MIT engineers have developed a technique to grow and peel ultrathin “skins” of electronic material. The method could pave the way for new classes of electronic devices, such as ultrathin wearable sensors, flexible transistors and computing elements, and highly sensitive and compact imaging devices. 

As a demonstration, the team fabricated a thin membrane of pyroelectric material — a class of heat-sensing material that produces an electric current in response to changes in temperature. The thinner the pyroelectric material, the better it is at sensing subtle thermal variations.

With their new method, the team fabricated the thinnest pyroelectric membrane yet, measuring 10 nanometers thick, and demonstrated that the film is highly sensitive to heat and radiation across the far-infrared spectrum.

The newly developed film could enable lighter, more portable, and highly accurate far-infrared (IR) sensing devices, with potential applications for night-vision eyewear and autonomous driving in foggy conditions. Current state-of-the-art far-IR sensors require bulky cooling elements. In contrast, the new pyroelectric thin film requires no cooling and is sensitive to much smaller changes in temperature. The researchers are exploring ways to incorporate the film into lighter, higher-precision night-vision glasses.

“This film considerably reduces weight and cost, making it lightweight, portable, and easier to integrate,” Xinyuan Zhang, a graduate student in MIT’s Department of Materials Science and Engineering (DMSE). “For example, it could be directly worn on glasses.”

The heat-sensing film could also have applications in environmental and biological sensing, as well as imaging of astrophysical phenomena that emit far-infrared radiation.

What’s more, the new lift-off technique is generalizable beyond pyroelectric materials. The researchers plan to apply the method to make other ultrathin, high-performance semiconducting films.

Their results are reported today in a paper appearing in the journal Nature. The study’s MIT co-authors are first author Xinyuan Zhang, Sangho Lee, Min-Kyu Song, Haihui Lan, Jun Min Suh, Jung-El Ryu, Yanjie Shao, Xudong Zheng, Ne Myo Han, and Jeehwan Kim, associate professor of mechanical engineering and of materials science and engineering, along with researchers at the University Wisconsin at Madison led by Professor Chang-Beom Eom and authors from multiple other institutions.

Chemical peel

Kim’s group at MIT is finding new ways to make smaller, thinner, and more flexible electronics. They envision that such ultrathin computing “skins” can be incorporated into everything from smart contact lenses and wearable sensing fabrics to stretchy solar cells and bendable displays. To realize such devices, Kim and his colleagues have been experimenting with methods to grow, peel, and stack semiconducting elements, to fabricate ultrathin, multifunctional electronic thin-film membranes.

One method that Kim has pioneered is “remote epitaxy” — a technique where semiconducting materials are grown on a single-crystalline substrate, with an ultrathin layer of graphene in between. The substrate’s crystal structure serves as a scaffold along which the new material can grow. The graphene acts as a nonstick layer, similar to Teflon, making it easy for researchers to peel off the new film and transfer it onto flexible and stacked electronic devices. After peeling off the new film, the underlying substrate can be reused to make additional thin films.

Kim has applied remote epitaxy to fabricate thin films with various characteristics. In trying different combinations of semiconducting elements, the researchers happened to notice that a certain pyroelectric material, called PMN-PT, did not require an intermediate layer assist in order to separate from its substrate. Just by growing PMN-PT directly on a single-crystalline substrate, the researchers could then remove the grown film, with no rips or tears to its delicate lattice.

“It worked surprisingly well,” Zhang says. “We found the peeled film is atomically smooth.”

Lattice lift-off

In their new study, the MIT and UW Madison researchers took a closer look at the process and discovered that the key to the material’s easy-peel property was lead. As part of its chemical structure, the team, along with colleagues at the Rensselaer Polytechnic Institute, discovered that the pyroelectric film contains an orderly arrangement of lead atoms that have a large “electron affinity,” meaning that lead attracts electrons and prevents the charge carriers from traveling and connecting to another materials such as an underlying substrate. The lead acts as tiny nonstick units, allowing the material as a whole to peel away, perfectly intact.

The team ran with the realization and fabricated multiple ultrathin films of PMN-PT, each about 10 nanometers thin. They peeled off pyroelectric films and transfered them onto a small chip to form an array of 100 ultrathin heat-sensing pixels, each about 60 square microns (about .006 square centimeters). They exposed the films to ever-slighter changes in temperature and found the pixels were highly sensitive to small changes across the far-infrared spectrum.

The sensitivity of the pyroelectric array is comparable to that of state-of-the-art night-vision devices. These devices are currently based on photodetector materials, in which a change in temperature induces the material’s electrons to jump in energy and briefly cross an energy “band gap,” before settling back into their ground state. This electron jump serves as an electrical signal of the temperature change. However, this signal can be affected by noise in the environment, and to prevent such effects, photodetectors have to also include cooling devices that bring the instruments down to liquid nitrogen temperatures.

Current night-vision goggles and scopes are heavy and bulky. With the group’s new pyroelectric-based approach, NVDs could have the same sensitivity without the cooling weight.

The researchers also found that the films were sensitive beyond the range of current night-vision devices and could respond to wavelengths across the entire infrared spectrum. This suggests that the films could be incorporated into small, lightweight, and portable devices for various applications that require different infrared regions. For instance, when integrated into autonomous vehicle platforms, the films could enable cars to “see” pedestrians and vehicles in complete darkness or in foggy and rainy conditions. 

The film could also be used in gas sensors for real-time and on-site environmental monitoring, helping detect pollutants. In electronics, they could monitor heat changes in semiconductor chips to catch early signs of malfunctioning elements.

The team says the new lift-off method can be generalized to materials that may not themselves contain lead. In those cases, the researchers suspect that they can infuse Teflon-like lead atoms into the underlying substrate to induce a similar peel-off effect. For now, the team is actively working toward incorporating the pyroelectric films into a functional night-vision system.

“We envision that our ultrathin films could be made into high-performance night-vision goggles, considering its broad-spectrum infrared sensitivity at room-temperature, which allows for a lightweight design without a cooling system,” Zhang says. “To turn this into a night-vision system, a functional device array should be integrated with readout circuitry. Furthermore, testing in varied environmental conditions is essential for practical applications.”

This work was supported by the U.S. Air Force Office of Scientific Research.

When chemists design new chemical reactions, one useful piece of information involves the reaction’s transition state — the point of no return from which a reaction must proceed.

This information allows chemists to try to produce the right conditions that will allow the desired reaction to occur. However, current methods for predicting the transition state and the path that a chemical reaction will take are complicated and require a huge amount of computational power.

MIT researchers have now developed a machine-learning model that can make these predictions in less than a second, with high accuracy. Their model could make it easier for chemists to design chemical reactions that could generate a variety of useful compounds, such as pharmaceuticals or fuels.

“We’d like to be able to ultimately design processes to take abundant natural resources and turn them into molecules that we need, such as materials and therapeutic drugs. Computational chemistry is really important for figuring out how to design more sustainable processes to get us from reactants to products,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering, a professor of chemistry, and the senior author of the new study.

Former MIT graduate student Chenru Duan PhD ’22, who is now at Deep Principle; former Georgia Tech graduate student Guan-Horng Liu, who is now at Meta; and Cornell University graduate student Yuanqi Du are the lead authors of the paper, which appears today in Nature Machine Intelligence.

Better estimates

For any given chemical reaction to occur, it must go through a transition state, which takes place when it reaches the energy threshold needed for the reaction to proceed. These transition states are so fleeting that they’re nearly impossible to observe experimentally.

As an alternative, researchers can calculate the structures of transition states using techniques based on quantum chemistry. However, that process requires a great deal of computing power and can take hours or days to calculate a single transition state.

“Ideally, we’d like to be able to use computational chemistry to design more sustainable processes, but this computation in itself is a huge use of energy and resources in finding these transition states,” Kulik says.

In 2023, Kulik, Duan, and others reported on a machine-learning strategy that they developed to predict the transition states of reactions. This strategy is faster than using quantum chemistry techniques, but still slower than what would be ideal because it requires the model to generate about 40 structures, then run those predictions through a “confidence model” to predict which states were most likely to occur.

One reason why that model needs to be run so many times is that it uses randomly generated guesses for the starting point of the transition state structure, then performs dozens of calculations until it reaches its final, best guess. These randomly generated starting points may be very far from the actual transition state, which is why so many steps are needed.

The researchers’ new model, React-OT, described in the Nature Machine Intelligence paper, uses a different strategy. In this work, the researchers trained their model to begin from an estimate of the transition state generated by linear interpolation — a technique that estimates each atom’s position by moving it halfway between its position in the reactants and in the products, in three-dimensional space.

“A linear guess is a good starting point for approximating where that transition state will end up,” Kulik says. “What the model’s doing is starting from a much better initial guess than just a completely random guess, as in the prior work.”

Because of this, it takes the model fewer steps and less time to generate a prediction. In the new study, the researchers showed that their model could make predictions with only about five steps, taking about 0.4 seconds. These predictions don’t need to be fed through a confidence model, and they are about 25 percent more accurate than the predictions generated by the previous model.

“That really makes React-OT a practical model that we can directly integrate to the existing computational workflow in high-throughput screening to generate optimal transition state structures,” Duan says.

“A wide array of chemistry”

To create React-OT, the researchers trained it on the same dataset that they used to train their older model. These data contain structures of reactants, products, and transition states, calculated using quantum chemistry methods, for 9,000 different chemical reactions, mostly involving small organic or inorganic molecules.

Once trained, the model performed well on other reactions from this set, which had been held out of the training data. It also performed well on other types of reactions that it hadn’t been trained on, and could make accurate predictions involving reactions with larger reactants, which often have side chains that aren’t directly involved in the reaction.

“This is important because there are a lot of polymerization reactions where you have a big macromolecule, but the reaction is occurring in just one part. Having a model that generalizes across different system sizes means that it can tackle a wide array of chemistry,” Kulik says.

The researchers are now working on training the model so that it can predict transition states for reactions between molecules that include additional elements, including sulfur, phosphorus, chlorine, silicon, and lithium.

“To quickly predict transition state structures is key to all chemical understanding,” says Markus Reiher, a professor of theoretical chemistry at ETH Zurich, who was not involved in the study. “The new approach presented in the paper could very much accelerate our search and optimization processes, bringing us faster to our final result. As a consequence, also less energy will be consumed in these high-performance computing campaigns. Any progress that accelerates this optimization benefits all sorts of computational chemical research.”

The MIT team hopes that other scientists will make use of their approach in designing their own reactions, and have created an app for that purpose.

“Whenever you have a reactant and product, you can put them into the model and it will generate the transition state, from which you can estimate the energy barrier of your intended reaction, and see how likely it is to occur,” Duan says.

The research was funded by the U.S. Army Research Office, the U.S. Department of Defense Basic Research Office, the U.S. Air Force Office of Scientific Research, the National Science Foundation, and the U.S. Office of Naval Research.

In metamaterials design, the name of the game has long been “stronger is better.”

Metamaterials are synthetic materials with microscopic structures that give the overall material exceptional properties. A huge focus has been in designing metamaterials that are stronger and stiffer than their conventional counterparts. But there’s a trade-off: The stiffer a material, the less flexible it is.

MIT engineers have now found a way to fabricate a metamaterial that is both strong and stretchy. The base material is typically highly rigid and brittle, but it is printed in precise, intricate patterns that form a structure that is both strong and flexible.

The key to the new material’s dual properties is a combination of stiff microscopic struts and a softer woven architecture. This microscopic “double network,” which is printed using a plexiglass-like polymer, produced a material that could stretch over four times its size without fully breaking. In comparison, the polymer in other forms has little to no stretch and shatters easily once cracked.

The researchers say the new double-network design can be applied to other materials, for instance to fabricate stretchy ceramics, glass, and metals. Such tough yet bendy materials could be made into tear-resistant textiles, flexible semiconductors, electronic chip packaging, and durable yet compliant scaffolds on which to grow cells for tissue repair.

“We are opening up this new territory for metamaterials,” says Carlos Portela, the Robert N. Noyce Career Development Associate Professor at MIT. “You could print a double-network metal or ceramic, and you could get a lot of these benefits, in that it would take more energy to break them, and they would be significantly more stretchable.”

Portela and his colleagues report their findings today in the journal Nature Materials. His MIT co-authors include first author James Utama Surjadi as well as Bastien Aymon and Molly Carton.

Inspired gel

Along with other research groups, Portela and his colleagues have typically designed metamaterials by printing or nanofabricating microscopic lattices using conventional polymers similar to plexiglass and ceramic. The specific pattern, or architecture, that they print can impart exceptional strength and impact resistance to the resulting metamaterial.

Several years ago, Portela was curious whether a metamaterial could be made from an inherently stiff material, but be patterned in a way that would turn it into a much softer, stretchier version.

“We realized that the field of metamaterials has not really tried to make an impact in the soft matter realm,” he says. “So far, we’ve all been looking for the stiffest and strongest materials possible.”

Instead, he looked for a way to synthesize softer, stretchier metamaterials. Rather than printing microscopic struts and trusses, similar to those of conventional lattice-based metamaterials, he and his team made an architecture of interwoven springs, or coils. They found that, while the material they used was itself stiff like plexiglass, the resulting woven metamaterial was soft and springy, like rubber.

“They were stretchy, but too soft and compliant,” Portela recalls.

In looking for ways to bulk up their softer metamaterial, the team found inspiration in an entirely different material: hydrogel. Hydrogels are soft, stretchy, Jell-O-like materials that are composed of mostly water and a bit of polymer structure. Researchers including groups at MIT have devised ways to make hydrogels that are both soft and stretchy, and also tough. They do so by combining polymer networks with very different properties, such as a network of molecules that is naturally stiff,  which gets chemically cross-linked with another molecular network that is inherently soft. Portela and his colleagues wondered whether such a double-network design could be adapted to metamaterials.

“That was our ‘aha’ moment,” Portela says. “We thought: Can we get inspiration from these hydrogels to create a metamaterial with similar stiff and stretchy properties?”

Strut and weave

For their new study, the team fabricated a metamaterial by combining two microscopic architectures. The first is a rigid, grid-like scaffold of struts and trusses. The second is a pattern of coils that weave around each strut and truss. Both networks are made from the same acrylic plastic and are printed in one go, using a high-precision, laser-based printing technique called two-photon lithography.

The researchers printed samples of the new double-network-inspired metamaterial, each measuring in size from several square microns to several square millimeters. They put the material through a series of stress tests, in which they attached either end of the sample to a specialized nanomechanical press and measured the force it took to pull the material apart. They also recorded high-resolution videos to observe the locations and ways in which the material stretched and tore as it was pulled apart.

They found their new double-network design was able stretch three times its own length, which also happened to be 10 times farther compared to a conventional lattice-patterned metamaterial printed with the same acrylic plastic. Portela says the new material’s stretchy resistance comes from the interactions between the material’s rigid struts and the messier, coiled weave as the material is stressed and pulled.

“Think of this woven network as a mess of spaghetti tangled around a lattice. As we break the monolithic lattice network, those broken parts come along for the ride, and now all this spaghetti gets entangled with the lattice pieces,” Portela explains. “That promotes more entanglement between woven fibers, which means you have more friction and more energy dissipation.”

In other words, the softer structure wound throughout the material’s rigid lattice takes on more stress thanks to multiple knots or entanglements promoted by the cracked struts. As this stress spreads unevenly through the material, an initial crack is unlikely to go straight through and quickly tear the material. What’s more, the team found that if they introduced strategic holes, or “defects,” in the metamaterial, they could further dissipate any stress that the material undergoes, making it even stretchier and more resistant to tearing apart.

“You might think this makes the material worse,” says study co-author Surjadi. “But we saw once we started adding defects, we doubled the amount of stretch we were able to do, and tripled the amount of energy that we dissipated. That gives us a material that’s both stiff and tough, which is usually a contradiction.”

The team has developed a computational framework that can help engineers estimate how a metamaterial will perform given the pattern of its stiff and stretchy networks. They envision such a blueprint will be useful in designing tear-proof textiles and fabrics.

“We also want to try this approach on more brittle materials, to give them multifunctionality,” Portela says. “So far we’ve talked of mechanical properties, but what if we could also make them conductive, or responsive to temperature? For that, the two networks could be made from different polymers, that respond to temperature in different ways, so that a fabric can open its pores or become more compliant when it’s warm and can be more rigid when it’s cold. That’s something we can explore now.”

This research was supported, in part, by the U.S. National Science Foundation, and the MIT MechE MathWorks Seed Fund. This work was performed, in part, through the use of MIT.nano’s facilities.

Amama has lived in a rural region of northern Ghana all her life. In 2022, she went into labor with her first child. Women in the region traditionally give birth at home with the help of a local birthing attendant, but Amama experienced last-minute complications, and the decision was made to go to a hospital. Unfortunately, there were no ambulances in the community and the nearest hospital was 30 minutes away, so Amama was forced to take a motorcycle taxi, leaving her husband and caregiver behind.

Amama spent the next 30 minutes traveling over bumpy dirt roads to get to the hospital. She was in pain and afraid. When she arrived, she learned her child had not survived.

Unfortunately, Amama’s story is not unique. Around the world, more than 700 women die every day due to preventable pregnancy and childbirth complications. A lack of transportation to hospitals contributes to those deaths.

Moving Health was founded by MIT students to give people like Amama a safer way to get to the hospital. The company, which was started as part of a class at MIT D-Lab, works with local communities in rural Ghana to offer a network of motorized tricycle ambulances to communities that lack emergency transportation options.

The locally made ambulances are designed for the challenging terrain of rural Ghana, equipped with medical supplies, and have space for caregivers and family members.

“We’re providing the first rural-focused emergency transportation network,” says Moving Health CEO and co-founder Emily Young ’18. “We’re trying to provide emergency transportation coverage for less cost and with a vehicle tailored to local needs. When we first started, a report estimated there were 55 ambulances in the country of over 30 million people. Now, there is more coverage, but still the last mile areas of the country do not have access to reliable emergency transportation.”

Today, Moving Health’s ambulances and emergency transportation network cover more than 100,000 people in northern Ghana who previously lacked reliable medical transportation.

One of those people is Amama. During her most recent pregnancy, she was able to take a Moving Health ambulance to the hospital. This time, she traveled in a sanitary environment equipped with medical supplies and surrounded by loved ones. When she arrived, she gave birth to healthy twins.

From class project to company

Young and Sade Nabahe ’17, SM ’21 met while taking Course 2.722J (D-Lab: Design), which challenges students to think like engineering consultants on international projects. Their group worked on ways to transport pregnant women in remote areas of Tanzania to hospitals more safely and quickly. Young credits D-Lab instructor Matt McCambridge with helping students explore the project outside of class. Fellow Moving Health co-founder Eva Boal ’18 joined the effort the following year.

The early idea was to build a trailer that could attach to any motorcycle and be used to transport women. Following the early class projects, the students received funding from MIT’s PKG Center and the MIT Undergraduate Giving Campaign, which they used to travel to Tanzania in the following year’s Independent Activities Period (IAP). That’s when they built their first prototype in the field.

The founders realized they needed to better understand the problem from the perspective of locals and interviewed over 250 pregnant women, clinicians, motorcycle drivers, and birth attendants.

“We wanted to make sure the community was leading the charge to design what this solution should be. We had to learn more from the community about why emergency transportation doesn’t work in these areas,” Young says. “We ended up redesigning our vehicle completely.”

Following their graduation from MIT in 2018, the founders bought one-way tickets to Tanzania and deployed a new prototype. A big part of their plans was creating a product that could be manufactured by the community to support the local economy.

Nabahe and Boal left the company in 2020, but word spread of Moving Health’s mission, and Young received messages from organizations in about 15 different countries interested in expanding the company’s trials.

Young found the most alignment in Ghana, where she met two local engineers, Ambra Jiberu and Sufiyanu Imoro, who were building cars from scratch and inventing innovative agricultural technologies. With these two engineers joining the team, she was confident they had the team to build a solution in Ghana.

Taking what they’d learned in Tanzania, the new team set up hundreds of interviews and focus groups to understand the Ghanaian health system. The team redesigned their product to be a fully motorized tricycle based on the most common mode of transportation in northern Ghana. Today Moving Health focuses solely on Ghana, with local manufacturing and day-to-day operations led by Country Director and CTO Isaac Quansah.

Moving Health is focused on building a holistic emergency transportation network. To do this, Moving Health’s team sets up community-run dispatch systems, which involves organizing emergency phone numbers, training community health workers, dispatchers, and drivers, and integrating all of that within the existing health care system. The company also conducts educational campaigns in the communities it serves.

Moving Health officially launched its ambulances in 2023. The ambulance has an enclosed space for patients, family members, and medical providers and includes a removable stretcher along with supplies like first aid equipment, oxygen, IVs, and more. It costs about one-tenth the price of a traditional ambulance.

“We’ve built a really cool, small-volume manufacturing facility, led by our local engineering team, that has incredible quality,” Young says. “We also have an apprenticeship program that our two lead engineers run that allows young people to learn more hard skills. We want to make sure we’re providing economic opportunities in these communities. It’s very much a Ghanaian-made solution.”

Unlike the national ambulances, Moving Health’s ambulances are stationed in rural communities, at community health centers, to enable faster response times.

“When the ambulances are stationed in these people’s communities, at their local health centers, it makes all the difference,” Young says. “We’re trying to create an emergency transportation solution that is not only geared toward rural areas, but also focused on pregnancy and prioritizing women’s voices about what actually works in these areas.”

A lifeline for mothers

When Young first got to Ghana, she met Sahada, a local woman who shared the story of her first birth at the age of 18. Sahada had intended to give birth in her community with the help of a local birthing attendant, but she began experiencing so much pain during labor the attendant advised her to go to the nearest hospital. With no ambulances or vehicles in town, Sahada’s husband called a motorcycle driver, who took her alone on the three-hour drive to the nearest hospital.

“It was rainy, extremely muddy, and she was in a lot of pain,” Young recounts. “She was already really worried for her baby, and then the bike slips and they crash. They get back on, covered in mud, she has no idea if the baby survived, and finally gets to the maternity ward.”

Sahada was able to give birth to a healthy baby boy, but her story stuck with Young.

“The experience was extremely traumatic, and what’s really crazy is that counts as a successful birth statistic,” Young says. “We hear that kind of story a lot.”

This year, Moving Health plans to expand into a new region of northern Ghana. The team is also exploring other ways their network can provide health care to rural regions. But no matter how the company evolves, the team remain grateful to have seen their D-Lab project turn into such an impactful solution.

“Our long-term vision is to prove that this can work on a national level and supplement the existing health system,” Young says. “Then we’re excited to explore mobile health care outreach and other transportation solutions. We’ve always been focused on maternal health, but we’re staying cognizant of other community ideas that might be able to help improve health care more broadly.”

MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.

For instance, the researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state-of-the-art approaches.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same.

Building on these insights, the researchers identified a unifying equation that underlies many classical AI algorithms. They used that equation to reframe popular methods and arrange them into a table, categorizing each based on the approximate relationships it learns.

Just like the periodic table of chemical elements, which initially contained blank squares that were later filled in by scientists, the periodic table of machine learning also has empty spaces. These spaces predict where algorithms should exist, but which haven’t been discovered yet.

The table gives researchers a toolkit to design new algorithms without the need to rediscover ideas from prior approaches, says Shaden Alshammari, an MIT graduate student and lead author of a paper on this new framework.

“It’s not just a metaphor,” adds Alshammari. “We’re starting to see machine learning as a system with structure that is a space we can explore rather than just guess our way through.”

She is joined on the paper by John Hershey, a researcher at Google AI Perception; Axel Feldmann, an MIT graduate student; William Freeman, the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Mark Hamilton, an MIT graduate student and senior engineering manager at Microsoft. The research will be presented at the International Conference on Learning Representations.

An accidental equation

The researchers didn’t set out to create a periodic table of machine learning.

After joining the Freeman Lab, Alshammari began studying clustering, a machine-learning technique that classifies images by learning to organize similar images into nearby clusters.

She realized the clustering algorithm she was studying was similar to another classical machine-learning algorithm, called contrastive learning, and began digging deeper into the mathematics. Alshammari found that these two disparate algorithms could be reframed using the same underlying equation.

“We almost got to this unifying equation by accident. Once Shaden discovered that it connects two methods, we just started dreaming up new methods to bring into this framework. Almost every single one we tried could be added in,” Hamilton says.

The framework they created, information contrastive learning (I-Con), shows how a variety of algorithms can be viewed through the lens of this unifying equation. It includes everything from classification algorithms that can detect spam to the deep learning algorithms that power LLMs.

The equation describes how such algorithms find connections between real data points and then approximate those connections internally.

Each algorithm aims to minimize the amount of deviation between the connections it learns to approximate and the real connections in its training data.

They decided to organize I-Con into a periodic table to categorize algorithms based on how points are connected in real datasets and the primary ways algorithms can approximate those connections.

“The work went gradually, but once we had identified the general structure of this equation, it was easier to add more methods to our framework,” Alshammari says.

A tool for discovery

As they arranged the table, the researchers began to see gaps where algorithms could exist, but which hadn’t been invented yet.

The researchers filled in one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. This resulted in a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach.

They also used I-Con to show how a data debiasing technique developed for contrastive learning could be used to boost the accuracy of clustering algorithms.

In addition, the flexible periodic table allows researchers to add new rows and columns to represent additional types of datapoint connections.

Ultimately, having I-Con as a guide could help machine learning scientists think outside the box, encouraging them to combine ideas in ways they wouldn’t necessarily have thought of otherwise, says Hamilton.

“We’ve shown that just one very elegant equation, rooted in the science of information, gives you rich algorithms spanning 100 years of research in machine learning. This opens up many new avenues for discovery,” he adds.

“Perhaps the most challenging aspect of being a machine-learning researcher these days is the seemingly unlimited number of papers that appear each year. In this context, papers that unify and connect existing algorithms are of great importance, yet they are extremely rare. I-Con provides an excellent example of such a unifying approach and will hopefully inspire others to apply a similar approach to other domains of machine learning,” says Yair Weiss, a professor in the School of Computer Science and Engineering at the Hebrew University of Jerusalem, who was not involved in this research.

This research was funded, in part, by the Air Force Artificial Intelligence Accelerator, the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions, and Quanta Computer.

Kripa Varanasi, professor of mechanical engineering, was named faculty director of the MIT Deshpande Center for Technological Innovation, effective March 1.

“Kripa is widely recognized for his significant contributions in the field of interfacial science, thermal fluids, electrochemical systems, and advanced materials. It’s remarkable to see the tangible impact Kripa’s ventures have made across such a wide range of fields,” says Anantha P. Chandrakasan, dean of the School of Engineering, chief innovation and strategy officer, and Vannevar Bush Professor of Electrical Engineering and Computer Science. “From energy and water conservation to consumer products and agriculture, his solutions are making a real difference. The Deshpande Center will benefit greatly from both his entrepreneurial expertise and deep technical insight.”

The MIT Deshpande Center for Technological Innovation is an interdepartmental center that empowers MIT students and faculty to make a difference in the world by helping them bring their innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies. The center was established through a gift from philanthropist Guruaj “Desh” Deshpande and his wife, Jaishree.

“Kripa brings an entrepreneurial spirit, innovative thinking, and commitment to mentorship that has always been central to the Deshpande Center’s mission,” says Deshpande. “He is exceptionally well-positioned to help the next generation of MIT innovators turn bold ideas into real-world solutions that make a difference.”

Varanasi has seen the Deshpande Center’s influence on the MIT community since its founding in 2002, when he was a graduate student.

“The Deshpande Center was founded when I was a graduate student, and it truly inspired many of us to think about entrepreneurship and commercialization — with Desh himself being an incredible role model,” says Varanasi. “Over the years, the center has built a storied legacy as a one-of-a-kind institution for propelling university-invented technologies to commercialization. Many amazing companies have come out of this program, shaping industries and making a real impact.”

A member of the MIT faculty since 2009, Varanasi leads the interdisciplinary Varanasi Research Group, which focuses on understanding physico-chemical and biological phenomena at the interfaces of matter. His group develops novel surfaces, materials, and technologies that improve efficiency and performance across industries, including energy, decarbonization, life sciences, water, agriculture, transportation, and consumer products.

In addition to his academic work, Varanasi is a prolific entrepreneur who has co-founded six companies, including AgZen, Alsym Energy, CoFlo Medical, Dropwise, Infinite Cooling, and LiquiGlide, which was a Deshpande Center grantee in 2009. These ventures aim to translate research breakthroughs into products with global reach.

His companies have been widely recognized for driving innovation across a range of industries. LiquiGlide, which produces frictionless liquid coatings, was named one of Time and Forbes’ “Best Inventions of the Year” in 2012. Infinite Cooling, which offers a technology to capture and recycle power plant water vapor, has won the U.S. Department of Energy’s National Cleantech University Prize and top prizes at MassChallenge and the MIT $100K competition. It is also a participating company at this year’s IdeaStream: Next Gen event, hosted by the Deshpande Center.

Another company that Varanasi co-founded, AgZen, is pioneering feedback optimization for agrochemical application that allows farmers to use 30-90 percent less pesticides and fertilizers while achieving 1-10 percent more yield. Meanwhile, Alsym Energy is advancing nonflammable, high-performance batteries for energy storage solutions that are lithium-free and capable of a wide range of storage durations. 

Throughout his career, Varanasi has been recognized for both research excellence and mentorship. His honors include the National Science Foundation CAREER Award, DARPA Young Faculty Award, SME Outstanding Young Manufacturing Engineer Award, ASME’s Bergles-Rohsenow Heat Transfer Award and Gustus L. Larson Memorial Award, Boston Business Journal’s 40 Under 40, and MIT’s Frank E. Perkins Award for Excellence in Graduate Advising​.

Varanasi earned his undergraduate degree in mechanical engineering from the Indian Institute of Technology Madras, and his master’s degree and PhD from MIT. Prior to joining the Institute’s faculty, he served as lead researcher and project leader at the GE Global Research Center, where he received multiple internal awards for innovation and technical excellence​.

"It’s an honor to lead the Deshpande Center, and in collaboration with the MIT community, I look forward to building on its incredible foundation — fostering bold ideas, driving real-world impact from cutting-edge innovations, and making it a powerhouse for commercialization,” adds Varanasi.

As faculty director, Varanasi will work closely with Deshpande Center executive director Rana Gupta to guide the center’s support of MIT faculty and students developing technology-based ventures.

“With Kripa’s depth and background, we will capitalize on the initiatives started with Angela Koehler. Kripa shares our vision to grow and expand the center’s capabilities to serve more of MIT,” adds Gupta.

Varanasi succeeds Angela Koehler, associate professor of biological engineering, who served as faculty director from July 2023 through March 2025.

“Angela brought fresh vision and energy to the center,” he says. “She expanded its reach, introduced new funding priorities in climate and life sciences, and re-imagined the annual IdeaStream event as a more robust launchpad for innovation. We’re deeply grateful for her leadership.”

Koehler, who was recently appointed faculty lead of the MIT Health and Life Sciences Collaborative, will continue to play a key role in the Institute’s innovation and entrepreneurship ecosystem​.

Essential for many industries ranging from Hollywood computer-generated imagery to product design, 3D modeling tools often use text or image prompts to dictate different aspects of visual appearance, like color and form. As much as this makes sense as a first point of contact, these systems are still limited in their realism due to their neglect of something central to the human experience: touch.

Fundamental to the uniqueness of physical objects are their tactile properties, such as roughness, bumpiness, or the feel of materials like wood or stone. Existing modeling methods often require advanced computer-aided design expertise and rarely support tactile feedback that can be crucial for how we perceive and interact with the physical world.

With that in mind, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have created a new system for stylizing 3D models using image prompts, effectively replicating both visual appearance and tactile properties.

The CSAIL team’s “TactStyle” tool allows creators to stylize 3D models based on images while also incorporating the expected tactile properties of the textures. TactStyle separates visual and geometric stylization, enabling the replication of both visual and tactile properties from a single image input.

PhD student Faraz Faruqi, lead author of a new paper on the project, says that TactStyle could have far-reaching applications, extending from home decor and personal accessories to tactile learning tools. TactStyle enables users to download a base design — such as a headphone stand from Thingiverse — and customize it with the styles and textures they desire. In education, learners can explore diverse textures from around the world without leaving the classroom, while in product design, rapid prototyping becomes easier as designers quickly print multiple iterations to refine tactile qualities.

“You could imagine using this sort of system for common objects, such as phone stands and earbud cases, to enable more complex textures and enhance tactile feedback in a variety of ways,” says Faruqi, who co-wrote the paper alongside MIT Associate Professor Stefanie Mueller, leader of the Human-Computer Interaction (HCI) Engineering Group at CSAIL. “You can create tactile educational tools to demonstrate a range of different concepts in fields such as biology, geometry, and topography.”

Traditional methods for replicating textures involve using specialized tactile sensors — such as GelSight, developed at MIT — that physically touch an object to capture its surface microgeometry as a “heightfield.” But this requires having a physical object or its recorded surface for replication. TactStyle allows users to replicate the surface microgeometry by leveraging generative AI to generate a heightfield directly from an image of the texture.

On top of that, for platforms like the 3D printing repository Thingiverse, it’s difficult to take individual designs and customize them. Indeed, if a user lacks sufficient technical background, changing a design manually runs the risk of actually “breaking” it so that it can’t be printed anymore. All of these factors spurred Faruqi to wonder about building a tool that enables customization of downloadable models on a high level, but that also preserves functionality.

In experiments, TactStyle showed significant improvements over traditional stylization methods by generating accurate correlations between a texture’s visual image and its heightfield. This enables the replication of tactile properties directly from an image. One psychophysical experiment showed that users perceive TactStyle’s generated textures as similar to both the expected tactile properties from visual input and the tactile features of the original texture, leading to a unified tactile and visual experience.

TactStyle leverages a preexisting method, called “Style2Fab,” to modify the model’s color channels to match the input image’s visual style. Users first provide an image of the desired texture, and then a fine-tuned variational autoencoder is used to translate the input image into a corresponding heightfield. This heightfield is then applied to modify the model’s geometry to create the tactile properties.

The color and geometry stylization modules work in tandem, stylizing both the visual and tactile properties of the 3D model from a single image input. Faruqi says that the core innovation lies in the geometry stylization module, which uses a fine-tuned diffusion model to generate heightfields from texture images — something previous stylization frameworks do not accurately replicate.

Looking ahead, Faruqi says the team aims to extend TactStyle to generate novel 3D models using generative AI with embedded textures. This requires exploring exactly the sort of pipeline needed to replicate both the form and function of the 3D models being fabricated. They also plan to investigate “visuo-haptic mismatches” to create novel experiences with materials that defy conventional expectations, like something that appears to be made of marble but feels like it’s made of wood.

Faruqi and Mueller co-authored the new paper alongside PhD students Maxine Perroni-Scharf and Yunyi Zhu, visiting undergraduate student Jaskaran Singh Walia, visiting masters student Shuyue Feng, and assistant professor Donald Degraen of the Human Interface Technology (HIT) Lab NZ in New Zealand.

What happens when a fashion legend taps into the transformative power of artificial intelligence? For more than five decades, fashion designer and entrepreneur Norma Kamali has pioneered bold industry shifts, creating iconic silhouettes worn by celebrities including Whitney Houston and Jessica Biel. Now, she is embracing a new frontier — one that merges creativity with algorithms and AI to redefine the future of her industry.

Through MIT Professional Education’s online “Applied Generative AI for Digital Transformation” course, which she completed in 2023, Kamali explored AI’s potential to serve as creative partner and ensure the longevity and evolution of her brand.

Kamali’s introduction to AI began with a meeting in Abu Dhabi, where industry experts, inspired by her Walmart collection, suggested developing an AI-driven fashion platform. Intrigued by the idea, but wary of the concept of “downloading her brain,” Kamali instead envisioned a system that could expand upon her 57-year archive — a closed-loop AI tool trained solely on her work. “I thought, AI could be my Karl Lagerfeld,” she says, referencing the designer’s reverence for archival inspiration.

To bring this vision to life, Kamali sought a deeper understanding of generative AI — so she headed to MIT Professional Education, an arm of MIT that has taught and inspired global professionals for more than 75 years. “I wasn’t sure how much I could actually do,” she recalls. “I had all these preconceived notions, but the more I learned, the more ideas I had.” Initially intimidated by the technical aspects of AI, she persevered, diving into prompts and training data, and exploring its creative potential. “I was determined,” she says. “And then suddenly, I was playing.”

Experimenting with her proprietary AI model, created by Maison Meta, Kamali used AI to reinterpret one of her signature styles — black garments adorned with silver studs. By prompting AI with iterations of her existing silhouettes, she witnessed unexpected and thrilling results. “It was magic,” she says. “Art, technology, and fashion colliding in ways I never imagined.” Even AI’s so-called “hallucinations” — distortions often seen as errors — became a source of inspiration. “Some of the best editorial fashion is absurd,” she notes. “AI-generated anomalies created entirely new forms of art.”

Kamali’s approach to AI reflects a broader shift across industries, where technology is not just a tool but a catalyst for reinvention. Bhaskar Pant, executive director of MIT Professional Education, underscores this transformation. “While everyone is speculating about the impact of AI, we are committed to advancing AI’s role in helping industries and leaders achieve breakthroughs, higher levels of productivity, and, as in this case, unleash creativity. Professionals must be empowered to harness AI’s potential in ways that not only enhance their work, but redefine what’s possible. Norma’s journey is a testament to the power of lifelong learning — demonstrating that innovation is ageless, fueled by curiosity and ambition.”

The experience also deepened Kamali’s perspective on AI’s role in the creative process. “AI doesn’t have a heartbeat,” she asserts. “It can’t replace human passion. But it can enhance creativity in ways we’re only beginning to understand.” Kamali also addressed industry fears about job displacement, arguing that the technology is already reshaping fashion’s labor landscape. “Sewing talent is harder to find. Designers need new tools to adapt.”

Beyond its creative applications, Kamali sees AI as a vehicle for sustainability. A longtime advocate for reducing dry cleaning — a practice linked to chemical exposure — she envisions AI streamlining fabric selection, minimizing waste, and enabling on-demand production. “Imagine a system where you design your wedding dress online, and a robot constructs it, one garment at a time,” she says. “The possibilities are endless.”

Abel Sanchez, MIT research scientist and lead instructor for MIT Professional Education’s Applied Generative AI for Digital Transformation course, emphasizes the transformative potential of AI across industries. “AI is a force reshaping the foundations of every sector, including fashion. Generative AI is unlocking unprecedented digital transformation opportunities, enabling organizations to rethink processes, design, and customer engagement. Norma is at the forefront of this shift, exploring how AI can propel the fashion industry forward, spark new creative frontiers, and redefine how designers interact with technology.”

Kamali’s experience in the course sparked an ongoing exchange of ideas with Sanchez, further fueling her curiosity. “AI is evolving so fast, I know I’ll need to go back,” she says. “MIT gave me the foundation, but this is just the beginning.” For those hesitant to embrace AI, she offers a striking analogy: “Imagine landing in a small town, in a foreign country, where you don’t speak the language, don’t recognize the food, and feel completely lost. That’s what it will be like if you don’t learn AI. The train has left the station — it’s time to get on board.”

With her AI-generated designs now featured on her website alongside her traditional collections, Kamali is proving that technology and creativity aren’t at odds — they’re collaborators. And as she continues to push the boundaries of both, she remains steadfast in her belief: “Learning is the adventure of life. Why stop now?”

Globally, and especially in low- and middle-income countries (LMICs), a significant portion of the population lacks access to essential health-care services. Although there are many contributing factors that create barriers to access, in many LMICs failing or obsolete equipment plays a significant role.

“Those of us who have investigated health-care systems in LMICs are familiar with so-called ‘equipment graveyards,’” says Nevan Hanumara SM ’06, PhD ’12, a research scientist in MIT’s Department of Mechanical Engineering, describing piles of broken, imported equipment, often bearing stickers indicating their origins from donor organizations.

“Looking at the root causes of medical equipment failing and falling out of service in LMICs, we find that the local biomedical engineers truly can’t do the maintenance, due to a cascade of challenges,” he says.

Among these challenges are: design weaknesses — systems designed for temperate, air-conditioned hospitals and stabilized power don’t fare well in areas with inconsistent power supply, dust, high heat and humidity, and continuous utilization; lack of supply chain — parts ordered in the U.S. can arrive in days, where parts ordered to East Africa may take months; and limited access to knowledgeable professionals — outside of major metropolitan areas, biomedical engineers are scarce.

Hanumara, Leroy Sibanda SM ’24, a recent graduate with a dual degree in management and electrical engineering and computer science (EECS), and Anthony Pennes ’16, a technical instructor in EECS, began to ponder what could be changed if local biomedical engineers were actually involved with the design of the equipment that they’re charged with maintaining.

Pennes, who staffs class 2.75/6.4861 (Medical Device Design), among other courses, developed hands-on biosensing and mechatronics exercises as class activities several years ago. Hanumara became interested in expanding that curriculum to produce something that could have a larger impact.

Working as a team, and with support from MIT International Science and Technology Initiatives (MISTI), the MIT Jameel World Education Lab, and the Priscilla King Gray Public Service Center, the trio created a hands-on course, exercises, and curriculum, supported by what they’ve now dubbed a “Biomed Lab in a Box” kit.

Sibanda, who hails from Bulawayo, Zimbabwe, brings additional lived experience to the project. He says friends up and down the continent speak about great practical primary and secondary education, and a tertiary education that provides a heavy emphasis on theory. The consequence, he says, is a plethora of graduates who are absolutely brilliant at the theory, but less experienced in advanced practical concepts.

“Anyone who has ever had to build systems that need to stand up to real-world conditions understands the chasm between knowing how to calculate the theoretically perfect ‘x’ and being capable of implementing a real-world solution with the materials available,” says Sibanda.

Hanumara and Sibanda traveled to Nairobi, Kenya, and Mbarara, Uganda, in late 2024 to test their kit and their theory, teaching three-day long biomedical innovation mini-courses at both Kenyatta University and Mbarara University of Science and Technology (MUST), with Pennes providing remote support from MIT’s campus.

With a curriculum based off of 2.75, labs were designed to connect the theoretical to the physical, increasing in complexity and confronting students with the real challenges of biomedical hardware and sensing, such as weak signals, ambient noise, motion artifacts, debugging, and precision assembly.

Pennes says the goal for the mini-courses was to shape the project around the real-world experiences of the region’s biomedical engineering students. “One of the problems that they experience in this region is not simply a lack of equipment, but the lack of ability to maintain it,” he says. “Some organization will come in and donate thousands of dollars of surgical lighting; then a power supply will burn out, and the organization will never come back to fix it.”

But that’s just the beginning of the problem, he adds. Engineers often find that the design isn’t open, and there’s no manual, making it impossible to find a circuit design for what’s inside the donated, proprietary system. “You have to poke and prod around the disassembled gear to see if you can discern the makers’ original goals in wiring it, and figure out a fix,” says Pennes.

In one example, he recalls seeing a donated screen for viewing X-rays — the lightbox kind, used to backlight film so that technicians can read the image — with a burned-out bulb. “The screen is lit by a proprietary bulb, so when it burned out, they could not replace it,” he recounts.

Local biomedical engineers ultimately realized that they could take a number of off-the-shelf fluorescent bulbs and angle them to fit inside the box. “Then they sort of MacGyver’d the wiring to make them all work. You get the medical technology to work however you can.”

It’s this hands-on, imaginative approach to problem-solving that the team hopes to promote — and it’s one that’s very familiar at MIT. “We’re not just ideas people, where we write a paper and we’re done with it — we want to see it applied,” says Hanumara. “It’s why so many startups come out of MIT.”

Course modules presented at Kenyatta and MUST included “Breadboarding an optical LED – photodetector pulse detector,” “Soldering a PCB and testing a 3-lead EKG,” and “Assembling and programming a syringe pump.” Each module is designed to be a self-contained learning experience, and the kit is accompanied by a USB flash drive with a 96-page lab manual written by Sibanda, and all the needed software, which is important to have when internet access is unreliable. The third exercise, relating to the syringe pump, is available via open access from the journal Biomedical Engineering Education.

“Our mission was to expose eager, young biomedical engineers to the hands-on, ‘mens-et-manus’ (‘mind-and-hand’) culture which is the cornerstone of MIT, and encourage them to develop their talents and aspirations as engineers and innovators,” says Hanumara. “We wanted to help empower them to participate in developing high-quality, contextually appropriate, technologies that improve health-care delivery in their own region.”

A LinkedIn post written by Hanumara shared reflections from students on their experiences with the material. “Every lab — from pulse oximetry and EKGs to syringe pump prototyping — brought classroom concepts to life, showing me the real-world applications of what we study,” wrote Muthoni Muriithi, a student at Kenyatta University. “Using breadboards, coding microcontrollers, soldering components, and analyzing biological data in real time helped me grasp how much careful design and precision go into creating reliable health-care tools.”

Feedback provided by students at both institutions is already helping to inform updates to the materials and future pilot programs.

Sibanda says another key thing the team is tracking what happens beyond the sessions, after the instructors leave. “It’s not just about offering the resource,” he says. “It’s important to understand what students find to be the most valuable, especially on their own.”

Hanumara concurs. “[Pennes] designed the core board that we’re using to be multifunctional. We didn’t touch any of the functions he built in — we want to see what the students will do with them. We also want to see what they can do with the mental framework,” he says, adding that this approach is important to empower students to explore, invent, and eventually scale up their own ideas.

Further, the project addresses another challenge the team identified early on: supply chain issues. In keeping with the mission of local capacity building, the entire kit was assembled in Nairobi by Gearbox Europlacer, which operates the only automated circuit board line in East Africa and is licensed to produce Raspberry Pi’s microcontrollers. “We did not tell the students anything,” says Hanumara, “but left it to them to notice that their circuit boards and microcontrollers said ‘Made in Kenya.’”

“The insistence on local manufacturing keeps us from falling into the trap that so much equipment donated into East Africa creates — you have one of these items, and if some part of it breaks you can never replace it,” says Pennes. “Having locally sourced items instead means that if you need another component, or devise an interesting side project, you have a shopping list and you can go get whatever you need.”

“Building off our ‘Biomed Lab in a Box’ experiment,” says Hanumara, “we aim to work with our colleagues in East Africa to further explore what can be designed and built with the eager, young talent and capabilities in the region.”

Hanumara’s LinkedIn post also thanked collaborating professors June Madete and Dean Johnes Obungoloch, from Kenyatta and MUST, respectively, and Latiff Cherono, managing director of Gearbox. The team hopes to eventually release the whole course in open-source format. 

Julie A. Lucas has decided to step down as MIT’s vice president for resource development, President Sally Kornbluth announced today. Lucas has set her last day as June 30, which coincides with the close of the Institute’s fiscal year, to ensure a smooth transition for staff and donors. 

Lucas has led fundraising at the Institute since 2014. During that time, MIT’s average annual fundraising has increased 96 percent to $611 million, up from $313 million in the decade before her arrival. MIT’s annual fundraising totals have exceeded the Institute’s annual $500 million fundraising target for nine straight fiscal years, including a few banner fiscal years with results upward of $700 to $900 million.

“Before I arrived at MIT, Julie built a fundraising operation worthy of the Institute’s world-class stature,” Kornbluth says. “I have seen firsthand how Julie’s expertise, collegial spirit, and commitment to our mission resonates with alumni and friends, motivating them to support the Institute.”

Lucas spearheaded the MIT Campaign for a Better World, which concluded in 2021 and raised $6.2 billion, setting a record as the Institute’s largest fundraising initiative. Emphasizing the Institute’s hands-on approach to solving the world’s toughest challenges — and centered on its strengths in education, research, and innovation — the campaign attracted participation from more than 112,000 alumni and friends around the globe, including nearly 56,000 new donors.  

“From the moment I met Julie Lucas, I knew she was the right person to serve as MIT’s chief philanthropic leader of our capital campaign,” says MIT President Emeritus L. Rafael Reif. “Julie is both a ‘maker’ and a ‘doer,’ well attuned to our ‘mens et manus’ motto. The Institute has benefited immensely from her impressive set of skills and ability to convey a coherent message that has inspired and motivated alumni and friends, foundations and corporations, to support MIT.” 

Under Lucas, MIT’s Office of Resource Development (RD) created new fundraising programs and processes, and introduced expanded ways of giving. For example, RD established the Institute’s planned giving program, which supports donors who want to make a lasting impact at MIT through philanthropic vehicles such as bequests, retirement plan distributions, life-income gifts, and gifts of complex assets. She also played a lead role in creating a donor-advised fund at MIT that, since its inception in 2017, has seen almost $120 million in contributions.  

“Julie is a remarkable fundraiser and leader — and when it comes to Julie’s leadership of Resource Development, the results speak for themselves,” says Mark Gorenberg ’76, chair of the MIT Corporation, who has participated in multiple MIT committees and campaigns over the last two decades. “These tangible fundraising outcomes have helped to facilitate innovations and discoveries, expand educational programs and facilities, support faculty and researchers, and ensure that an MIT education is affordable and accessible to the brightest minds from around the world.”

Prior to joining MIT, Lucas served in senior fundraising roles at the University of Southern California and Fordham Law School, as well as New York University and its business and law schools. 

While Lucas readies herself for the next phase in her career, she remains grateful for her time at the Institute. 

“Philanthropy is a powerful fuel for good in our world,” Lucas says. “My decision to step down was difficult. I feel honored and thankful that my work — and the work of the team of professionals I lead in Resource Development — has helped continue the amazing trajectory of MIT research and innovation that benefits all of us by solving humanity’s greatest challenges, both now and in the future.”

Lucas currently serves on the steering committee and is the immediate past chair of CASE 50, the Council for Advancement and Support of Education group that includes the top 50 fundraising institutions in the world. In addition, she is chair of the 2025 CASE Summit for Leaders in Advancement and a founding member of Aspen Leadership Group’s Chief Development Officer Network.

MIT astronomers have discovered a planet some 140 light-years from Earth that is rapidly crumbling to pieces.

The disintegrating world is about the mass of Mercury, although it circles about 20 times closer to its star than Mercury does to the sun, completing an orbit every 30.5 hours. At such close proximity to its star, the planet is likely covered in magma that is boiling off into space. As the roasting planet whizzes around its star, it is shedding an enormous amount of surface minerals and effectively evaporating away.

The astronomers spotted the planet using NASA’s Transiting Exoplanet Survey Satellite (TESS), an MIT-led mission that monitors the nearest stars for transits, or periodic dips in starlight that could be signs of orbiting exoplanets. The signal that tipped the astronomers off was a peculiar transit, with a dip that fluctuated in depth every orbit.

The scientists confirmed that the signal is of a tightly orbiting rocky planet that is trailing a long, comet-like tail of debris.

“The extent of the tail is gargantuan, stretching up to 9 million kilometers long, or roughly half of the planet’s entire orbit,” says Marc Hon, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research.

It appears that the planet is disintegrating at a dramatic rate, shedding an amount of material equivalent to one Mount Everest each time it orbits its star. At this pace, given its small mass, the researchers predict that the planet may completely disintegrate in about 1 million to 2 million years.

“We got lucky with catching it exactly when it’s really going away,” says Avi Shporer, a collaborator on the discovery who is also at the TESS Science Office. “It’s like on its last breath.”

Hon and Shporer, along with their colleagues, have published their results today in the Astrophysical Journal Letters. Their MIT co-authors include Saul Rappaport, Andrew Vanderburg, Jeroen Audenaert, William Fong, Jack Haviland, Katharine Hesse, Daniel Muthukrishna, Glen Petitpas, Ellie Schmelzer, Sara Seager, and George Ricker, along with collaborators from multiple other institutions.

Roasting away

The new planet, which scientists have tagged as BD+05 4868 Ab, was detected almost by happenstance.

“We weren’t looking for this kind of planet,” Hon says. “We were doing the typical planet vetting, and I happened to spot this signal that appeared very unusual.”

The typical signal of an orbiting exoplanet looks like a brief dip in a light curve, which repeats regularly, indicating that a compact body such as a planet is briefly passing in front of, and temporarily blocking, the light from its host star.

This typical pattern was unlike what Hon and his colleagues detected from the host star BD+05 4868 A, located in the constellation of Pegasus. Though a transit appeared every 30.5 hours, the brightness took much longer to return to normal, suggesting a long trailing structure still blocking starlight. Even more intriguing, the depth of the dip changed with each orbit, suggesting that whatever was passing in front of the star wasn’t always the same shape or blocking the same amount of light.

“The shape of the transit is typical of a comet with a long tail,” Hon explains. “Except that it’s unlikely that this tail contains volatile gases and ice as expected from a real comet — these would not survive long at such close proximity to the host star. Mineral grains evaporated from the planetary surface, however, can linger long enough to present such a distinctive tail.”

Given its proximity to its star, the team estimates that the planet is roasting at around 1,600 degrees Celsius, or close to 3,000 degrees Fahrenheit. As the star roasts the planet, any minerals on its surface are likely boiling away and escaping into space, where they cool into a long and dusty tail.

The dramatic demise of this planet is a consequence of its low mass, which is between that of Mercury and the moon. More massive terrestrial planets like the Earth have a stronger gravitational pull and therefore can hold onto their atmospheres. For BD+05 4868 Ab, the researchers suspect there is very little gravity to hold the planet together.

“This is a very tiny object, with very weak gravity, so it easily loses a lot of mass, which then further weakens its gravity, so it loses even more mass,” Shporer explains. “It’s a runaway process, and it’s only getting worse and worse for the planet.”

Mineral trail

Of the nearly 6,000 planets that astronomers have discovered to date, scientists know of only three other disintegrating planets beyond our solar system. Each of these crumbling worlds were spotted over 10 years ago using data from NASA’s Kepler Space Telescope. All three planets were spotted with similar comet-like tails. BD+05 4868 Ab has the longest tail and the deepest transits out of the four known disintegrating planets to date.

“That implies that its evaporation is the most catastrophic, and it will disappear much faster than the other planets,” Hon explains.

The planet’s host star is relatively close, and thus brighter than the stars hosting the other three disintegrating planets, making this system ideal for further observations using NASA’s James Webb Space Telescope (JWST), which can help determine the mineral makeup of the dust tail by identifying which colors of infrared light it absorbs.

This summer, Hon and graduate student Nicholas Tusay from Penn State University will lead observations of BD+05 4868 Ab using JWST. “This will be a unique opportunity to directly measure the interior composition of a rocky planet, which may tell us a lot about the diversity and potential habitability of terrestrial planets outside our solar system,” Hon says.

The researchers also will look through TESS data for signs of other disintegrating worlds.

“Sometimes with the food comes the appetite, and we are now trying to initiate the search for exactly these kinds of objects,” Shporer says. “These are weird objects, and the shape of the signal changes over time, which is something that’s difficult for us to find. But it’s something we’re actively working on.”

This work was supported, in part, by NASA.

In 2000, Patrick J. McGovern ’59 and Lore Harp McGovern made an extraordinary gift to establish the McGovern Institute for Brain Research at MIT, driven by their deep curiosity about the human mind and their belief in the power of science to change lives. Their $350 million pledge began with a simple yet audacious vision: to understand the human brain in all its complexity, and to leverage that understanding for the betterment of humanity.
 
Twenty-five years later, the McGovern Institute stands as a testament to the power of interdisciplinary collaboration, continuing to shape our understanding of the brain and improve the quality of life for people worldwide.

In the beginning

“This is, by any measure, a truly historic moment for MIT,” said MIT’s 15th president, Charles M. Vest, during his opening remarks at an event in 2000 to celebrate the McGovern gift agreement. “The creation of the McGovern Institute will launch one of the most profound and important scientific ventures of this century in what surely will be a cornerstone of MIT scientific contributions from the decades ahead.”
 
Vest tapped Phillip A. Sharp, MIT Institute professor emeritus of biology and Nobel laureate, to lead the institute, and appointed six MIT professors — Emilio Bizzi, Martha Constantine-Paton, Ann Graybiel PhD ’71, H. Robert Horvitz ’68, Nancy Kanwisher ’80, PhD ’86, and Tomaso Poggio — to represent its founding faculty.  Construction began in 2003 on Building 46, a 376,000 square foot research complex at the northeastern edge of campus. MIT’s new “gateway from the north” would eventually house the McGovern Institute, the Picower Institute for Learning and Memory, and MIT’s Department of Brain and Cognitive Sciences.

Robert Desimone, the Doris and Don Berkey Professor of Neuroscience at MIT, succeeded Sharp as director of the McGovern Institute in 2005, and assembled a distinguished roster of 22 faculty members, including a Nobel laureate, a Breakthrough Prize winner, two National Medal of Science/Technology awardees, and 15 members of the American Academy of Arts and Sciences.
 
A quarter century of innovation

On April 11, 2025, the McGovern Institute celebrated its 25th anniversary with a half-day symposium featuring presentations by MIT Institute Professor Robert Langer, alumni speakers from various McGovern labs, and Desimone, who is in his 20th year as director of the institute.

Desimone highlighted the institute’s recent discoveries, including the development of the CRISPR genome-editing system, which has culminated in the world’s first CRISPR gene therapy approved for humans — a remarkable achievement that is ushering in a new era of transformative medicine. In other milestones, McGovern researchers developed the first prosthetic limb fully controlled by the body’s nervous system; a flexible probe that taps into gut-brain communication; an expansion microscopy technique that paves the way for biology labs around the world to perform nanoscale imaging; and advanced computational models that demonstrate how we see, hear, use language, and even think about what others are thinking. Equally transformative has been the McGovern Institute’s work in neuroimaging, uncovering the architecture of human thought and establishing markers that signal the early emergence of mental illness, before symptoms even appear.

Synergy and open science
 
“I am often asked what makes us different from other neuroscience institutes and programs around the world,” says Desimone. “My answer is simple. At the McGovern Institute, the whole is greater than the sum of its parts.”
 
Many discoveries at the McGovern Institute have depended on collaborations across multiple labs, ranging from biological engineering to human brain imaging and artificial intelligence. In modern brain research, significant advances often require the joint expertise of people working in neurophysiology, behavior, computational analysis, neuroanatomy, and molecular biology. More than a dozen different MIT departments are represented by McGovern faculty and graduate students, and this synergy has led to insights and innovations that are far greater than what any single discipline could achieve alone.
 
Also baked into the McGovern ethos is a spirit of open science, where newly developed technologies are shared with colleagues around the world. Through hospital partnerships for example, McGovern researchers are testing their tools and therapeutic interventions in clinical settings, accelerating their discoveries into real-world solutions.

The McGovern legacy  

Hundreds of scientific papers have emerged from McGovern labs over the past 25 years, but most faculty would argue that it’s the people — the young researchers — that truly define the McGovern Institute. Award-winning faculty often attract the brightest young minds, but many McGovern faculty also serve as mentors, creating a diverse and vibrant scientific community that is setting the global standard for brain research and its applications. Kanwisher, for example, has guided more than 70 doctoral students and postdocs who have gone on to become leading scientists around the world. Three of her former students, Evelina Fedorenko PhD ’07, Josh McDermott PhD ’06, and Rebecca Saxe PhD ’03, the John W. Jarve (1978) Professor of Brain and Cognitive Sciences, are now her colleagues at the McGovern Institute. Other McGovern alumni shared stories of mentorship, science, and real-world impact at the 25th anniversary symposium.

Looking to the future, the McGovern community is more committed than ever to unraveling the mysteries of the brain and making a meaningful difference in lives of individuals at a global scale.
 
“By promoting team science, open communication, and cross-discipline partnerships,” says institute co-founder Lore Harp McGovern, “our culture demonstrates how individual expertise can be amplified through collective effort. I am honored to be the co-founder of this incredible institution — onward to the next 25 years!”

One of the most exciting developments in cancer treatment is a wave of new cell therapies that train a patient’s immune system to attack cancer cells. Such therapies have saved the lives of patients with certain aggressive cancers and few other options. Most of these therapies work by teaching immune cells to recognize and attack specific proteins on the surface of cancer cells.

Unfortunately, most proteins found on cancer cells aren’t unique to tumors. They’re also often present on healthy cells, making it difficult to target cancer aggressively without triggering dangerous attacks on other tissue. The problem has limited the application of cell therapies to a small subset of cancers.

Now Senti Bio is working to create smarter cell therapies using synthetic biology. The company, which was founded by former MIT faculty member and current MIT Research Associate Tim Lu ’03, MEng ’03, PhD ’08 and Professor James Collins, is equipping cells with gene circuits that allow the cells to sense and respond to their environments.

Lu, who studied computer science as an undergraduate at MIT, describes Senti’s approach as programming living cells to behave more like computers — responding to specific biological cues with “if/then” logic, just like computer code.

“We have innovated a cell therapy that says, ‘Kill anything displaying the cancer target, but spare anything that has this healthy target,’” Lu explains. “Despite the promise of certain cancer targets, problems can arise when they are expressed on healthy cells that we want to protect. Our logic gating technology was designed to recognize and avoid killing those healthy cells, which introduces a whole spectrum of additional cancers that don’t have a single clean target that we can now potentially address. That’s the power of embedding these cells with logic.”

The company’s lead drug candidate aims to help patients with acute myeloid leukemia (AML) who have experienced a relapse or are unresponsive to other therapies. The prognosis for such patients is poor, but early data from the company’s first clinical trial showed that two of the first three patients Senti treated experienced complete remission, where subsequent bone marrow tests couldn’t detect a single cancer cell.

“It’s essentially one of the best responses you can get in this disease, so we were really excited to see that,” says Lu, who served on MIT’s faculty until leaving to lead Senti in 2022.

Senti is expecting to release more patient data at the upcoming American Association for Cancer Research (AACR) meeting at the end of April.

“Our groundbreaking work at Senti is showing that one can harness synthetic biology technologies to create programmable, smart medicines for treating patients with cancer,” says Collins, who is currently MIT’s Termeer Professor of Medical Engineering and Science. “This is tremendously exciting and demonstrates how one can utilize synthetic biological circuits, in this case logic gates, to design highly effective, next-generation living therapeutics.”

From computer science to cancer care

Lu was inspired as an undergraduate studying electrical engineering and computer science by the Human Genome Project, an international race to sequence the human genome. Later, he entered the Harvard-MIT Health Sciences and Technology (HST) program, through which he earned a PhD from MIT in electrical and biomedical imaging and an MD from Harvard. During that time, he worked in the lab of his eventual Senti co-founder James Collins, a synthetic biology pioneer.

In 2010, Lu joined MIT as an assistant professor with a joint appointment in the departments of Biological Engineering and of Electrical Engineering and Computer Science. Over the course of the next 14 years, Lu led the Synthetic Biology Group at MIT and started several biotech companies, including Engine Biosciences and Tango Therapeutics, which are also developing precision cancer treatments.

In 2015, a group of researchers including Lu and MIT Institute Professor Phillip Sharp published research showing they could use gene circuits to get immune cells to selectively respond to tumor cells in their environment.

“One of the first things we published focused on the idea of logic gates in living cells,” Lu says. “A computer has ‘and’ gates, ‘or’ gates, and ‘not’ gates that allow it to perform computations, and we started publishing gene circuits that implement logic into living cells. These allow cells to detect signals and then make logical decisions like, ‘Should we switch on or off?’”

Around that time, the first cell therapies and cancer immunotherapies began to be approved by the Food and Drug Administration, and the founders saw their technology as a way to take those approaches to the next level. They officially founded Senti Bio in 2016, with Lu taking a sabbatical from MIT to serve as CEO.

The company licensed technology from MIT and subsequently advanced the cellular logic gates so they could work with multiple types of engineered immune cells, including T cells and “natural killer” cells. Senti’s cells can respond to specific proteins that exist on the surface of both cancer and healthy cells to increase selectivity.

“We can now create a cell therapy where the cell makes a decision as to whether to kill a cancer cell or spare a healthy cell even when those cells are right next to each other,” Lu says. “If you can’t distinguish between cancerous and healthy cells, you get unwanted side effects, or you may not be able to hit the cancer as hard as you’d like. But once you can do that, there’s a lot of ways to maximize your firepower against the cancer cells.”

Hope for patients

Senti’s lead clinical trial is focusing on patients with relapsed or refractory blood cancers, including AML.

“Obviously the most important thing is getting a good response for patients,” Lu says. “But we’re also doing additional scientific work to confirm that the logic gates are working the way we expect them to in humans. Based on that information, we can then deploy logic gates into additional therapeutic indications such as solid tumors, where you have a lot of the same problems with finding a target.”

Another company that has partnered with Senti to use some of Senti’s technology also has an early clinical trial underway in liver cancer. Senti is also partnering with other companies to apply its gene circuit technology in areas like regenerative medicine and neuroscience.

“I think this is broader than just cell therapies,” Lu says. “We believe if we can prove this out in AML, it will lead to a fundamentally new way of diagnosing and treating cancer, where we’re able to definitively identify and target cancer cells and spare healthy cells. We hope it will become a whole new class of medicines moving forward.”

Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash.

Some methods exist for ensuring LLMs conform to the rules of whatever language they are generating text in, but many of these methods either distort the model’s intended meaning or are too time-consuming to be feasible for complex tasks.

A new approach developed by researchers at MIT and elsewhere automatically guides an LLM to generate text that adheres to the rules of the relevant language, such as a particular programming language, and is also error-free. Their method allows an LLM to allocate efforts toward outputs that are most likely to be valid and accurate, while discarding unpromising outputs early in the process. This probabilistic approach boosts computational efficiency.

Due to these efficiency gains, the researchers’ architecture enabled small LLMs to outperform much larger models in generating accurate, properly structured outputs for several real-world use cases, including molecular biology and robotics.

In the long run, this new architecture could help nonexperts control AI-generated content. For instance, it could allow businesspeople to write complex queries in SQL, a language for database manipulation, using only natural language prompts.

“This work has implications beyond research. It could improve programming assistants, AI-powered data analysis, and scientific discovery tools by ensuring that AI-generated outputs remain both useful and correct,” says João Loula, an MIT graduate student and co-lead author of a paper on this framework.

Loula is joined on the paper by co-lead authors Benjamin LeBrun, a research assistant at the Mila-Quebec Artificial Intelligence Institute, and Li Du, a graduate student at John Hopkins University; co-senior authors Vikash Mansinghka ’05, MEng ’09, PhD ’09, a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences; Alexander K. Lew SM ’20, an assistant professor at Yale University; Tim Vieira, a postdoc at ETH Zurich; and Timothy J. O’Donnell, an associate professor at McGill University and a Canada CIFAR AI Chair at Mila, who led the international team; as well as several others. The research will be presented at the International Conference on Learning Representations.

Enforcing structure and meaning

One common approach for controlling the structured text generated by LLMs involves checking an entire output, like a block of computer code, to make sure it is valid and will run error-free. If not, the user must start again, racking up computational resources.

On the other hand, a programmer could stop to check the output along the way. While this can ensure the code adheres to the programming language and is structurally valid, incrementally correcting the code may cause it to drift from the meaning the user intended, hurting its accuracy in the long run.

“It is much easier to enforce structure than meaning. We can quickly check whether something is in the right programming language, but to check its meaning you have to execute the code. Our work is also about dealing with these different types of information,” Loula says.

The researchers’ approach involves engineering knowledge into the LLM to steer it toward the most promising outputs. These outputs are more likely to follow the structural constraints defined by a user, and to have the meaning the user intends.

“We are not trying to train an LLM to do this. Instead, we are engineering some knowledge that an expert would have and combining it with the LLM’s knowledge, which offers a very different approach to scaling than you see in deep learning,” Mansinghka adds.

They accomplish this using a technique called sequential Monte Carlo, which enables parallel generation from an LLM to compete with each other. The model dynamically allocates resources to different threads of parallel computation based on how promising their output appears.

Each output is given a weight that represents how likely it is to be structurally valid and semantically accurate. At each step in the computation, the model focuses on those with higher weights and throws out the rest.

In a sense, it is like the LLM has an expert looking over its shoulder to ensure it makes the right choices at each step, while keeping it focused on the overall goal. The user specifies their desired structure and meaning, as well as how to check the output, then the researchers’ architecture guides the LLM to do the rest.

“We’ve worked out the hard math so that, for any kinds of constraints you’d like to incorporate, you are going to get the proper weights. In the end, you get the right answer,” Loula says.

Boosting small models

To test their approach, they applied the framework to LLMs tasked with generating four types of outputs: Python code, SQL database queries, molecular structures, and plans for a robot to follow.

When compared to existing approaches, the researchers’ method performed more accurately while requiring less computation.

In Python code generation, for instance, the researchers’ architecture enabled a small, open-source model to outperform a specialized, commercial closed-source model that is more than double its size.

“We are very excited that we can allow these small models to punch way above their weight,” Loula says.

Moving forward, the researchers want to use their technique to control larger chunks of generated text, rather than working one small piece at a time. They also want to combine their method with learning, so that as they control the outputs a model generates, it learns to be more accurate.

In the long run, this project could have broader applications for non-technical users. For instance, it could be combined with systems for automated data modeling, and querying generative models of databases.

The approach could also enable machine-assisted data analysis systems, where the user can converse with software that accurately models the meaning of the data and the questions asked by the user, adds Mansinghka.

“One of the fundamental questions of linguistics is how the meaning of words, phrases, and sentences can be grounded in models of the world, accounting for uncertainty and vagueness in meaning and reference. LLMs, predicting likely token sequences, don’t address this problem. Our paper shows that, in narrow symbolic domains, it is technically possible to map from words to distributions on grounded meanings. It’s a small step towards deeper questions in cognitive science, linguistics, and artificial intelligence needed to understand how machines can communicate about the world like we do,” says O’Donnell.

This research is funded, in part, by the Canada CIFAR AI Chairs Program, the MIT Quest for Intelligence, and Convergent Research. 

The following is part of a series of short interviews from the Department of Electrical Engineering and Computer Science (EECS). Each spotlight features a student answering questions about themselves and life at MIT. Today’s interviewee, YongYan (Crystal) Liang, is a senior majoring in EECS with a particular interest in bioengineering and medical devices — which led her to join the Living Machines track as part of New Engineering Education Transformation (NEET) at MIT. An Advanced Undergraduate Research Opportunities Program (SuperUROP) scholar, Liang was supported by the Nadar Foundation Undergraduate Research and Innovation Scholar award for her project, which focused on steering systems for intravascular drug delivery devices. A world traveler, Liang has also taught robotics to students in MISTI Global Teaching Labs (GTL) programs in Korea and Germany — and is involved with the Terrascope and MedLinks communities. 

Q: Do you have a bucket list? If so, share one or two of the items on it.

A: I’d like to be proficient in at least five languages in a conversational sense (though probably not at a working proficiency level). Currently, I’m fluent in English, and can speak Cantonese and Mandarin. I also have a 1,600-plus day Duolingo streak where I’m trying to learn the foundations of a few languages, including German, Korean, Japanese, and Russian. 

Another bucket list item I have is to try every martial art/combat sport there is, even if it’s just an introduction class. So far, I’ve practiced taekwondo for a few years, taken a few lessons in boxing/kickboxing, and dabbled in beginners’ classes for karate, Krav Maga, and Brazilian jiujitsu. I’ll probably try to take judo, aikido, and other classes this upcoming year! It would also be pretty epic to be a fourth dan black belt one day, though that may take a decade or two.

Q: If you had to teach a really in-depth class about one niche topic, what would you pick?

A: Personally, I think artificial organs are pretty awesome! I would probably talk about the fusion of engineering with our bodies, and organ enhancement. This might include adding functionalities and possible organ regeneration, so that those waiting for organ donations can be helped without being morally conflicted by waiting for another person’s downfall. I’ve previously done research in several BioEECS-related labs that I’d love to talk about as well. This includes the Traverso Lab at Pappalardo, briefly in the Edelman Lab at the [Institute for Medical Engineering and Science], the Langer Lab at the Koch Institute of Integrative Cancer Research, as well as in the MIT Media Lab with the Conformable Decoders and BioMechatronics group. I also contributed to a recently published paper related to gastrointestinal devices: OSIRIS.  

Q: If you suddenly won the lottery, what would you spend some of the money on? 

A: I would make sure my mom got most of the money. The first thing we’d do is probably go house shopping around the world and buy properties in great travel destinations — then go around and live in said properties. We would do this on rotation with our friends until we ran out of money, then put the properties up for rent and use the money to open a restaurant with my mom’s recipes as the menu. Then I’d get to eat her food forever.

Q: What do you believe is an underrated invention or technology?

A: I feel like many people wear glasses or put on contacts nowadays and don’t really think twice about it, glossing over how cool it is that we can fix bad sight and how critical sight is for our survival. If a zombie apocalypse happened and my glasses broke, it would be over for me. And don’t get me started about the invention of the indoor toilet and plumbing systems!

Q: Are you a re-reader or a re-watcher? If so, what are your comfort books, shows, or movies? 

A: I’m both a re-reader and a re-watcher! I have a lot of fun binging webtoons and dramas. I’m also a huge Marvel fan, although recently, it’s been a hit or miss. Action and romcoms are my kinda vibes, and occasionally I do watch some anime. If I’m bored I usually re-watch some [Marvel Cinematic Universe] movies, or Fairy Tail, or read some Isekai genre stories. 

Q: It’s time to get on the shuttle to the first Mars colony, and you can only bring one personal item. What are you going to bring along with you?

A: My first thought was my phone, but I feel like that may be too standard of an answer. If we were talking about the fantasy realm, I might ask Stephen Strange to borrow his sling ring to open more portals to link the Earth and Mars. As to why he wouldn’t have just come with us in the first place, I don’t know; maybe he’s too busy fighting aliens, or something?

Q: What are you looking forward to about life after graduation? What do you think you’ll miss about MIT? 

A: I won’t be missing dining hall food very much, that’s for sure — except for the amazing oatmeal from one of the Maseeh dining hall chefs, Sum! I am, however, excited to live the nine-to-five life for a few years and have my weekends back. I’ll miss my friends dearly, since everyone will be so spread out across the States and abroad. I’ll miss the nights we spent watching movies, playing games, cooking, eating, and yapping away. I’m excited to see everyone grow and take another step closer to their dreams. It will be fun visiting them and being able to explore the world at the same time! For more immediate plans, I’ll be going back to Apple this summer to intern again, and will finish my MEng with the 6A program at Cadence. Afterwards, I shall see where life takes me!

It is clear that humankind needs increasingly more resources, from computing power to steel and concrete, to meet the growing demands associated with data centers, infrastructure, and other mainstays of society. New, cost-effective approaches for producing the advanced materials key to that growth were the focus of a two-day workshop at MIT on March 11 and 12.

A theme throughout the event was the importance of collaboration between and within universities and industries. The goal is to “develop concepts that everybody can use together, instead of everybody doing something different and then trying to sort it out later at great cost,” said Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering at MIT.

The workshop was produced by MIT’s Materials Research Laboratory (MRL), which has an industry collegium, and MIT’s Industrial Liaison Program. 

The program included an address by Javier Sanfelix, lead of the Advanced Materials Team for the European Union. Sanfelix gave an overview of the EU’s strategy to developing advanced materials, which he said are “key enablers of the green and digital transition for European industry.”

That strategy has already led to several initiatives. These include a material commons, or shared digital infrastructure for the design and development of advanced materials, and an advanced materials academy for educating new innovators and designers. Sanfelix also described an Advanced Materials Act for 2026 that aims to put in place a legislative framework that supports the entire innovation cycle.

Sanfelix was visiting MIT to learn more about how the Institute is approaching the future of advanced materials. “We see MIT as a leader worldwide in technology, especially on materials, and there is a lot to learn about [your] industry collaborations and technology transfer with industry,” he said.

Innovations in steel and concrete

The workshop began with talks about innovations involving two of the most common human-made materials in the world: steel and cement. We’ll need more of both but must reckon with the huge amounts of energy required to produce them and their impact on the environment due to greenhouse-gas emissions during that production.

One way to address our need for more steel is to reuse what we have, said C. Cem Tasan, the POSCO Associate Professor of Metallurgy in the Department of Materials Science and Engineering (DMSE) and director of the Materials Research Laboratory.

But most of the existing approaches to recycling scrap steel involve melting the metal. “And whenever you are dealing with molten metal, everything goes up, from energy use to carbon-dioxide emissions. Life is more difficult,” Tasan said.

The question he and his team asked is whether they could reuse scrap steel without melting it. Could they consolidate solid scraps, then roll them together using existing equipment to create new sheet metal? From the materials-science perspective, Tasan said, that shouldn’t work, for several reasons.

But it does. “We’ve demonstrated the potential in two papers and two patent applications already,” he said. Tasan noted that the approach focuses on high-quality manufacturing scrap. “This is not junkyard scrap,” he said.

Tasan went on to explain how and why the new process works from a materials-science perspective, then gave examples of how the recycled steel could be used. “My favorite example is the stainless-steel countertops in restaurants. Do you really need the mechanical performance of stainless steel there?” You could use the recycled steel instead.

Hessam Azarijafari addressed another common, indispensable material: concrete. This year marks the 16th anniversary of the MIT Concrete Sustainability Hub (CSHub), which began when a set of industry leaders and politicians reached out to MIT to learn more about the benefits and environmental impacts of concrete.

The hub’s work now centers around three main themes: working toward a carbon-neutral concrete industry; the development of a sustainable infrastructure, with a focus on pavement; and how to make our cities more resilient to natural hazards through investment in stronger, cooler construction.

Azarijafari, the deputy director of the CSHub, went on to give several examples of research results that have come out of the CSHub. These include many models to identify different pathways to decarbonize the cement and concrete sector. Other work involves pavements, which the general public thinks of as inert, Azarijafari said. “But we have [created] a state-of-the-art model that can assess interactions between pavement and vehicles.” It turns out that pavement surface characteristics and structural performance “can influence excess fuel consumption by inducing an additional rolling resistance.”

Azarijafari emphasized  the importance of working closely with policymakers and industry. That engagement is key “to sharing the lessons that we have learned so far.”

Toward a resource-efficient microchip industry

Consider the following: In 2020 the number of cell phones, GPS units, and other devices connected to the “cloud,” or large data centers, exceeded 50 billion. And data-center traffic in turn is scaling by 1,000 times every 10 years.

But all of that computation takes energy. And “all of it has to happen at a constant cost of energy, because the gross domestic product isn’t changing at that rate,” said Kimerling. The solution is to either produce much more energy, or make information technology much more energy-efficient. Several speakers at the workshop focused on the materials and components behind the latter.

Key to everything they discussed: adding photonics, or using light to carry information, to the well-established electronics behind today’s microchips. “The bottom line is that integrating photonics with electronics in the same package is the transistor for the 21st century. If we can’t figure out how to do that, then we’re not going to be able to scale forward,” said Kimerling, who is director of the MIT Microphotonics Center.

MIT has long been a leader in the integration of photonics with electronics. For example, Kimerling described the Integrated Photonics System Roadmap – International (IPSR-I), a global network of more than 400 industrial and R&D partners working together to define and create photonic integrated circuit technology. IPSR-I is led by the MIT Microphotonics Center and PhotonDelta. Kimerling began the organization in 1997.

Last year IPSR-I released its latest roadmap for photonics-electronics integration, “which  outlines a clear way forward and specifies an innovative learning curve for scaling performance and applications for the next 15 years,” Kimerling said.

Another major MIT program focused on the future of the microchip industry is FUTUR-IC, a new global alliance for sustainable microchip manufacturing. Begun last year, FUTUR-IC is funded by the National Science Foundation.

“Our goal is to build a resource-efficient microchip industry value chain,” said Anuradha Murthy Agarwal, a principal research scientist at the MRL and leader of FUTUR-IC. That includes all of the elements that go into manufacturing future microchips, including workforce education and techniques to mitigate potential environmental effects.

FUTUR-IC is also focused on electronic-photonic integration. “My mantra is to use electronics for computation, [and] shift to photonics for communication to bring this energy crisis in control,” Agarwal said.

But integrating electronic chips with photonic chips is not easy. To that end, Agarwal described some of the challenges involved. For example, currently it is difficult to connect the optical fibers carrying communications to a microchip. That’s because the alignment between the two must be almost perfect or the light will disperse. And the dimensions involved are minuscule. An optical fiber has a diameter of only millionths of a meter. As a result, today each connection must be actively tested with a laser to ensure that the light will come through.

That said, Agarwal went on to describe a new coupler between the fiber and chip that could solve the problem and allow robots to passively assemble the chips (no laser needed). The work, which was conducted by researchers including MIT graduate student Drew Wenninger, Agarwal, and Kimerling, has been patented, and is reported in two papers. A second recent breakthrough in this area involving a printed micro-reflector was described by Juejun “JJ” Hu, John F. Elliott Professor of Materials Science and Engineering.

FUTUR-IC is also leading educational efforts for training a future workforce, as well as techniques for detecting — and potentially destroying — the perfluroalkyls (PFAS, or “forever chemicals”) released during microchip manufacturing. FUTUR-IC educational efforts, including virtual reality and game-based learning, were described by Sajan Saini, education director for FUTUR-IC. PFAS detection and remediation were discussed by Aristide Gumyusenge, an assistant professor in DMSE, and Jesus Castro Esteban, a postdoc in the Department of Chemistry.

Other presenters at the workshop included Antoine Allanore, the Heather N. Lechtman Professor of Materials Science and Engineering; Katrin Daehn, a postdoc in the Allanore lab; Xuanhe Zhao, the Uncas (1923) and Helen Whitaker Professor in the Department of Mechanical Engineering; Richard Otte, CEO of Promex; and Carl Thompson, the Stavros V. Salapatas Professor in Materials Science and Engineering.

As technology rapidly propels society forward, MIT is rethinking how it prepares students to face the world and its greatest challenges. Generations of educators have shared knowledge at MIT by connecting lessons to practical applications, but what does the Institute’s motto “mens et manus” (“mind and hand”), referring to hands-on learning, look like in the future?

This was the guiding question of the annual Festival of Learning, co-hosted by MIT Open Learning and the Office of the Vice Chancellor. MIT faculty, instructors, students, and staff engaged in meaningful discussions about teaching and learning as the Institute critically revisits its undergraduate academic program.

“Because the world is changing, we owe it to our students to reflect these realities in our academic experiences,” said Daniel E. Hastings, Cecil and Ida Green Education Professor of Aeronautics and Astronautics and then-interim vice chancellor. “It’s in our DNA to try new things at MIT.”

Fostering a greater sense of purpose

MIT emphasizes hands-on learning much like many engineering schools. What deeply concerned panelists like Susan Silbey, the Leon and Anne Goldberg Professor of Humanities, Sociology, and Anthropology, is that students are not engaging in enough intellectual thinking via significant reading, textual interpretation, or involvement with uncertain questions.

Christopher Capozzola, senior associate dean for open learning, echoed this, saying, “We have designed a world in which [students] feel enormous pressure to maximize their career outcomes at the end” of their undergraduate education.

Students move in systems of explicit incentives, he said, such as grades and the General Institute Requirements, but also respond to unwritten incentives, like extracurriculars, internships, and prestige. “That’s our fault, not theirs,” Capozzola said, and identified this as an opportunity to improve the MIT curriculum.

How can educators encourage students to connect more with course material, instead of treating it as a means to an end? Adam Martin, professor of biology, always asks his students to challenge the status quo by incorporating test questions with data arguing against the models from the textbook.

“I want them to think,” Martin said. “I want them to challenge what we think is the frontier of the field.”

Considering context

One of the most significant topics of discussion was the importance of context in education. For example, class 7.102 (Introduction to Molecular Biology Techniques) uses story-based problem-solving to show students how the curriculum fits into real-world contexts.

The fictional premise driving 7.102 is that a child fell into the Charles River and caught an antibiotic-resistant bacterial infection. To save the child, students must characterize the bacteria and identify phages that could kill it.

“It really shows the students not only basic techniques, but what it’s like to be in a team and in a discovery situation,” said Martin.

This hands-on approach — collecting water, isolating the phages within, and comparing to more reliable sources — unlocks students’ imaginations, Martin said. In an environment intentionally designed to give students room to fail, the narrative incentivizes students to persist with repeated experimentation.

But Silbey, who is also a professor of behavioral and policy sciences at MIT Sloan School of Management, has noticed the reluctance of students to engage with nontechnical contexts. Students, she concluded, “have minimal understanding of how the action of any individual becomes part of something larger, durable, consequential through invisible but powerful mechanisms of aggregation.”

Educators agreed that contextual understanding was equally important to a STEM curriculum as technical instruction. “Teaching and thinking at that interface between technology and society is really crucial for making technologists feel responsible for the things that they create and the things that they use,” added Capozzola.

Amitava Mitra, founding executive director of MIT New Engineering Education Transformation (NEET), highlighted an example where students developed an effective technical solution to decarbonize homes in Ulaanbaatar, Mongolia. Or so they thought.

“Once we saw what was on the ground and understood the context — the social model, the social processes — we realized we had no clue,” the students told Mitra.

One way MIT is trying to bridge these gaps is through the Social and Ethical Responsibilities of Computing program. This curriculum integrates ethical considerations alongside computing courses to help students envision the social and moral consequences of their actions.

In one technical machinery lecture, Silbey’s students had trouble envisioning the negative impacts of autonomous vehicles. But after she shared the history of the regulation of dangerous products, she said many students became more open to examining potential ripple effects.

Creating interdisciplinary opportunities

The panelists viewed interdisciplinary education as critical preparation for the complexities of the real world.

“Whether it’s tackling climate change, creating sustainable infrastructure, creating cutting-edge technologies in life sciences or robotics, we need our engineers, social scientists, and scientists to work in teams cutting across disciplines to create solutions today,” said Mitra.

To expand opportunities for undergraduates to collaborate across academic departments and other campus units, NEET was launched in 2017. NEET is a project-based experiential learning curriculum that requires technical and social expertise. One student group, for example, is designing, building, and installing a solar-powered charging station at MIT Open Space. To introduce a project like this into MIT’s infrastructure, students must coordinate with a variety of Institute offices — such as Campus Planning, Engineering & Energy Management, and Insurance — and city groups, like the Cambridge Fire Department.

“It's an eye-opener for them,” said Mitra.

Capozzola noted how “para-curricular” activities like NEET, MIT Undergraduate Research Opportunities Program, MISTI, D-Lab, and others prove that effective hands-on education doesn’t have to be a formal credit-bearing program.

“Students put in enormous amounts of time and effort for things that shape them, that speak to their passion and this deep engagement,” Capozzola said. “This is a special area where I think MIT particularly excels.”

Moving forward together

In a panel featuring both MIT instructors and students, educators recognized that designing an effective curriculum requires balancing content across subjects or core topics while organizing materials on Canvas — MIT’s learning management system — in a way that’s intuitive for students. Instructors collaborated directly with students and staff via MIT’s Canvas Innovation Fund to make these improvements.

“There are things that the novice students see in what I’m teaching that I don’t see,” said Sean Robinson, lecturer in physics and associate director of the Helena Foundation Junior Laboratory. “Our class is aimed at taking people who think of themselves as physics students and getting them to think of themselves as physicists. I want junior colleagues.”

The biggest takeaway from student panelists was the importance of minimizing logistical struggles by structuring Canvas to guide students toward learning objectives. Cory Romanov ’24, technical instructor of physics, and McKenzie Dinesen, a senior in aerospace engineering and Russian and Eurasian studies, emphasized that explaining learning goals and organizing course content with clear deadlines were simple improvements that went a long way to enhance the student experience.

Emphasizing the benefit of feedback like this, Capozzola said, “It’s important to give people at MIT — students, staff, and others who are often closed out of conversations — a more democratic voice so that we can be a model for the university that we want to be in 25 years.”

As MIT continues to enhance its educational approach, the insights from the Festival of Learning highlight a crucial evolution in how students engage with knowledge. From rethinking course structures to integrating interdisciplinary and experiential learning, the panelists underscored the need for a curriculum that balances technical expertise with a deep understanding of social and ethical contexts.

“It’s important to equip students on the ‘mens’ side with the kinds of civic knowledge that they need to go out into the world,” said Capozzola, “but also the ‘manus,’ to be able to do the everyday work of getting your hands dirty and building democratic institutions.” 

MIT political scientist Adam Berinsky has been named to the 2025 class of Andrew Carnegie Fellows, a high-profile honor for scholars pursuing research in the social sciences and humanities.

The fellowship is provided by The Carnegie Corp. of New York. Berinsky, the Mitsui Professor of Political Science, and 25 other fellows were selected from more than 300 applicants. They will each receive stipends of $200,000 for research that seeks to understand how and why our society has become so polarized, and how we can strengthen the forces of cohesion to fortify our democracy.

“Through these fellowships Carnegie is harnessing the unrivaled brainpower of our universities to help us to understand how our society has become so polarized,” says Carnegie President Louise Richardson. “Our future grant-making will be informed by what we learn from these scholars as we seek to mitigate the pernicious effects of political polarization.”

Berinsky said he is “incredibly honored to be named an Andrew Carnegie Fellow for the coming year. This fellowship will allow me to work on critical issues in the current political moment.”

During his year as a Carnegie Fellow, Berinsky will be working on a project, “Fostering an Accurate Information Ecosystem to Mitigate Polarization in the United States.”

“For a functioning democracy, it is essential that citizens share a baseline of common facts,” says Berinsky. “However, in today’s politically polarized climate, ‘alternative facts,’ and other forms of misinformation — from political rumors to conspiracy theories — distort how people see reality, and damage our social fabric.”

“I’ve spent the last 15 years investigating why individuals accept misinformation and how to counter misperceptions. But there is still a lot of work to be done. My project aims to tackle the serious problem of misinformation in the United States by bringing together existing approaches in new, more powerful combinations. I’m hoping that the whole can be more than the sum of its parts.”

Berinsky has been a member of the MIT faculty since 2003. He is the author of “Political Rumors: Why We Accept Misinformation and How to Fight It” (Princeton University Press, 2023).

Other MIT faculty who have received the Carnegie Fellowship in recent years include economists David Autor and Daron Acemoglu and political scientists Fotini Christia, Taylor Fravel, Richard Nielsen, and Charles Stewart.

Cleft lip and cleft palate are among the most common birth defects, occurring in about one in 1,050 births in the United States. These defects, which appear when the tissues that form the lip or the roof of the mouth do not join completely, are believed to be caused by a mix of genetic and environmental factors.

In a new study, MIT biologists have discovered how a genetic variant often found in people with these facial malformations leads to the development of cleft lip and cleft palate.

Their findings suggest that the variant diminishes cells’ supply of transfer RNA, a molecule that is critical for assembling proteins. When this happens, embryonic face cells are unable to fuse to form the lip and roof of the mouth.

“Until now, no one had made the connection that we made. This particular gene was known to be part of the complex involved in the splicing of transfer RNA, but it wasn’t clear that it played such a crucial role for this process and for facial development. Without the gene, known as DDX1, certain transfer RNA can no longer bring amino acids to the ribosome to make new proteins. If the cells can’t process these tRNAs properly, then the ribosomes can’t make protein anymore,” says Michaela Bartusel, an MIT research scientist and the lead author of the study.

Eliezer Calo, an associate professor of biology at MIT, is the senior author of the paper, which appears today in the American Journal of Human Genetics.

Genetic variants

Cleft lip and cleft palate, also known as orofacial clefts, can be caused by genetic mutations, but in many cases, there is no known genetic cause.

“The mechanism for the development of these orofacial clefts is unclear, mostly because they are known to be impacted by both genetic and environmental factors,” Calo says. “Trying to pinpoint what might be affected has been very challenging in this context.”

To discover genetic factors that influence a particular disease, scientists often perform genome-wide association studies (GWAS), which can reveal variants that are found more often in people who have a particular disease than in people who don’t.

For orofacial clefts, some of the genetic variants that have regularly turned up in GWAS appeared to be in a region of DNA that doesn’t code for proteins. In this study, the MIT team set out to figure out how variants in this region might influence the development of facial malformations.

Their studies revealed that these variants are located in an enhancer region called e2p24.2. Enhancers are segments of DNA that interact with protein-coding genes, helping to activate them by binding to transcription factors that turn on gene expression.

The researchers found that this region is in close proximity to three genes, suggesting that it may control the expression of those genes. One of those genes had already been ruled out as contributing to facial malformations, and another had already been shown to have a connection. In this study, the researchers focused on the third gene, which is known as DDX1.

DDX1, it turned out, is necessary for splicing transfer RNA (tRNA) molecules, which play a critical role in protein synthesis. Each transfer RNA molecule transports a specific amino acid to the ribosome — a cell structure that strings amino acids together to form proteins, based on the instructions carried by messenger RNA.

While there are about 400 different tRNAs found in the human genome, only a fraction of those tRNAs require splicing, and those are the tRNAs most affected by the loss of DDX1. These tRNAs transport four different amino acids, and the researchers hypothesize that these four amino acids may be particularly abundant in proteins that embryonic cells that form the face need to develop properly.

When the ribosomes need one of those four amino acids, but none of them are available, the ribosome can stall, and the protein doesn’t get made.

The researchers are now exploring which proteins might be most affected by the loss of those amino acids. They also plan to investigate what happens inside cells when the ribosomes stall, in hopes of identifying a stress signal that could potentially be blocked and help cells survive.

Malfunctioning tRNA

While this is the first study to link tRNA to craniofacial malformations, previous studies have shown that mutations that impair ribosome formation can also lead to similar defects. Studies have also shown that disruptions of tRNA synthesis — caused by mutations in the enzymes that attach amino acids to tRNA, or in proteins involved in an earlier step in tRNA splicing — can lead to neurodevelopmental disorders.

“Defects in other components of the tRNA pathway have been shown to be associated with neurodevelopmental disease,” Calo says. “One interesting parallel between these two is that the cells that form the face are coming from the same place as the cells that form the neurons, so it seems that these particular cells are very susceptible to tRNA defects.”

The researchers now hope to explore whether environmental factors linked to orofacial birth defects also influence tRNA function. Some of their preliminary work has found that oxidative stress — a buildup of harmful free radicals — can lead to fragmentation of tRNA molecules. Oxidative stress can occur in embryonic cells upon exposure to ethanol, as in fetal alcohol syndrome, or if the mother develops gestational diabetes.

“I think it is worth looking for mutations that might be causing this on the genetic side of things, but then also in the future, we would expand this into which environmental factors have the same effects on tRNA function, and then see which precautions might be able to prevent any effects on tRNAs,” Bartusel says.

The research was funded by the National Science Foundation Graduate Research Program, the National Cancer Institute, the National Institute of General Medical Sciences, and the Pew Charitable Trusts.

At any given time, about 100,000 people in the U.S. are waiting to become kidney transplant recipients. Roughly one-fifth of those get a new kidney each year, but others die while waiting. In short, the demand for kidneys makes it important to think about how we use the limited supply.

A study co-authored by an MIT economist brings new data to this issue, providing nuanced estimates of the lifespan-lengthening effect of kidney transplants. That can be hard to measure well, but the study is the first to account for some of the complexities involved, including the decisions patients make when accepting kidney transplants, and some of their pre-existing health factors.

The research concludes the system in use produces an additional 9.29 life-years from transplantation (LYFT) for kidney recipients. (LYFT is the difference in median survival for those with and without transplants.) If the organs were assigned randomly to patients, the study finds, that LYFT average would only be 7.54 overall. From that perspective, the current transplant system is a net positive for patients. However, the study also finds that the LYFT figure could potentially be raised as high as 14.08, depending on how the matching system is structured.

In any case, more precise estimates about the benefits of kidney transplants can help inform policymakers about the dynamics of the matching system in use.

“There’s always this question about how to take the scarce number of organs being donated and place them efficiently, and place them well,” says MIT economist Nikhil Agarwal, co-author of a newly published paper detailing the study’s results. As he emphasizes, the point of the paper is to inform the ongoing refinement of the matching system, rather than advocate one viewpoint or another.

The paper, “Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys,” is published in the latest issue of Econometrica. The authors are Agarwal, who is a professor in MIT’s Department of Economics; Charles Hodgson, an assistant professor of economics at Yale University; and Paulo Somaini, an associate professor of economics in Stanford University’s Graduate School of Business.

After people die, there is a period lasting up to 48 hours when they could be viable organ donors. Potential kidney recipients are prioritized by time spent on wait-lists as well as tissue-type similarity, and can accept or reject any given transplant offer.

Over the last decade-plus, Agarwal has conducted significant empirical research on matching systems for organ donations, especially kidney transplants. To conduct this study, the researchers used comprehensive data about patients on the kidney wait-list from 2000-2010, made available by the Organ Procurement and Transplantation Network, the national U.S. registry. This allowed the scholars to analyze both the matching system and the health effects of transplants; they track patient survival until February 2020.

The work is the first quasiexperimental study of kidney transplants; by carefully examining the decision-making tendencies of kidney recipients, along with many other health factors, the scholars are able to evaluate the effects of a transplant, other things being equal. Recipients are more likely to select kidney offers from donors who are younger, lacked hypertension, died of head trauma (suggesting their internal organs were healthy), and with whom they have perfect tissue-type matches.

“The [previous] methodology of estimating what are the life-years benefits was not incorporating this selection issue,” Agarwal says.

Additionally, overall, a key empirical feature of kidney transplants is that recipients who are healthier overall tend to have the largest realized life-years benefits from a transplant, meaning that the greatest increase in LYFT is not found in the set of patients with the worst health.

“You might think people who are the sickest and who are most likely to die without an organ are going to benefit the most from it [in added life-years],” Agarwal says. “But there might be some other comorbidity or factor that made them sick, and their body’s going to take a toll on the new organ, so the benefits might not be as large.”

With this in mind, the maximal LYFT number of 14.08 in the study comes from, broadly, a hypothetical scenario in which an increased number of otherwise healthy people receive transplants. Again, the current system tends to prioritize time spent on a wait-list. And some observers might advocate for a system that prioritizes those who are sickest. With all that in mind, the policymaking process for kidney transplants may still involve recognition that the biggest gains in patient life-years are not necessarily aligned with other prioritization factors.

“Our results indicate … a dilemma rooted in the tension between these two goals,” the authors write in the paper.

To be clear, Agarwal is not advocating for any one system over another, but conducting data-driven research so that policy officials can make more fully informed decisions in the ongoing, long-term process of trying to refine valuable transplant networks.

“I don’t necessarily think it’s my comparative advantage to make the ethical decisions, but we can at least think about and quantify what some of the tradeoffs are,” Agarwal adds.

Support for the research was provided in part by the National Science Foundation and by the Alfred P. Sloan Foundation.