gain access to organic fruits or produce at a reasonable price?”

Setting up shop

Since mid-September, Monday has been Farm Stand day at MIT. Tables covered in green gingham tablecloths strike through the Stata Center lobby, holding stacks of cardboard boxes filled with produce. Customers wait in line to claim their piece of the fresh harvest — carrots, potatoes, onions, tomatoes, herbs, and various greens.

Many of these students typically head to off-campus grocery stores to get their fresh produce. Katie Stabb, a sophomore civil and environmental engineering major and self-proclaimed “crazy plant lady,” grows her own food in the summer, but travels far from campus to shop for her vegetables during the school year. Having this stand right at MIT gives her time back, and she’s been spreading the news to her East Campus dorm mates — even picking boxes up for them when they can’t make it themselves and helping them figure out what to do with their excess ingredients.

“I have encountered having way too many chives before, but that’s new for some folks,” she says. “Last week we pooled all of our chives and I made chive pancakes, kind of like scallion pancakes.”

Stabb is not alone. In a multi-question customer survey conducted at the close of the Farm Stand season, 62 percent of respondents said the Harvest Box gave them the chance to try new foods and 49 percent experimented with new recipes. Seventy percent said this project helped them increase their vegetable intake.

Nearly 60 percent of the survey respondents were graduate students living off campus. Banas, one of the MIT Farm co-leads, is one of those grad students enjoying the benefits.

“I was cooking and making food that I bought from the farm stand and thought, ‘Oh, this is very literally influencing my life in a positive way.’ And I’m hoping that this has a similar impact for other people,” she says.

The impact goes beyond the ability of students to nourish themselves with fresh vegetables. New communities have grown from this collaboration. Jones, for example, expanded her network at MITOS by tapping into expertise and resources from MIT Dining, the Vice President for Finance Merchant Services, and the MIT Federal Credit Union.

“There were just these pockets of people in every corner of MIT who know how to do these very specific things that might seem not very glamorous, but make something like this possible,” says Jones. “It’s such a positive, affirming moment when you’re starting from scratch and someone’s like, ‘This is such a cool idea, how can I help?’”

Strengthening community

Inviting people from MIT to connect across campus and explore beyond Cambridge has helped students and employees alike feel like they’re part of something bigger.

“The community that’s grown around this work is what keeps me so engaged,” says Rapaport. “MIT can have a bit of a siloing effect. It’s easy to become so focused on your classes and academics that your world revolves around them. Farm club grew out of wanting to build connections across the student body and to see ourselves and MIT as part of a larger network of people, communities, and relationships.”

This particular connection will continue to grow, as Rapaport and Banas will use their architectural expertise to lead a design-build team in developing a climate-adaptive and bio-based root cellar at Hannan Healthy Foods, to improve the farm’s winter vegetable storage conditions. 

Community engagement is an ethos Hannan has embraced since the start of his farming journey in 2018, motivated by a desire to provision first his family and then others with healthy food.

“One thing I have done over the years, I was not trying to do farming by myself,” he says. “I always reached out to as many people as I could. The idea is, if community is not involved, they just see it as an individual business.”

It’s why he gifts his volunteers huge bags of tomatoes at the end of a shift, or donates some of his harvest to food banks, or engages an advisory committee of local residents to ensure he’s filling the right needs.

“There’s a reciprocal dimension to gifting that needs to continue,” says Paxson. “That is what builds and maintains community — it’s classic anthropology."

And much of what’s exchanged in this type of reciprocity can’t be charted or graded or marked on a spreadsheet. It’s cooking pancakes with dorm mates. It’s meeting and appreciating new colleagues. It’s grabbing a friend to harvest cabbage on a beautiful autumn Sunday.

“Seeing a student who volunteered over the weekend harvesting chives come to the market on Monday and then want to take a selfie with those chives,” says Jones. “To me, that’s a cool moment.”

At a high level, ammonia seems like a dream fuel: It’s carbon-free, energy-dense, and easier to move and store than hydrogen. Ammonia is also already manufactured and transported at scale, meaning it could transform energy systems using existing infrastructure. But burning ammonia creates dangerous nitrous oxides, and splitting ammonia molecules to create hydrogen fuel typically requires lots of energy and specialized engines.

The startup Amogy, founded by four MIT alumni, believes it has the technology to finally unlock ammonia as a major fuel source. The company has developed a catalyst it says can split — or “crack” — ammonia into hydrogen and nitrogen up to 70 percent more efficiently than state-of-the-art systems today. The company is planning to sell its catalysts as well as modular systems including fuel cells and engines to convert ammonia directly to power. Those systems don’t burn or combust ammonia, and thus bypass the health concerns related to nitrous oxides.

Since Amogy’s founding in 2020, the company has used its ammonia-cracking technology to create the world’s first ammonia-powered drone, tractor, truck, and tugboat. It has also attracted partnerships with industry leaders including Samsung, Saudi Aramco, KBR, and Hyundai, raising more than $300 million along the way.

“No one has showcased that ammonia can be used to power things at the scale of ships and trucks like us,” says CEO Seonghoon Woo PhD ’15, who founded the company with Hyunho Kim PhD ’18, Jongwon Choi PhD ’17, and Young Suk Jo SM ’13, PhD ’16. “We’ve demonstrated this approach works and is scalable.”

Earlier this year, Amogy completed a research and manufacturing facility in Houston and announced a pilot deployment of its catalyst with the global engineering firm JGC Holdings Corporation. Now, with a manufacturing contract secured with Samsung Heavy Industries, Amogy is set to start delivering more of its systems to customers next year. The company will deploy a 1-megawatt ammonia-to-power pilot project with the South Korean city of Pohang in 2026, with plans to scale up to 40 megawatts at that site by 2028 or 2029. Woo says dozens of other projects with multinational corporations are in the works.

Because of the power density advantages of ammonia over renewables and batteries, the company is targeting power-hungry industries like maritime shipping, power generation, construction, and mining for its early systems.

“This is only the beginning,” Woo says. “We’ve worked hard to build the technology and the foundation of our company, but the real value will be generated as we scale. We’ve proved the potential for ammonia to decarbonize heavy industry, and now we really want to accelerate adoption of our technology. We’re thinking long term about the energy transition.”

Unlocking a new fuel source

Woo completed his PhD in MIT’s Department of Materials Science and Engineering before his eventual co-founders, Kim, Choi, and Jo, completed their PhDs in MIT’s Department of Mechanical Engineering. Jo worked on energy science and ran experiments to make engines run more efficiently as part of his PhD.

“The PhD programs at MIT teach you how to think deeply about solving technical problems using systems-based approaches,” Woo says. “You also realize the value in learning from failures, and that mindset of iteration is similar to what you need to do in startups.”

In 2020, Woo was working in the semiconductor industry when he reached out to his eventual co-founders asking if they were working on anything interesting. At that time, Jo was still working on energy systems based on hydrogen and ammonia while Kim was developing new catalysts to create ammonia fuel.

“I wanted to start a company and build a business to do good things for society,” Woo recalls. “People had been talking about hydrogen as a more sustainable fuel source, but it had never come to fruition. We thought there might be a way to improve ammonia catalyst technology and accelerate the hydrogen economy.”

The founders started experimenting with Jo’s technology for ammonia cracking, the process in which ammonia (NH3) molecules split into their nitrogen (N2) and hydrogen (H2) constituent parts. Ammonia cracking to date has been done at huge plants in high-temperature reactors that require large amounts of energy. Those high temperatures limited the catalyst materials that could be used to drive the reaction.

Starting from scratch, the founders were able to identify new material recipes that could be used to miniaturize the catalyst and work at lower temperatures. The proprietary catalyst materials allow the company to create a system that can be deployed in new places at lower costs.

“We really had to redevelop the whole technology, including the catalyst and reformer, and even the integration with the larger system,” Woo says. “One of the most important things is we don’t combust ammonia — we don’t need pilot fuel, and we don’t generate any nitrogen gas or CO2.”

Today Amogy has a portfolio of proprietary catalyst technologies that use base metals along with precious metals. The company has proven the efficiency of its catalysts in demonstrations beginning with the first ammonia-powered drone in 2021. The catalyst can be used to produce hydrogen more efficiently, and by integrating the catalyst with hydrogen fuel cells or engines, Amogy also offers modular ammonia-to-power systems that can scale to meet customer energy demands.

“We’re enabling the decarbonization of heavy industry,” Woo says. “We are targeting transportation, chemical production, manufacturing, and industries that are carbon-heavy and need to decarbonize soon, for example to achieve domestic goals. Our vision in the longer term is to enable ammonia as a fuel in a variety of applications, including power generation, first at microgrids and then eventually full grid-scale.”

Scaling with industry

When Amogy completed its facility in Houston, one of their early visitors was MIT Professor Evelyn Wang, who is also MIT’s vice president for energy and climate. Woo says other people involved in the Climate Project at MIT have been supportive.

Another key partner for Amogy is Samsung Heavy Industries, which announced a multiyear deal to manufacturing Amogy’s ammonia-to-power systems on Nov. 12.

“Our strategy is to partner with the existing big players in heavy industry to accelerate the commercialization of our technology,” Woo says. “We have worked with big oil and gas companies like BHP and Saudi Aramco, companies interested in hydrogen fuel like KBR and Mitsubishi, and many more industrial companies.”

When paired with other clean energy technologies to provide the power for its systems, Woo says Amogy offers a way to completely decarbonize sectors of the economy that can’t electrify on their own.

“In heavy transport, you have to use high-energy density liquid fuel because of the long distances and power requirements,” Woo says. “Batteries can’t meet those requirements. It’s why hydrogen is such an exciting molecule for heavy industry and shipping. But hydrogen needs to be kept super cold, whereas ammonia can be liquid at room temperature. Our job now is to provide that power at scale.”

There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.

For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.

On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.

Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.

Controlling real-time operations

Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.

But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”

The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.

Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.

Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.

In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.

Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”

Using AI to help plan investments in infrastructure for the future

Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.

One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.

And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.

Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.

Harnessing AI to discover and exploit advanced materials needed for the energy transition

“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.

First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.

And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”

He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”

The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.

“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” 

MITEI’s contributions

At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.

Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.

In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”