Gleevec, a cancer drug first approved for sale in 2001, has dramatically changed the lives of people with chronic myeloid leukemia. This form of cancer was once regarded as very difficult to combat, but survival rates of patients who respond to Gleevec now resemble that of the population at large.

Gleevec is also a medicine developed with the help of federally funded research. That support helped scientists better understand how to create drugs targeting the BCR-ABL oncoprotein, the cancer-causing protein behind chronic myeloid leukemia.

A new study co-authored by MIT researchers quantifies how many such examples of drug development exist. The current administration is proposing a nearly 40 percent budget reduction to the National Institutes of Health (NIH), which sponsors a significant portion of biomedical research. The study finds that over 50 percent of small-molecule drug patents this century cite at least one piece of NIH-backed research that would likely be vulnerable to that potential level of funding change.

“What we found was quite striking,” says MIT economist Danielle Li, co-author of a newly published paper outlining the study’s results. “More than half of the drugs approved by the FDA since 2000 are connected to NIH research that would likely have been cut under a 40 percent budget reduction.”

Or, as the researchers write in the paper: “We found extensive connections between medical advances and research that was funded by grants that would have been cut if the NIH budget was sharply reduced.”

The paper, “What if NIH funding had been 40% smaller?” is published today as a Policy Article in the journal Science. The authors are Pierre Azoulay, the China Program Professor of International Management at the MIT Sloan School of Management; Matthew Clancy, an economist with the group Open Philanthropy; Li, the David Sarnoff Professor of Management of Technology at MIT Sloan; and Bhaven N. Sampat, an economist at Johns Hopkins University. (Biomedical researchers at both MIT and Johns Hopkins could be affected by adjustments to NIH funding.)

To conduct the study, the researchers leveraged the fact that the NIH uses priority lists to determine which projects get funded. That makes it possible to discern which projects were in the lower 40 percent of NIH-backed projects, priority-wise, for a given time period. The researchers call these “at-risk” pieces of research. Applying these data from 1980 through 2007, the scholars examined the patents of the new molecular entities — drugs with a new active ingredient — approved by the U.S. Food and Drug Administration since 2000. There is typically a time interval between academic research and subsequent related drug development.

The study focuses on small-molecule drugs — compact organic compounds, often taken orally as medicine — whereas NIH funding supports a wider range of advancements in medicine generally. Based on how many of these FDA-approved small-molecule medicines were linked to at-risk research from the prior period, the researchers estimated what kinds of consequences a 40 percent cut in funding would have generated going forward.

The study distinguishes between two types of links new drugs have to NIH funding. Some drug patents have what the researchers call “direct” links to new NIH-backed projects that generated new findings relevant to development of those particular drugs. Other patents have “indirect “ links to the NIH, when they cite prior NIH-funded studies that contributed to the overall body of knowledge used in drug development.

The analysis finds that 40 of the FDA-approved medications have direct links to new NIH-supported studies cited in the patents — or 7.1 percent. Of these, 14 patents cite at-risk pieces of NIH research.

When it comes to indirect links, of the 557 drugs approved by the FDA from 2000 to 2023, the study found that 59.4 percent have a patent citing at least one NIH-supported research publication. And, 51.4 percent cite at least one NIH-funded study from the at-risk category of projects. 

“The indirect connection is where we see the real breadth of NIH's impact,” Li says. “What the NIH does is fund research that forms the scientific foundation upon which companies and other drug developers build.”

As the researchers emphasize in the paper, there are many nuances involved in the study. A single citation of an NIH-funded study could appear in a patent for a variety of reasons, and does not necessarily mean “that the drug in question could never have been developed in its absence,” as they write in the paper. To reckon with this, the study also analyzes how many patents had at least 25 percent of their citations fall in the category of at-risk NIH-backed research. By this metric, they found that 65 of the 557 FDA-approved drugs, or 11.7 percent, met the threshold.

On the other hand, as the researchers state in the paper, it is possible the study “understates the extent to which medical advances are connected to NIH research.” For one thing, as the study’s endpoint for examining NIH data is 2007, there could have been more recent pieces of research informing medications that have already received FDA approval. The study does not quantify “second-order connections,” in which NIH-supported findings may have led to additional research that directly led to drug development. Again, NIH funding also supports a broad range of studies beyond the type examined in the current paper.

It is also likely, the scholars suggest, that NIH cuts would curtail the careers of many promising scientists, and in so doing slowdown medical progress. For a variety of these reasons, in addition to the core data itself, the scholars say the study indicates how broadly NIH-backed research has helped advance medicine.

“The worry is that these kinds of deep cuts to the NIH risk that foundation and therefore endanger the development of medicines that might be used to treat us, or our kids and grandkids, 20 years from now,” Li says.

Azoulay and Sampat have received past NIH funding. They also serve on an NIH working group about the empirical analysis of the scientific enterprise.

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience or intuition, and input from colleagues and peer reviewers.

Now, MIT researchers have developed a method for optimizing materials recipes and planning experiments that incorporates information from diverse sources like insights from the literature, chemical compositions, microstructural images, and more. The approach is part of a new platform, named Copilot for Real-world Experimental Scientists (CRESt), that also uses robotic equipment for high-throughput materials testing, the results of which are fed back into large multimodal models to further optimize materials recipes.

Human researchers can converse with the system in natural language, with no coding required, and the system makes its own observations and hypotheses along the way. Cameras and visual language models also allow the system to monitor experiments, detect issues, and suggest corrections.

“In the field of AI for science, the key is designing new experiments,” says Ju Li, School of Engineering Carl Richard Soderberg Professor of Power Engineering. “We use multimodal feedback — for example information from previous literature on how palladium behaved in fuel cells at this temperature, and human feedback — to complement experimental data and design new experiments. We also use robots to synthesize and characterize the material’s structure and to test performance.”

The system is described in a paper published in Nature. The researchers used CRESt to explore more than 900 chemistries and conduct 3,500 electrochemical tests, leading to the discovery of a catalyst material that delivered record power density in a fuel cell that runs on formate salt to produce electricity.

Joining Li on the paper as first authors are PhD student Zhen Zhang, Zhichu Ren PhD ’24, PhD student Chia-Wei Hsu, and postdoc Weibin Chen. Their coauthors are MIT Assistant Professor Iwnetim Abate; Associate Professor Pulkit Agrawal; JR East Professor of Engineering Yang Shao-Horn; MIT.nano researcher Aubrey Penn; Zhang-Wei Hong PhD ’25, Hongbin Xu PhD ’25; Daniel Zheng PhD ’25; MIT graduate students Shuhan Miao and Hugh Smith; MIT postdocs Yimeng Huang, Weiyin Chen, Yungsheng Tian, Yifan Gao, and Yaoshen Niu; former MIT postdoc Sipei Li; and collaborators including Chi-Feng Lee, Yu-Cheng Shao, Hsiao-Tsu Wang, and Ying-Rui Lu.

A smarter system

Materials science experiments can be time-consuming and expensive. They require researchers to carefully design workflows, make new material, and run a series of tests and analysis to understand what happened. Those results are then used to decide how to improve the material.

To improve the process, some researchers have turned to a machine-learning strategy known as active learning to make efficient use of previous experimental data points and explore or exploit those data. When paired with a statistical technique known as Bayesian optimization (BO), active learning has helped researchers identify new materials for things like batteries and advanced semiconductors.

“Bayesian optimization is like Netflix recommending the next movie to watch based on your viewing history, except instead it recommends the next experiment to do,” Li explains. “But basic Bayesian optimization is too simplistic. It uses a boxed-in design space, so if I say I’m going to use platinum, palladium, and iron, it only changes the ratio of those elements in this small space. But real materials have a lot more dependencies, and BO often gets lost.”

Most active learning approaches also rely on single data streams that don’t capture everything that goes on in an experiment. To equip computational systems with more human-like knowledge, while still taking advantage of the speed and control of automated systems, Li and his collaborators built CRESt.

CRESt’s robotic equipment includes a liquid-handling robot, a carbothermal shock system to rapidly synthesize materials, an automated electrochemical workstation for testing, characterization equipment including automated electron microscopy and optical microscopy, and auxiliary devices such as pumps and gas valves, which can also be remotely controlled.  Many processing parameters can also be tuned.

With the user interface, researchers can chat with CRESt and tell it to use active learning to find promising materials recipes for different projects. CRESt can include up to 20 precursor molecules and substrates into its recipe. To guide material designs, CRESt’s models search through scientific papers for descriptions of elements or precursor molecules that might be useful. When human researchers tell CRESt to pursue new recipes, it kicks off a robotic symphony of sample preparation, characterization, and testing. The researcher can also ask CRESt to perform image analysis from scanning electron microscopy imaging, X-ray diffraction, and other sources.

Information from those processes is used to train the active learning models, which use both literature knowledge and current experimental results to suggest further experiments and accelerate materials discovery.

“For each recipe we use previous literature text or databases, and it creates these huge representations of every recipe based on the previous knowledge base before even doing the experiment,” says Li. “We perform principal component analysis in this knowledge embedding space to get a reduced search space that captures most of the performance variability. Then we use Bayesian optimization in this reduced space to design the new experiment. After the new experiment, we feed newly acquired multimodal experimental data and human feedback into a large language model to augment the knowledgebase and redefine the reduced search space, which gives us a big boost in active learning efficiency.”

Materials science experiments can also face reproducibility challenges. To address the problem, CRESt monitors its experiments with cameras, looking for potential problems and suggesting solutions via text and voice to human researchers.

The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell. After exploring more than 900 chemistries over three months, CRESt discovered a catalyst material made from eight elements that achieved a 9.3-fold improvement in power density per dollar over pure palladium, an expensive precious metal. In further tests, CRESTs material was used to deliver a record power density to a working direct formate fuel cell even though the cell contained just one-fourth of the precious metals of previous devices.

The results show the potential for CRESt to find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

“A significant challenge for fuel-cell catalysts is the use of precious metal,” says Zhang. “For fuel cells, researchers have used various precious metals like palladium and platinum. We used a multielement catalyst that also incorporates many other cheap elements to create the optimal coordination environment for catalytic activity and resistance to poisoning species such as carbon monoxide and adsorbed hydrogen atom. People have been searching low-cost options for many years. This system greatly accelerated our search for these catalysts.”

A helpful assistant

Early on, poor reproducibility emerged as a major problem that limited the researchers’ ability to perform their new active learning technique on experimental datasets. Material properties can be influenced by the way the precursors are mixed and processed, and any number of problems can subtly alter experimental conditions, requiring careful inspection to correct.

To partially automate the process, the researchers coupled computer vision and vision language models with domain knowledge from the scientific literature, which allowed the system to hypothesize sources of irreproducibility and propose solutions. For example, the models can notice when there’s a millimeter-sized deviation in a sample’s shape or when a pipette moves something out of place. The researchers incorporated some of the model’s suggestions, leading to improved consistency, suggesting the models already make good experimental assistants.

The researchers noted that humans still performed most of the debugging in their experiments.

“CREST is an assistant, not a replacement, for human researchers,” Li says. “Human researchers are still indispensable. In fact, we use natural language so the system can explain what it is doing and present observations and hypotheses. But this is a step toward more flexible, self-driving labs.”

Mucus is more than just a sticky substance: It contains a wealth of powerful molecules called mucins that help to tame microbes and prevent infection. In a new study, MIT researchers have identified mucins that defend against Salmonella and other bacteria that cause diarrhea.

The researchers now hope to mimic this defense system to create synthetic mucins that could help prevent or treat illness in soldiers or other people at risk of exposure to Salmonella. It could also help prevent “traveler’s diarrhea,” a gastrointestinal infection caused by consuming contaminated food or water.

Mucins are bottlebrush-shaped polymers made of complex sugar molecules known as glycans, which are tethered to a peptide backbone. In this study, the researchers discovered that a mucin called MUC2 turns off genes that Salmonella uses to enter and infect host cells.

“By using and reformatting this motif from the natural innate immune system, we hope to develop strategies to preventing diarrhea before it even starts. This approach could provide a low-cost solution to a major global health challenge that costs billions annually in lost productivity, health care expenses, and human suffering,” says Katharina Ribbeck, the Andrew and Erna Viterbi Professor of Biological Engineering at MIT and the senior author of the study.

MIT Research Scientist Kelsey Wheeler PhD ’21 and Michaela Gold PhD ’22 are the lead authors of the paper, which appeared Tuesday in the journal Cell Reports.

Blocking infection

Mucus lines much of the body, providing a physical barrier to infection, but that’s not all it does. Over the past decade, Ribbeck has identified mucins that can help to disarm Vibrio cholerae, as well as Pseudomonas aeruginosa, which can infect the lungs and other organs, and the yeast Candida albicans.

In the new study, the researchers wanted to explore how mucins from the digestive tract might interact with Salmonella enterica, a foodborne pathogen that can cause illness after consuming raw or undercooked food, or contaminated water.

To infect host cells, Salmonella must produce proteins that are part of the type 3 secretion system (T3SS), which helps bacteria form needle-like complexes that transfer bacterial proteins directly into host cells. These proteins are all encoded on a segment of DNA called Salmonella pathogenicity island 1 (SPI-1).

The researchers found that when they exposed Salmonella to a mucin called MUC2, which is found in the intestines, the bacteria stopped producing the proteins encoded by SPI-1, and they were no longer able to infect cells.

Further studies revealed that MUC2 achieves this by turning off a regulatory bacterial protein known as HilD. When this protein is blocked by mucins, it can no longer activate the T3SS genes.

Using computational simulations, the researchers showed that certain monosaccharides found in glycans, including GlcNAc and GalNAc, can attach to a specific binding site of the HilD protein. However, their studies showed that these monosaccharides can’t turn off HilD on their own — the shutoff only occurs when the glycans are tethered to the peptide backbone of the mucin.

The researchers also discovered that a similar mucin called MUC5AC, which is found in the stomach, can block HilD. And, both MUC2 and MUC5AC can turn off virulence genes in other foodborne pathogens that also use HilD as a gene regulator.

Mucins as medicine

Ribbeck and her students now plan to explore ways to use synthetic versions of these mucins to help boost the body’s natural defenses and protect the GI tract from Salmonella and other infections.

Studies from other labs have shown that in mice, Salmonella tends to infect portions of the GI tract that have a thin mucus barrier, or no barrier at all.

“Part of Salmonella’s evasion strategy for this host defense is to find locations where mucus is absent and then infect there. So, one could imagine a strategy where we try to bolster mucus barriers to protect those areas with limited mucin,” Wheeler says.

One way to deploy synthetic mucins could be to add them to oral rehydration salts — mixtures of electrolytes that are dissolved in water and used to treat dehydration caused by diarrhea and other gastrointestinal illnesses.

Another potential application for synthetic mucins would be to incorporate them into a chewable tablet that could be consumed before traveling to areas where Salmonella and other diarrheal illnesses are common. This kind of “pre-exposure prophylaxis” could help prevent a great deal of suffering and lost productivity due to illness, the researchers say.

“Mucin mimics would particularly shine as preventatives, because that’s how the body evolved mucus — as part of this innate immune system to prevent infection,” Wheeler says.

The research was funded by the U.S. Army Research Office, the U.S. Army Institute for Collaborative Biotechnologies, the U.S. National Science Foundation, the U.S. National Institute of Health and Environmental Sciences, the U.S. National Institutes of Health, and the German Research Foundation.

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.

For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain scans. For many structures and image types, this is often a manual process that can be extremely time-consuming, especially if the regions being studied are challenging to delineate.

To streamline the process, MIT researchers developed an artificial intelligence-based system that enables a researcher to rapidly segment new biomedical imaging datasets by clicking, scribbling, and drawing boxes on the images. This new AI model uses these interactions to predict the segmentation.

As the user marks additional images, the number of interactions they need to perform decreases, eventually dropping to zero. The model can then segment each new image accurately without user input.

It can do this because the model’s architecture has been specially designed to use information from images it has already segmented to make new predictions.

Unlike other medical image segmentation models, this system allows the user to segment an entire dataset without repeating their work for each image.

In addition, the interactive tool does not require a presegmented image dataset for training, so users don’t need machine-learning expertise or extensive computational resources. They can use the system for a new segmentation task without retraining the model.

In the long run, this tool could accelerate studies of new treatment methods and reduce the cost of clinical trials and medical research. It could also be used by physicians to improve the efficiency of clinical applications, such as radiation treatment planning.

“Many scientists might only have time to segment a few images per day for their research because manual image segmentation is so time-consuming. Our hope is that this system will enable new science by allowing clinical researchers to conduct studies they were prohibited from doing before because of the lack of an efficient tool,” says Hallee Wong, an electrical engineering and computer science graduate student and lead author of a paper on this new tool.

She is joined on the paper by Jose Javier Gonzalez Ortiz PhD ’24; John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering; and senior author Adrian Dalca, an assistant professor at Harvard Medical School and MGH, and a research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference on Computer Vision.

Streamlining segmentation

There are primarily two methods researchers use to segment new sets of medical images. With interactive segmentation, they input an image into an AI system and use an interface to mark areas of interest. The model predicts the segmentation based on those interactions.

A tool previously developed by the MIT researchers, ScribblePrompt, allows users to do this, but they must repeat the process for each new image.

Another approach is to develop a task-specific AI model to automatically segment the images. This approach requires the user to manually segment hundreds of images to create a dataset, and then train a machine-learning model. That model predicts the segmentation for a new image. But the user must start the complex, machine-learning-based process from scratch for each new task, and there is no way to correct the model if it makes a mistake.

This new system, MultiverSeg, combines the best of each approach. It predicts a segmentation for a new image based on user interactions, like scribbles, but also keeps each segmented image in a context set that it refers to later.

When the user uploads a new image and marks areas of interest, the model draws on the examples in its context set to make a more accurate prediction, with less user input.

The researchers designed the model’s architecture to use a context set of any size, so the user doesn’t need to have a certain number of images. This gives MultiverSeg the flexibility to be used in a range of applications.

“At some point, for many tasks, you shouldn’t need to provide any interactions. If you have enough examples in the context set, the model can accurately predict the segmentation on its own,” Wong says.

The researchers carefully engineered and trained the model on a diverse collection of biomedical imaging data to ensure it had the ability to incrementally improve its predictions based on user input.

The user doesn’t need to retrain or customize the model for their data. To use MultiverSeg for a new task, one can upload a new medical image and start marking it.

When the researchers compared MultiverSeg to state-of-the-art tools for in-context and interactive image segmentation, it outperformed each baseline.

Fewer clicks, better results

Unlike these other tools, MultiverSeg requires less user input with each image. By the ninth new image, it needed only two clicks from the user to generate a segmentation more accurate than a model designed specifically for the task.

For some image types, like X-rays, the user might only need to segment one or two images manually before the model becomes accurate enough to make predictions on its own.

The tool’s interactivity also enables the user to make corrections to the model’s prediction, iterating until it reaches the desired level of accuracy. Compared to the researchers’ previous system, MultiverSeg reached 90 percent accuracy with roughly 2/3 the number of scribbles and 3/4 the number of clicks.

“With MultiverSeg, users can always provide more interactions to refine the AI predictions. This still dramatically accelerates the process because it is usually faster to correct something that exists than to start from scratch,” Wong says.

Moving forward, the researchers want to test this tool in real-world situations with clinical collaborators and improve it based on user feedback. They also want to enable MultiverSeg to segment 3D biomedical images.

This work is supported, in part, by Quanta Computer, Inc. and the National Institutes of Health, with hardware support from the Massachusetts Life Sciences Center.

People are increasingly turning to software to design complex material structures like airplane wings and medical implants. But as design models become more capable, our fabrication techniques haven’t kept up. Even 3D printers struggle to reliably produce the precise designs created by algorithms. The problem has led to a disconnect between the ways a material is expected to perform and how it actually works.

Now, MIT researchers have created a way for models to account for 3D printing’s limitations during the design process. In experiments, they showed their approach could be used to make materials that perform much more closely to the way they’re intended to.

“If you don’t account for these limitations, printers can either over- or under-deposit material by quite a lot, so your part becomes heavier or lighter than intended. It can also over- or underestimate the material performance significantly,” says Gilbert W. Winslow Associate Professor of Civil and Environmental Engineering Josephine Carstensen. “With our technique, you know what you’re getting in terms of performance because the numerical model and experimental results align very well.”

The approach is described in the journal Materials and Design, in an open-access paper co-authored by Carstensen and PhD student Hajin Kim-Tackowiak.

Matching theory with reality

Over the last decade, new design and fabrication technologies have transformed the way things are made, especially in industries like aerospace, automotive, and biomedical engineering, where materials must reach precise weight-to-strength ratios and other performance thresholds. In particular, 3D printing allows materials to be made with more complex internal structures.

“3D printing processes generally give us more flexibility because we don’t have to come up with forms or molds for things that would be made through more traditional means like injection molding,” Kim-Tackowiak explains.

As 3D printing has made production more precise, so have methods for designing complex material structures. One of the most advanced computational design techniques is known as topology optimization. Topology optimization has been used to generate new and often surprising material structures that can outperform conventional designs, in some cases approaching the theoretical limits of certain performance thresholds. It is currently being used to design materials with optimized stiffness and strength, maximized energy absorption, fluid permeability, and more.

But topology optimization often creates designs at extremely fine scales that 3D printers have struggled to reliably reproduce. The problem is the size of the print head that extrudes the material. If the design specifies a layer to be 0.5 millimeters thick, for instance, and the print head is only capable of extruding 1-millimeter-thick layers, the final design will be warped and imprecise.

Another problem has to do with the way 3D printers create parts, with a print head extruding a thin bead of material as it glides across the printing area, gradually building parts layer by layer. That can cause weak bonding between layers, making the part more prone to separation or failure.

The researchers sought to address the disconnect between expected and actual properties of materials that arise from those limitations.

“We thought, ‘We know these limitations in the beginning, and the field has gotten better at quantifying these limitations, so we might as well design from the get-go with that in mind,” Kim-Tackowiak says.

In previous work, Carstensen developed an algorithm that embedded information about the print nozzle size into design algorithms for beam structures. For this paper, the researchers built off that approach to incorporate the direction of the print head and the corresponding impact of weak bonding between layers. They also made it work with more complex, porous structures that can have extremely elastic properties.

The approach allows users to add variables to the design algorithms that account for the center of the bead being extruded from a print head and the exact location of the weaker bonding region between layers. The approach also automatically dictates the path the print head should take during production.

The researchers used their technique to create a series of repeating 2D designs with various sizes of hollow pores, or densities. They compared those creations to materials made using traditional topology optimization designs of the same densities.

In tests, the traditionally designed materials deviated from their intended mechanical performance more than materials designed using the researchers’ new technique at material densities under 70 percent. The researchers also found that conventional designs consistently over-deposited material during fabrication. Overall, the researchers’ approach led to parts with more reliable performance at most densities.

“One of the challenges of topology optimization has been that you need a lot of expertise to get good results, so that once you take the designs off the computer, the materials behave the way you thought they would,” Carstensen says. “We’re trying to make it easy to get these high-fidelity products.”

Scaling a new design approach

The researchers believe this is the first time a design technique has accounted for both the print head size and weak bonding between layers.

“When you design something, you should use as much context as possible,” Kim-Tackowiak says. “It was rewarding to see that putting more context into the design process makes your final materials more accurate. It means there are fewer surprises. Especially when we’re putting so much more computational resources into these designs, it’s nice to see we can correlate what comes out of the computer with what comes out of the production process.”

In future work, the researchers hope to improve their method for higher material densities and for different kinds of materials like cement and ceramics. Still, they said their approach offered an improvement over existing techniques, which often require experienced 3D printing specialists to help account for the limitations of the machines and materials.

“It was cool to see that just by putting in the size of your deposition and the bonding property values, you get designs that would have required the consultation of somebody who’s worked in the space for years,” Kim-Tackowiak says.

The researchers say the work paves the way to design with more materials.

“We’d like to see this enable the use of materials that people have disregarded because printing with them has led to issues,” Kim-Tackowiak says. “Now we can leverage those properties or work with those quirks as opposed to just not using all the material options we have at our disposal.”

MIT professors Zachary Hartwig and Wanda Orlikowski exemplify a rare but powerful kind of mentorship — one grounded not just in intellectual excellence, but in deep personal care. They remind us that transformative academic leadership starts with humanity. 

Whether it's Hartwig’s ability to bring engineering brilliance to life through genuine personal connection, or Orlikowski’s unwavering support for those who share in her mission to create meaningful impact, both foster environments where people, not just ideas, can thrive. 

Their students and colleagues describe feeling seen, supported, and encouraged not only to grow as scholars, but as people. It’s this ethic of care, of valuing the human behind the research, that defines their mentorship and elevates those around them.

Hartwig and Orlikowski are two of the 2023-25 Committed to Caring cohort who are fostering transformative research through growth, independence, and support. For MIT graduate students, the Committed to Caring program recognizes those who go above and beyond.

Zachary Hartwig: Signposts on the way to new territory

Zachary (Zach) Seth Hartwig is an associate professor in the Department of Nuclear Science and Engineering (NSE) with a co-appointment at the MIT Plasma Science and Fusion Center (PSFC). He has worked in the areas of large-scale applied superconductivity, magnet fusion device design, radiation detector development, and accelerator science and engineering. His active research focuses on the development of high-field superconducting magnet technologies for fusion energy and accelerated irradiation methods for fusion materials using ion beams.

One nominator expressed, “although he didn't formally become my advisor until after I submitted my thesis prospectus, I always felt like Zach had my back.” This feeling of support was shared by Hartwig’s advisees through numerous examples.

When the pandemic started, Hartwig made sure that the student had ongoing support and a safe place to simply exist as an international visiting student during a tumultuous time. This care often presented in small ways: when the mentee needed to debug their cryogenic system, Hartwig showed up at the lab every day to help plan the next test; when this same student struggled to write the introduction of their first paper, Hartwig continued to provide support; and when the student wanted to practice for their qualifying exam, Hartwig insisted on helping until the last day. Additionally, when the advisee’s funding was nearing its end, Hartwig secured transition support to bridge the gap.

The nominator reflected on Hartwig’s cheerful and positive mentorship style, noting that “through it all, he … always valued my ideas, he was never judgmental, he never raised his voice, he never dismissed me.” 

Hartwig characterizes himself as “highly supportive, but from the backseat.” He is active with and available to his students; however, it is essential to him that they are the ones driving the research. “Graduate students need to experience increasing amounts of autonomy, but within a supportive framework that fades as they need to rely on it less and less as they become independent researchers,” he notes.

Hartwig shapes the intellectual maturation of his students. He believes that graduate school is not solely about results or publications, but about whom students become in the process. 

“The most important output of a PhD program is not your results, your papers, or your thesis; it’s YOU,” he emphasizes. His mentorship is built around this philosophy, creating an environment where students steadily evolve into independent researchers.

Importantly, Hartwig cultivates a culture where daring, unconventional ideas are not just allowed — they’re encouraged. He models this approach through his own career, which has taken bold leaps across disciplines and technologies. 

“MIT should do things only MIT can do,” he tells his students. His message is clear: Graduate students should not be afraid to go against the grain.

This philosophy has inspired many of his students to explore nontraditional research paths, armed with the confidence that failure is not a setback, but a sign that they are asking ambitious questions. Hartwig regularly reinforces this, reminding students that null results and dead ends often teach us the most. 

“They’re the signposts you have to pass on the way to new territory,” he says.

Ultimately, one of the most fulfilling parts of Hartwig’s work is witnessing the moment when it all “clicks” for a student — when they begin to lead boldly, push back thoughtfully, and take true ownership of their research. “It’s a beautiful thing when it happens,” he reflects. 

For Hartwig, mentorship is about fostering not only the skills of a scientist, but the identity of one. His students don’t just grow in knowledge, they grow in courage, conviction, and clarity.

Wanda Orlikowski: Shaping research by supporting the people who make it happen

Wanda Orlikowski is the Alfred P. Sloan Professor of Information Technology and Organization Studies at MIT’s Sloan School of Management. Her research examines technologies in the workplace, with a particular focus on how digital reconfigurations generate significant shifts in organizing, coordination, and accountability. She is currently exploring the digital transformation of work.

Through times of uncertainty, students always find support in Orlikowski. One of her nominators shared that they have encountered many moments of doubt during the research development phase of their dissertation. “I [have had] concerns … that I'm not making progress. I do all this work, and it’s not going anywhere, I keep returning back to where I started,” the mentee reflected. 

Orlikowski has walked this advisee through those moments patiently and with great empathy, connecting her own experiences with those of her students. She often talks about the research process not being a straight line of progress, but rather a spiral. 

“This metaphor … suggests that coming back to ideas again and again is in fact progress,” rather than a failure. “Every time I come back to it, I’m at a higher plane, and I’m refining the same idea further and further,” the nominator wrote.

Students say that Orlikowski makes an effort to support them through moments of doubt, turning these moments into opportunities for growth. “It has … been such a benefit for me to have her near-constant availability,” the student said. “She listens to my thoughts and lets me just talk and spitball ideas, without her interrupting.” 

Orlikowski pushes and prods her students to elaborate, clarify, and expand their thoughts. She does this proactively, spending many hours every week talking to her students, reading their writing, and making scrupulous comments on their work. 

Orlikowski has been remarkably perceptive when her students need support. One of the nominators struggled during their first holiday season in the PhD program, unable to visit their family. Orlikowski noticed the student’s isolation and reached out, inviting the student to her family’s Christmas dinner, a gesture that turned into a heartwarming tradition. 

“I gave her an orchid that first year, and to this day, it continues to bloom each year. Wanda regularly sends me pictures of it, and the joy she expresses in keeping it alive means so much to me. I feel that in her care, both the orchid and our connection have flourished,” the mentee remarks.

“One of the things I’ve appreciated most about Wanda is that she has never tried to change who I am,” the nominator adds. They go on to describe themselves as not a very strategic or extroverted person by nature, and for a long time, they struggled with the idea that these qualities might hinder their success in academia. “Wanda has helped me embrace my true self.”

“It’s not about fitting into a mold,” Orlikowski reminded the student, “It’s about being true to who you are, and doing great work.” Her support has made the student comfortable with their approach to both research and life.

The academic world often feels like it rewards self-promotion and strategic maneuvering, but Orlikowski has alleviated much of her students’ anxiety about whether they can be competitive without it. “You don’t have to pretend to be something you’re not,” she assures them. “The work will speak for itself.” 

Orlikowski’s support for her students extends beyond encouragement; she advocates for their work, helping them gain visibility and traction in the broader academic community. “It’s not just words — she has actively supported me, promoting my work through her network of students and peers,” the nominator articulated. 

Her belief in her mentees, and her willingness to support their work, has had a profound impact on their academic journey.

Just as the United States has prospered through its ability to draw talent from every corner of the globe, so too has MIT thrived as a magnet for the world’s most keen and curious minds — many of whom remain here to invent solutions, create companies, and teach future leaders, contributing to America’s success.

President Ronald Reagan remarked in 1989 that the United States leads the world “because, unique among nations, we draw our people — our strength — from every country and every corner of the world. And by doing so we continuously renew and enrich our nation.” Those words ring still ring true 36 years later — and the sentiment resonates especially at MIT.

"To find people with the drive, skill, and daring to see, discover, and invent things no one else can, we open ourselves to talent from every corner of the United States and from around the globe,” says MIT President Sally Kornbluth. “MIT is an American university, proudly so — but we would be gravely diminished without the students and scholars who join us from other nations."

MIT’s steadfast commitment to attracting the best and brightest talent from around the world has contributed to not just its own success, but also that of the nation as whole. MIT’s stature as an international hub of education and innovation adds value to the U.S. economy and competitiveness in myriad ways — from foreign-born faculty delivering breakthroughs here and founding American companies that create American jobs to international students contributing over $264 million annually to the U.S. economy during the 2023-24 school year.

Highlighting the extent and value of its global character, the Office of the Vice Provost for International Activities recently expanded a new video series, “The World at MIT.” In it, 20 faculty members born outside the United States tell how they dreamed of coming to MIT while growing up abroad and eventually joined the MIT faculty, where they’ve helped establish and maintain global leadership in science while teaching the next generation of innovators. A common thread running through their stories is the importance of the campus’s distinct nature as a community that is both profoundly American and deeply connected to the people, institutions, and concerns of regions and nations around the globe.

Joining the MIT faculty in 1980, MIT President Emeritus L. Rafael Reif knew almost instantly that he would stay.

“I was impressed by the richness of the variety of groups of people and cultures here,” says Reif, who moved to the United States from Venezuela and eventually served as MIT’s president from 2012 to 2022. “There is no richer place than MIT, because every point of view is here. That is what makes the place so special.”

The benefits of welcoming international students and researchers to campus extend well beyond MIT. More than 17,000 MIT alumni born elsewhere now call the United States home, for example, and many have founded U.S.-based companies that have generated billions of dollars in economic activity.

Contributing to America’s prestige internationally, one-third of MIT’s 104 Nobel laureates — including seven of the eight Nobel winners over the last decade — were born abroad. Drawn to MIT, they went on to make their breakthroughs in the United States. Among them is Lester Wolfe Professor of Chemistry Moungi Bawendi, who won the Nobel Prize in Chemistry in 2023 for his work in the chemical production of high-quality quantum dots.   

“MIT is a great environment. It’s very collegial, very collaborative. As a result, we also have amazing students,” says Bawendi, who lived in France and Tunisia as a child before moving to the U.S. “I couldn’t have done my first three years here, which eventually got me a Nobel Prize, without having really bold, smart, adventurous graduate students.”

The give-and-take among MIT faculty and students also inspires electrical engineering and computer science professor Akintunde Ibitayo (Tayo) Akinwande, who grew up in Nigeria.

“Anytime I teach a class, I always learn something from my students’ probing questions,” Akinwande says. “It gives me new insights sometimes, and that’s always the kind of environment I like — where I’m learning something new all the time.”

MIT’s global vibe inspires its students to not only explore worlds of ideas in campus labs and classrooms, but to journey the world itself. Forty-three percent of undergraduates pursued international experiences during the last academic year — taking courses at foreign universities, conducting research, or interning at multinational companies. MIT students and faculty alike are regularly engaged in research outside the United States, addressing some of the world’s toughest challenges and devising solutions that can be deployed back home, as well as abroad. In so doing, they embody MIT’s motto of “mens et manus” (“mind and hand”), reflecting the educational ideals of MIT’s founders who promoted education for practical application.

As someone who loves exploring “lofty questions” along with the practical design of things, Nergis Mavalvala found a perfect fit at MIT and calls her position as the Marble Professor of Astrophysics and dean of the School of Science “the best job in the world.”

“Everybody here wants to make the world a better place and are using their intellectual gifts and their education to do so,” says Mavalvala, who emigrated from Pakistan. “And I think that’s an amazing community to be part of.”

Daniela Rus agrees. Now the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of MIT’s Computer Science and Artificial Intelligence Laboratory, Rus was drawn to the practical application of mathematics while still a student in her native Romania.   

“And so, now here I am at MIT, essentially bringing together the world of science and math with the world of making things,” Rus says. “I’ve been here for two decades, and it’s been an extraordinary journey.”

The daughter of an Albert Einstein afficionado, Yukiko Yamashita grew up in Japan thinking of science not as a job, but a calling. MIT, where she is a professor of biology, is a place where people “are really open to unconventional ideas” and “intellectual freedom” thrives.

“There is something sacred about doing science. That’s how I grew up,” Yamashita says. “There are some distinct MIT characteristics. In a good way, people can’t let go. Every day, I am creating more mystery than I answer.”

For more about the paths that brought Yamashita and others to MIT and stories of how their disparate personal histories enrich the campus and wider community, visit the “World at MIT” videos website.

“Our global community’s multiplicity of ideas, experiences, and perspectives contributes enormously to MIT’s innovative and entrepreneurial spirit and, by extension, to the innovation and competitiveness of the U.S.,” says Vice Provost for International Activities Duane Boning, whose department developed the video series. “The bottom line is that both MIT and the U.S. grow stronger when we harness the talents of the world’s best and brightest.”

Whitney Zhang ’21 believes in the importance of valuing workers regardless of where they fit into an organizational chart.

Zhang is a PhD student in MIT’s Department of Economics studying labor economics. She explores how the technological and managerial decisions companies make affect workers across the pay spectrum. 

“I’ve been interested in economics, economic impacts, and related social issues for a long time,” says Zhang, who majored in mathematical economics as an undergraduate. “I wanted to apply my math skills to see how we could improve policies and their effects.”

Zhang is interested in how to improve conditions for workers. She believes it’s important to build relationships with policymakers, focusing on an evidence-driven approach to policy, while always remembering to center those the policies may affect. “We have to remember the people whose lives are impacted by business operations and legislation,” she says. 

She’s also aware of the complex intermixture of politics, social status, and financial obligations organizations and their employees have to navigate.

“Though I’m studying workers, it’s important to consider the entire complex ecosystem when solving for these kinds of challenges, including firm incentives and global economic conditions,” she says.

The intersection of tech and labor policy

Zhang began investigating employee productivity, artificial intelligence, and related economic and labor market phenomena early in her time as a doctoral student, collaborating frequently with fellow PhD students in the department.

A collaboration with economics doctoral student Shakked Noy yielded the 2023 study investigating ChatGPT as a tool to improve productivity. Their research found it substantially increased workers’ productivity on writing tasks, most so for workers who initially performed the worst on the tasks.

“This was one of the earliest pieces of evidence on the productivity effects of generative AI, and contributed to providing concrete data on how impactful these types of tools might be in the workplace and on the labor market,” Zhang says.

In other ongoing research — “Determinants of Irregular Worker Schedules” — Zhang is using data from a payroll provider to examine scheduling unpredictability, investigating why companies employ unpredictable schedules and how these schedules affect low-wage employees’ quality of life.

The scheduling project, conducted with MIT economics PhD student Nathan Lazarus, is motivated, in part, by existing sociological evidence that low-wage workers’ unpredictable schedules are associated with worse sleep and well-being. “We’ve seen a relationship between higher turnover and inconsistent, inadequate schedules, which suggests workers dis-prefer these kinds of schedules,” Zhang says.

At an academic roundtable, Zhang presented her results to Starbucks employees involved in scheduling and staffing. The attendees wanted to learn more about how different scheduling practices impacted workers and their productivity. “These are the kinds of questions that could reveal useful information for small businesses, large corporations, and others,” she says.

By conducting this research, Zhang hopes to better understand whether or not scheduling regulations can improve affected employees’ quality of life, while also considering potential unintended consequences. “Why are these schedules set the way they’re set?” she asks. “Do businesses with these kinds of schedules require increased regulation?”

Another project, conducted with MIT economics doctoral student Arjun Ramani, examines the linkages between offshoring, remote work, and related outcomes. “Do the technological and managerial practices that have made remote work possible further facilitate offshoring?” she asks. “Do organizations see significant gains in efficiency? What are the impacts on U.S. and offshore workers?”

Her work is being funded through the National Science Foundation Graduate Research Fellowship Program and the Washington Center for Equitable Growth.

Putting people at the center

Zhang has observed the different kinds of people economics and higher education could bring together. She followed a dual enrollment track in high school, completing college-level courses with students from across a variety of demographic identities. “I enjoyed centering people in my work,” she says. “Taking classes with a diverse group of students, including veterans and mothers returning to school to complete their studies, made me more curious about socioeconomic issues and the policies relevant to them.”

She later enrolled at MIT, where she participated in the Undergraduate Research Opportunities Program (UROP). She also completed an internship at the World Bank, worked as a summer analyst at the Federal Reserve Bank of New York, and worked as an assistant for a diverse faculty cohort including MIT economists David Autor, Jon Gruber, and Nina Roussille. Autor is her primary advisor on her doctoral research, a mentor she cites as a significant influence.

“[Autor’s] course, 14.03 (Microeconomics and Public Policy), cemented connections between theory and practice,” she says. “I thought the class was revelatory in showing the kinds of questions economics can answer.”

Doctoral study has revealed interesting pathways of investigation for Zhang, as have her relationships with her student peers and other faculty. She has, for example, leveraged faculty connections to gain access to hourly wage data in support of her scheduling and employee impacts work. “Generally, economists have had administrative data on earnings, but not on hours,” she notes.

Zhang’s focus on improving others’ lives extends to her work outside the classroom. She’s a mentor for the Boston Chinatown Neighborhood Center College Access Program and a member of MIT’s Graduate Christian Fellowship group. When she’s not enjoying spicy soups or paddling on the Charles, she takes advantage of opportunities to decompress with her art at W20 Arts Studios.

“I wanted to create time for myself outside of research and the classroom,” she says.

Zhang cites the benefits of MIT’s focus on cross-collaboration and encouraging students to explore other disciplines. As an undergraduate, Zhang minored in computer science, which taught her coding skills critical to her data work. Exposure to engineering also led her to become more interested in questions around how technology and workers interact.

Working with other scholars in the department has improved how Zhang conducts inquiries. “I’ve become the kind of well-rounded student and professional who can identify and quantify impacts, which is invaluable for future projects,” she says. Exposure to different academic and research areas, Zhang argues, helps increase access to ideas and information.

U.S. Air Force Maj. Adam Fuhrmann ’11 was one of 10 individuals chosen from a field of 8,000 applicants for the 2025 U.S. astronaut candidate class, NASA announced in a live ceremony on Sept. 22. 

This is NASA’s 24th class of astronaut candidates since the first Mercury 7 astronauts were chosen in 1959. Upon completion of his training, Fuhrmann will be the 45th MIT graduate to become a flight-eligible astronaut.

“As test pilots we don't do anything on our own, we work with amazing teams of engineers and maintenance professionals to plan, simulate, and execute complex and sometimes risky missions in aircraft to collect data and accomplish a mission, all while assessing risk and making smart calls as a team to do that as safely as possible,” Fuhrmann said at NASA’s announcement ceremony in Houston, Texas. “I'm happy to try to bring some of that experience to do the same thing with the NASA team and learn from everyone at Johnson Space Center how to apply those lessons to human spaceflight.”

His class now begins two years of training at the Johnson Space Center in Houston that includes instruction and skills development for complex operations aboard the International Space Station, Artemis missions to the moon, and beyond. Training includes robotics, land and water survival, geology, foreign language, space medicine and physiology, and more, while also conducting simulated spacewalks and flying high-performance jets.

From MIT to astronaut training

Fuhrmann, 35, is from Leesburg, Virginia, and has accumulated more than 2,100 flight hours in 27 aircraft, including the F-16 and F-35. He has served as a U.S. Air Force fighter pilot and experimental test pilot for nearly 14 years and deployed in support of operations Freedom’s Sentinel and Resolute Support, logging 400 combat hours.

Fuhrmann holds a bachelor’s degree in aeronautics and astronautics from MIT and master’s degrees in flight test engineering and systems engineering from the U.S. Air Force Test Pilot School and Purdue University, respectively. While at MIT, he was a member of Air Force ROTC Detachment 365 and was selected as the third-ever student leader of the Bernard M. Gordon-MIT Engineering Leadership Program (GEL) in spring 2011.

“We are tremendously proud of Adam for this notable accomplishment, and we look forward to following his journey through astronaut candidate school and beyond,” says Leo McGonagle, GEL founding and executive director.

“It’s always a thrill to learn that one of our own has joined NASA's illustrious astronaut corps,” says Julie Shah, head of the MIT Department of Aeronautics and Astronautics and the H.N. Slater Professor in Aeronautics and Astronautics. “Adam is Course 16’s 19th astronaut alum. We take very seriously the responsibility to provide the very best aerospace engineering education, and it's so gratifying to see that those fundamentals continue to set individuals from our community on the path to becoming an astronaut.”

Learning to be a leader at MIT

McGonagle recalls that Fuhrmann was a very early participant in GEL from 2009 to 2011.

“The GEL Program was still in its infancy during this time and was in somewhat of a fragile state as we were seeking to grow and cement ourselves as a viable MIT program. As the fall 2010 semester was winding down, it was evident that the program needed an effective GEL2 student leader during the spring semester, who could lead by example and inspire fellow students and who was an example of what right looks like. I knew Adam was already an emerging leader as a senior cadet in MIT’s Air Force ROTC Detachment, so I tapped him for the role of spring student leader of GEL,” said McGonagle.

Fuhrmann initially sought to decline this role, citing his time as a leader in ROTC. But McGonagle, having led the Army ROTC Program prior to GEL, felt that the GEL Student Leader role would challenge and develop Fuhrmann in other ways. In GEL, he would be charged with leading and inspiring students from a broad background of experiences, and focused exclusively on leading within engineering contexts, while engaging with engineering industry organizations.

“GEL needed strong student leadership at this time, so Adam took on the role, and it ended up being a win-win for both him and the program. He later expressed to me that the experience challenged him in ways that he hadn’t anticipated and complemented his Air Force ROTC leadership development. He was grateful for the opportunity, and the program stabilized and grew under Adam’s leadership. He was the right student at the right time and place,” said McGonagle.

Fuhrmann has remained connected to the GEL program. He asked McGonagle to administer his oath of commissioning into the U.S. Air Force, with his family in attendance, at the historic Bunker Hill Monument in Boston. “One of my proudest GEL memories,” said McGonagle, who is a former U.S. Army Lt. Colonel.

Throughout his time in service which included overseas deployments, Fuhrmann has actively participated in Junior Engineering Leader’s Roundtable leadership labs (ELLs) with GEL students, and he has kept in touch with his GEL2 cohort.

“Adam’s GEL2 cohort meets informally once or twice a year, usually via Zoom, to share and discuss professional challenges, lessons learned, life stories, to keep in touch with each other. This small but excellent group of GEL alum is committed to staying connected and supporting one another, as part of the broader GEL community,” said McGonagle.

At the center of nuclear reactors across the United States, a new type of chromium-coated fuel is being used to make the reactors more efficient and more resistant to accidents. The fuel is one of many innovations sprung from collaboration between researchers at MIT and the Idaho National Laboratory (INL) — a relationship that has altered the trajectory of the country’s nuclear industry.

Amid renewed excitement around nuclear energy in America, MIT’s research community is working to further develop next-generation fuels, accelerate the deployment of small modular reactors (SMRs), and enable the first nuclear reactor in space.

Researchers at MIT and INL have worked closely for decades, and the collaboration takes many forms, including joint research efforts, student and postdoc internships, and a standing agreement that lets INL employees spend extended periods on MIT’s campus researching and teaching classes. MIT is also a founding member of the Battelle Energy Alliance, which has managed the Idaho National Laboratory for the Department of Energy since 2005.

The collaboration gives MIT’s community a chance to work on the biggest problems facing America’s nuclear industry while bolstering INL’s research infrastructure.

“The Idaho National Laboratory is the lead lab for nuclear energy technology in the United States today — that’s why it’s essential that MIT works hand in hand with INL,” says Jacopo Buongiorno, the Battelle Energy Alliance Professor in Nuclear Science and Engineering at MIT. “Countless MIT students and postdocs have interned at INL over the years, and a memorandum of understanding that strengthened the collaboration between MIT and INL in 2019 has been extended twice.”

Ian Waitz, MIT’s vice president for research, adds, “The strong collaborative history between MIT and the Idaho National Laboratory enables us to jointly contribute practical technologies to enable the growth of clean, safe nuclear energy. It’s a clear example of how rigorous collaboration across sectors, and among the nation’s top research facilities, can advance U.S. economic prosperity, health, and well-being.”

Research with impact

Much of MIT’s joint research with INL involves tests and simulations of new nuclear materials, fuels, and instrumentation. One of the largest collaborations was part of a global push for more accident-tolerant fuels in the wake of the nuclear accident that followed the 2011 earthquake and tsunami in Fukushima, Japan.

In a series of studies involving INL and members of the nuclear energy industry, MIT researchers helped identify and evaluate alloy materials that could be deployed in the near term to not only bolster safety but also offer higher densities of fuel.

“These new alloys can withstand much more challenging conditions during abnormal occurrences without reacting chemically with steam, which could result in hydrogen explosions during accidents,” explains Buongiorno, who is also the director of science and technology at MIT’s Nuclear Reactor Laboratory and the director of MIT’s Center for Advanced Nuclear Energy Systems. “The fuels can take much more abuse without breaking apart in the reactor, resulting in a higher safety margin.”

The fuels tested at MIT were eventually adopted by power plants across the U.S., starting with the Byron Clean Energy Center in Ogle County, Illinois.

“We’re also developing new materials, fuels, and instrumentation,” Buongiorno says. “People don’t just come to MIT and say, ‘I have this idea, evaluate it for me.’ We collaborate with industry and national labs to develop the new ideas together, and then we put them to the test,  reproducing the environment in which these materials and fuels would operate in commercial power reactors. That capability is quite unique.”

Another major collaboration was led by Koroush Shirvan, MIT’s Atlantic Richfield Career Development Professor in Energy Studies. Shirvan’s team analyzed the costs associated with different reactor designs, eventually developing an open-source tool to help industry leaders evaluate the feasibility of different approaches.

“The reason we’re not building a single nuclear reactor in the U.S. right now is cost and financial risk,” Shirvan says. “The projects have gone over budget by a factor of two and their schedule has lengthened by a factor of 1.5, so we’ve been doing a lot of work assessing the risk drivers. There’s also a lot of different types of reactors proposed, so we’ve looked at their cost potential as well and how those costs change if you can mass manufacture them.”

Other INL-supported research of Shirvan’s involves exploring new manufacturing methods for nuclear fuels and testing materials for use in a nuclear reactor on the surface of the moon.

“You want materials that are lightweight for these nuclear reactors because you have to send them to space, but there isn’t much data around how those light materials perform in nuclear environments,” Shirvan says.

People and progress

Every summer, MIT students at every level travel to Idaho to conduct research in INL labs as interns.

“It’s an example of our students getting access to cutting-edge research facilities,” Shirvan says.

There are also several joint research appointments between the institutions. One such appointment is held by Sacit Cetiner, a distinguished scientist at INL who also currently runs the MIT and INL Joint Center for Reactor Instrumentation and Sensor Physics (CRISP) at MIT’s Nuclear Reactor Laboratory.

CRISP focuses its research on key technology areas in the field of instrumentation and controls, which have long stymied the bottom line of nuclear power generation.

“For the current light-water reactor fleet, operations and maintenance expenditures constitute a sizeable fraction of unit electricity generation cost,” says Cetiner. “In order to make advanced reactors economically competitive, it’s much more reasonable to address anticipated operational issues during the design phase. One such critical technology area is remote and autonomous operations. Working directly with INL, which manages the projects for the design and testing of several advanced reactors under a number of federal programs, gives our students, faculty, and researchers opportunities to make a real impact.”

The sharing of experts helps strengthen MIT and the nation’s nuclear workforce overall.

“MIT has a crucial role to play in advancing the country’s nuclear industry, whether that’s testing and developing new technologies or assessing the economic feasibility of new nuclear designs,” Buongiorno says.

MIT has placed second in U.S. News and World Report’s annual rankings of the nation’s best universities, announced today. 

As in past years, MIT’s engineering program continues to lead the list of undergraduate engineering programs at a doctoral institution. The Institute also placed first in five out of 10 engineering disciplines.

U.S. News placed MIT first in its evaluation of undergraduate computer science programs, ranking it No. 1 in four out of 10 computer science disciplines.

MIT also topped the list of undergraduate business programs, a ranking it shares with the University of Pennsylvania. Among business subfields, MIT is ranked first in two out of 10 specialties.

Within the magazine’s rankings of “academic programs to look for,” MIT topped the list in the category of undergraduate research and creative projects. The Institute also ranks as the second most innovative national university and the fourth best value, according to the U.S. News peer assessment survey of top academics.

MIT placed first in five engineering specialties: aerospace/aeronautical/astronautical engineering; chemical engineering; computer engineering; materials engineering; and mechanical engineering. It placed within the top five in two other engineering areas: biomedical engineering and electrical/electronic/communication engineering.

Other schools in the top five overall for undergraduate engineering programs are Stanford University, the University of California at Berkeley, Georgia Tech, Caltech, the University of Illinois at Urbana-Champaign, and the University of Michigan at Ann Arbor.

In computer science, MIT placed first in four specialties: artificial intelligence (shared with Carnegie Mellon University); biocomputing/bioinformatics/biotechnology; computer systems; and theory. It placed in the top five of six other disciplines: cybersecurity; data analytics/science; game/simulation development (shared with Carnegie Mellon); mobile/web applications; programming languages; and software engineering.

Other schools in the top five overall for undergraduate computer science programs are Carnegie Mellon, Stanford, UC Berkeley, Princeton University, and Georgia Tech.

Among undergraduate business specialties, the MIT Sloan School of Management leads in production/operations management and quantitative analysis. It also placed within the top five in five other categories: analytics; entrepreneurship; finance; management information systems; and supply chain management/logistics.

Other undergraduate business programs ranking in the top five include UC Berkeley, the University of Michigan at Ann Arbor, and New York University.

Recently, U.S. News & World Report ranked medium to large undergraduate economics programs based on a peer assessment survey; MIT’s economics program has placed first in this ranking.

MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants. 

Four additional MIT alumni — Anshula Gandhi ’19, Viktor Kunčak SM ’01, PhD ’07; Gireeja Ranade ’07; and Damiano Testa PhD ’05 — were also honored for separate projects.

The first 29 winning projects will support mathematicians and researchers at universities and organizations working to develop artificial intelligence systems that help advance mathematical discovery and research across several key tasks.

Roe and Sutherland, along with Chris Birkbeck of the University of East Anglia, will use their grant to boost automated theorem proving by building connections between the L-Functions and Modular Forms Database (LMFDB) and the Lean4 mathematics library (mathlib).

“Automated theorem provers are quite technically involved, but their development is under-resourced,” says Sutherland. With AI technologies such as large language models (LLMs), the barrier to entry for these formal tools is dropping rapidly, making formal verification frameworks accessible to working mathematicians. 

Mathlib is a large, community-driven mathematical library for the Lean theorem prover, a formal system that verifies the correctness of every step in a proof. Mathlib currently contains on the order of 105 mathematical results (such as lemmas, propositions, and theorems). The LMFDB, a massive, collaborative online resource that serves as a kind of “encyclopedia” of modern number theory, contains more than 109 concrete statements. Sutherland and Roe are managing editors of the LMFDB.

Roe and Sutherland’s grant will be used for a project that aims to augment both systems, making the LMFDB’s results available within mathlib as assertions that have not yet been formally proved, and providing precise formal definitions of the numerical data stored within the LMFDB. This bridge will benefit both human mathematicians and AI agents, and provide a framework for connecting other mathematical databases to formal theorem-proving systems.

The main obstacles to automating mathematical discovery and proof are the limited amount of formalized math knowledge, the high cost of formalizing complex results, and the gap between what is computationally accessible and what is feasible to formalize.

To address these obstacles, the researchers will use the funding to build tools for accessing the LMFDB from mathlib, making a large database of unformalized mathematical knowledge accessible to a formal proof system. This approach enables proof assistants to identify specific targets for formalization without the need to formalize the entire LMFDB corpus in advance.

“Making a large database of unformalized number-theoretic facts available within mathlib will provide a powerful technique for mathematical discovery, because the set of facts an agent might wish to consider while searching for a theorem or proof is exponentially larger than the set of facts that eventually need to be formalized in actually proving the theorem,” says Roe.

The researchers note that proving new theorems at the frontier of mathematical knowledge often involves steps that rely on a nontrivial computation. For example, Andrew Wiles’ proof of Fermat’s Last Theorem uses what is known as the “3-5 trick” at a crucial point in the proof.

“This trick depends on the fact that the modular curve X_0(15) has only finitely many rational points, and none of those rational points correspond to a semi-stable elliptic curve,” according to Sutherland. “This fact was known well before Wiles’ work, and is easy to verify using computational tools available in modern computer algebra systems, but it is not something one can realistically prove using pencil and paper, nor is it necessarily easy to formalize.”

While formal theorem provers are being connected to computer algebra systems for more efficient verification, tapping into computational outputs in existing mathematical databases offers several other benefits.

Using stored results leverages the thousands of CPU-years of computation time already spent in creating the LMFDB, saving money that would be needed to redo these computations. Having precomputed information available also makes it feasible to search for examples or counterexamples without knowing ahead of time how broad the search can be. In addition, mathematical databases are curated repositories, not simply a random collection of facts. 

“The fact that number theorists emphasized the role of the conductor in databases of elliptic curves has already proved to be crucial to one notable mathematical discovery made using machine learning tools: murmurations,” says Sutherland.

“Our next steps are to build a team, engage with both the LMFDB and mathlib communities, start to formalize the definitions that underpin the elliptic curve, number field, and modular form sections of the LMFDB, and make it possible to run LMFDB searches from within mathlib,” says Roe. “If you are an MIT student interested in getting involved, feel free to reach out!” 

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.

But when it comes to designing materials with exotic quantum properties like superconductivity or unique magnetic states, those models struggle. That’s too bad, because humans could use the help. For example, after a decade of research into a class of materials that could revolutionize quantum computing, called quantum spin liquids, only a dozen material candidates have been identified. The bottleneck means there are fewer materials to serve as the basis for technological breakthroughs.

Now, MIT researchers have developed a technique that lets popular generative materials models create promising quantum materials by following specific design rules. The rules, or constraints, steer models to create materials with unique structures that give rise to quantum properties.

“The models from these large companies generate materials optimized for stability,” says Mingda Li, MIT’s Class of 1947 Career Development Professor. “Our perspective is that’s not usually how materials science advances. We don’t need 10 million new materials to change the world. We just need one really good material.”

The approach is described today in a paper published by Nature Materials. The researchers applied their technique to generate millions of candidate materials consisting of geometric lattice structures associated with quantum properties. From that pool, they synthesized two actual materials with exotic magnetic traits.

“People in the quantum community really care about these geometric constraints, like the Kagome lattices that are two overlapping, upside-down triangles. We created materials with Kagome lattices because those materials can mimic the behavior of rare earth elements, so they are of high technical importance.” Li says.

Li is the senior author of the paper. His MIT co-authors include PhD students Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, and Denisse Cordova Carrizales; postdoc Manasi Mandal; undergraduate researchers Kiran Mak and Bowen Yu; visiting scholar Nguyen Tuan Hung; Xiang Fu ’22, PhD ’24; and professor of electrical engineering and computer science Tommi Jaakkola, who is an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society. Additional co-authors include Yao Wang of Emory University, Weiwei Xie of Michigan State University, YQ Cheng of Oak Ridge National Laboratory, and Robert Cava of Princeton University.

Steering models toward impact

A material’s properties are determined by its structure, and quantum materials are no different. Certain atomic structures are more likely to give rise to exotic quantum properties than others. For instance, square lattices can serve as a platform for high-temperature superconductors, while other shapes known as Kagome and Lieb lattices can support the creation of materials that could be useful for quantum computing.

To help a popular class of generative models known as a diffusion models produce materials that conform to particular geometric patterns, the researchers created SCIGEN (short for Structural Constraint Integration in GENerative model). SCIGEN is a computer code that ensures diffusion models adhere to user-defined constraints at each iterative generation step. With SCIGEN, users can give any generative AI diffusion model geometric structural rules to follow as it generates materials.

AI diffusion models work by sampling from their training dataset to generate structures that reflect the distribution of structures found in the dataset. SCIGEN blocks generations that don’t align with the structural rules.

To test SCIGEN, the researchers applied it to a popular AI materials generation model known as DiffCSP. They had the SCIGEN-equipped model generate materials with unique geometric patterns known as Archimedean lattices, which are collections of 2D lattice tilings of different polygons. Archimedean lattices can lead to a range of quantum phenomena and have been the focus of much research.

“Archimedean lattices give rise to quantum spin liquids and so-called flat bands, which can mimic the properties of rare earths without rare earth elements, so they are extremely important,” says Cheng, a co-corresponding author of the work. “Other Archimedean lattice materials have large pores that could be used for carbon capture and other applications, so it’s a collection of special materials. In some cases, there are no known materials with that lattice, so I think it will be really interesting to find the first material that fits in that lattice.”

The model generated over 10 million material candidates with Archimedean lattices. One million of those materials survived a screening for stability. Using the supercomputers in Oak Ridge National Laboratory, the researchers then took a smaller sample of 26,000 materials and ran detailed simulations to understand how the materials’ underlying atoms behaved. The researchers found magnetism in 41 percent of those structures.

From that subset, the researchers synthesized two previously undiscovered compounds, TiPdBi and TiPbSb, at Xie and Cava’s labs. Subsequent experiments showed the AI model’s predictions largely aligned with the actual material’s properties.

“We wanted to discover new materials that could have a huge potential impact by incorporating these structures that have been known to give rise to quantum properties,” says Okabe, the paper’s first author. “We already know that these materials with specific geometric patterns are interesting, so it’s natural to start with them.”

Accelerating material breakthroughs

Quantum spin liquids could unlock quantum computing by enabling stable, error-resistant qubits that serve as the basis of quantum operations. But no quantum spin liquid materials have been confirmed. Xie and Cava believe SCIGEN could accelerate the search for these materials.

“There’s a big search for quantum computer materials and topological superconductors, and these are all related to the geometric patterns of materials,” Xie says. “But experimental progress has been very, very slow,” Cava adds. “Many of these quantum spin liquid materials are subject to constraints: They have to be in a triangular lattice or a Kagome lattice. If the materials satisfy those constraints, the quantum researchers get excited; it’s a necessary but not sufficient condition. So, by generating many, many materials like that, it immediately gives experimentalists hundreds or thousands more candidates to play with to accelerate quantum computer materials research.”

“This work presents a new tool, leveraging machine learning, that can predict which materials will have specific elements in a desired geometric pattern,” says Drexel University Professor Steve May, who was not involved in the research. “This should speed up the development of previously unexplored materials for applications in next-generation electronic, magnetic, or optical technologies.”

The researchers stress that experimentation is still critical to assess whether AI-generated materials can be synthesized and how their actual properties compare with model predictions. Future work on SCIGEN could incorporate additional design rules into generative models, including chemical and functional constraints.

“People who want to change the world care about material properties more than the stability and structure of materials,” Okabe says. “With our approach, the ratio of stable materials goes down, but it opens the door to generate a whole bunch of promising materials.”

The work was supported, in part, by the U.S. Department of Energy, the National Energy Research Scientific Computing Center, the National Science Foundation, and Oak Ridge National Laboratory.

The Martin Trust Center for MIT Entrepreneurship strives to teach students the craft of entrepreneurship. Over the last few years, no technology has changed that craft more than artificial intelligence.

While many are predicting a rapid and complete transformation in how startups are built, the Trust Center’s leaders have a more nuanced view.

“The fundamentals of entrepreneurship haven’t changed with AI,” says Trust Center Entrepreneur in Residence Macauley Kenney. “There’s been a shift in how entrepreneurs accomplish tasks, and that trickles down into how you build a company, but we’re thinking of AI as another new tool in the toolkit. In some ways the world is moving a lot faster, but we also need to make sure the fundamental principles of entrepreneurship are well-understood.”

That approach was on display during this summer’s delta v startup accelerator program, where many students regularly turned to AI tools but still ultimately relied on talking to their customers to make the right decisions for their business.

Students in this year’s cohort used AI tools to accelerate their coding, draft presentations, learn about new industries, and brainstorm ideas. The Trust Center is encouraging students to use AI as they see fit while also staying mindful of the technology’s limitations.

The Trust Center itself has also embraced AI, most notably through Jetpack, its generative AI app that walks users through the 24 steps of disciplined entrepreneurship outlined in Managing Director Bill Aulet’s book of the same name. When students input a startup idea, the tool can suggest customer segments, early markets to pursue, business models, pricing, and a product plan.

The ways the Trust Center wants students to use Jetpack is apparent in its name: It’s inspired by the acceleration a jetpack provides, but users still need to guide its direction.

Even with AI technology’s current limitations, the Trust Center’s leaders acknowledge it can be a powerful tool for people at any stage of building a business, and their use of AI will continue to evolve with the technology.

“It’s undeniable we’re in the midst of an AI revolution right now,” says Entrepreneur in Residence Ben Soltoff. “AI is reshaping a lot of things we do, and it’s also shaping how we do entrepreneurship and how students build companies. The Trust Center has recognized that for years, and we’ve welcomed AI into how we teach entrepreneurship at all levels, from the earliest stages of idea formation to exploring and testing those ideas and understanding how to commercialize and scale them.”

AI’s strengths and weaknesses

For the past few years, when the Trust Center’s delta v staff get together for strategic retreats, AI has been a central topic. The delta v program’s organizers think about how students can get the most out of the technology each year as they plan their summer-long curriculum.

Everything starts with Orbit, the mobile app designed to help students find entrepreneurial resources, network with peers, access mentorship, and identify events and jobs. Jetpack was added to Orbit last year. It is trained on Aulet’s “Disciplined Entrepreneurship” as well as former Trust Center Executive Director Paul Cheek’s “Startup Tactics” book.

The Trust Center describes Jetpack’s outputs as first drafts designed to help students brainstorm their next steps.

“You need to verify everything when you are using AI to build a business,” says Kenney, who is also a lecturer at MIT Sloan and MIT D-Lab. “I have yet to meet anyone who will base their business on the output of something like ChatGPT without verifying everything first. Sometimes, the verification can take longer than if you had done the research yourself from the beginning.”

One company in this year’s cohort, Mendhai Health, uses AI and telehealth to offer personalized physical therapy for women struggling with pelvic floor dysfunction before and after childbirth.

“AI has definitely made the entrepreneurial process more efficient and faster,” says MBA student Aanchal Arora. “Still, overreliance on AI, at least at this point, can hamper your understanding of customers. You need to be careful with every decision you make.”

Kenney notes the way large language models are built can make them less useful for entrepreneurs.

“Some AI tools can increase your speed by doing things like automatically sorting your email or helping you vibe code apps, but many AI tools are built off averages, and those can be less effective when you’re trying to connect with a very specific demographic,” Kenney says. “It’s not helpful to have AI tell you about an average person, you need to personally have strong validation that your specific customer exists. If you try to build a tool for an average person, you may build a tool for no one at all.”

Students eager to embrace AI may also be overwhelmed by the sheer volume of tools available today. Fortunately, MIT students have a long history of being at the forefront of any new technology, and this year’s delta v cohort featured teams leveraging AI at the core of their solutions and in every step of their entrepreneurial journeys.

MIT Sloan MBA candidate Murtaza Jameel, whose company Cognify uses AI to simulates user interactions with websites and apps to improve digital experiences, describes his firm as an AI-native business.

“We’re building a design intelligence tool that replaces product testing with instant, predictive simulations of user behavior,” Jameel explains. “We’re trying to integrate AI into all of our processes: ideation, go to market, programming. All of our building has been done with AI coding tools. I have a custom bot that I’ve fed tons of information about our company to, and it’s a thought partner I’m speaking to every single day.”

The more things change…

One of the fundamentals the Trust Center doesn’t see changing is the need for students to get out of the lab or the classroom to talk to customers.

“There are ways that AI can unlock new capabilities and make things move faster, but we haven’t turned our curriculum on its head because of AI,” Soltoff says. “In delta v, we stress first and foremost: What are you building and who are you building it for? AI alone can’t tell you who your customer is, what they want, and how you can better serve their needs. You need to go out into the world to make that happen.”

Indeed, many of the biggest hurdles delta v teams faced this summer looked a lot like the hurdles entrepreneurs have always faced.

“We were prepared at the Trust Center to see a big change and to adapt to that, but the companies are still building and encountering the same challenges of customer identification, beachhead market identification, team dynamics,” Kenney says. “Those are still the big meaty challenges they’ve always been working on.”

Amid endless hype about AI agents and the future of work, many founders this summer still said the human side of delta v is what makes the program special.

“I came to MIT with one goal: to start a technology company,” Jameel says. “The delta v program was on my radar when I was applying to MIT. The program gives you incredible access to resources — networks, mentorship, advisors. Some of the top folks in our industry are advising us now on how to build our company. It’s really unique. These are folks who have done what you’re doing 10 or 20 years ago, all just rooting for you. That’s why I came to MIT.”

In recent years, power outages caused by extreme weather or substation attacks have exposed the vulnerability of the electric grid. For the nation’s military bases, which are served by the grid, being ready for outages is a matter of national security. What better way to test readiness than to cut the power?

Lincoln Laboratory is doing just that with its Energy Resilience Readiness Exercises (ERREs). During an exercise, a base is disconnected from the grid, testing the ability of backup power systems and service members to work through failure. Lasting up to 15 hours, each exercise mimics a real outage event with limited forewarning to the base population.

“No one thought that this kind of real-world test would be accepted. We’ve now done it at 33 installations, impacting over 800,000 people,” says Jean Sack ’13, SM ’15, who leads the program with Christopher Lashway and Annie Weathers in the laboratory's Energy Systems Group.

According to a Department of Energy report, 70 percent of the nation’s transmission lines are approaching end of life. This aging infrastructure, combined with increasing power demands and interdependencies, threatens cascading failures. In response, the Department of Defense (DoD) has sharpened its focus on energy resilience, or the ability to anticipate, withstand, and recover from outages. On a base, an outage could disrupt critical missions, open the door to physical or cyberattacks, and cut off water supplies.

“Threats to this already-fragile system are increasing. That's why this work is so important,” Sack says. 

Safely cutting power

Before an exercise, the laboratory team works closely with base leadership and infrastructure personnel to carefully plan how it will safely disconnect from utility power. Over multiple site visits, they study each building and mission to understand power capabilities, ensure health and safety, and develop contingency plans.

“We get people together who may never have spoken before, but depend on one another. We like to say ‘connecting mission owners to their utility providers,’” says Lashway, a former electrician turned energy-systems researcher. “The planning process is a huge learning opportunity, and a chance to fix issues ahead of the outage.”

On the day of the outage, laboratory staff are on site to ensure the process runs smoothly, but the base is meant to run the exercise. Since beginning in 2018, the ERRE campaign has reached huge installations, including Fort Bragg, a U.S. Army base in North Carolina that sees nearly 150,000 people daily, and sites as far away as England and Japan.

The key is to not limit its scope. All facilities and missions, especially those that are critical, should be included, and service members are tasked with working through issues. To make exercises even more useful as an evaluation of readiness, some are modified with scripted scenarios simulating real-world incidents. These scenarios might challenge personnel to handle a cyberattack to control systems, shutdown of a backup power plant, or a rocket launch during an outage.

“We can do all the tabletop exercises in the world, but when you actually pull the plug, the question is, what actually goes on?” former assistant secretary of defense for sustainment Robert McMahon said at a joint House Armed Services subcommittee hearing about initial exercises. “Perhaps the most important lesson that I've seen is a lack of appreciation and understanding by our senior leaders at the installation level, all the way up to my level, of what we thought was going to happen versus what actually occurred, and then being able to apply those lessons learned.”

Illuminating issues

The ERREs have brought to light common issues across bases. One of them is a reliance on fragile or faulty backup systems. For example, electronic equipment experiences a hard shutdown if it isn't supported by a backup battery to bridge power transitions. In some instances, these battery systems failed or unexpectedly depleted due to age or generator issues. “We see a giant comms room drop out, and then phones and computers don’t work. It emphasizes the need for redundancies,” Lashway says.

Generators also present issues. Some fail because they aren’t regularly serviced or refueled through the long outage. Sometimes, personnel mistakenly assumed a generator would support their entire building, requiring reconfigurations after the fact. Air conditioning systems are often excluded from generator-supported emergency circuits, but rooms with a large number of computers generate a lot of heat, and overheated equipment quickly shuts down.

The exercises also unveiled interdependencies and chain reactions. In one case, a fire-suppression system accidentally went off, dousing a hangar in foam. The cause was a pressure drop at the same exact moment a switch reset.

“Executing an operation at this scale stresses how each of these factors need to work harmoniously and efficiently to ensure that the base, and ultimately missions, remain functional,” Lashway says.

Beyond resolving technical issues, the exercises have been valuable for practicing coordination and following chains of command. They’ve also revealed social challenges of operating through outages. For instance, some DoD guidance restricts the use of generators at daycare centers, so parents needed to coordinate care while maintaining their mission. 

After an exercise, the laboratory compiles all findings in a report for the base. It provides time stamps of significant events by building, identifies links between issues, and summarizes common problems site-wide. It then provides recommendations to address vulnerabilities. “Our goal is to provide as much justification as possible for the base to get the resources they need to fix a problem,” Sack says. 

The researchers also want to help bases prevent issues and avoid costly repairs. Recently, they’ve been using power meters to capture electrical data before, during, and after an exercise. These monitoring tools reveal power-quality issues that are otherwise hidden.

“Not all power is created equal, and standards must be followed to ensure equipment, especially specialized military equipment, operates properly and doesn’t get damaged over the long term. Power metering provides a view into that,” says Lashway.

Sparking resiliency ahead

Lincoln Laboratory’s ERRE campaign has resulted in legislation. In 2021, Congress passed a law requiring each military branch to perform at least five ERREs, or "Black Start Exercises," per year through 2027. That law was recently reauthorized until 2032. The team has transitioned the ERRE process to two private companies, as well as within the Air Force and Army, to conduct exercises in the coming years.

“It's very exciting that this got Congress' attention and has scaled across the DoD,” says Nick Judson, who leads the portfolio of energy, water, and natural hazard resilience efforts within the Energy Systems Group. “This idea started out as a way to enable change on DoD installations, and included a lot of difficult conversations about turning the power off to critical missions, and now we're seeing significant improvements to the readiness of bases and their missions.”

It may even be encouraging some healthy competition across the services, Lashway says. At a recent regional event in Colorado, three U.S. Space Force installations each vied to push the scope and duration of their exercises.

The team’s focus is now turning to related analysis, such as water resiliency. Water and wastewater systems are vulnerable to disruptions beyond power outages, including equipment failure, sabotage, or water source depletion.

“We are conducting tabletop exercises and workshops uniting stakeholders around the importance of water and wastewater systems to enable missions,” says Amelia Servi, who leads this work. “So far, we’ve seen great engagement from groups managing water systems who have been seeking funds to fix these aging systems, and from missions who have previously taken water for granted.”

They are also working on long-term energy planning, including ways for installations to be less dependent on the grid. One way is to install microgrids, which are self-sufficient systems that can tap into stored energy. According to Sack, microgrids are highly customized and complicated to operate, so one goal is to design a standardized system. The team's recent power-metering data is providing useful initial inputs into such a design.

The researchers are also considering how this work could improve energy resiliency for civilians. Large-scale exercises might not be feasible for the public, but they could be conducted in areas important to public safety, or in places that rely on military resources. During one exercise in Georgia, city residents partially depended upon a base's power plant, so that exercise included working with the city to ensure its resiliency to the outage.

“Striking that balance of testing readiness without causing harm is a big challenge in this field and a huge motivation for us,” Sack says. “We are encouraged by the outcomes. Our work is impacting the services at the highest level, rewriting infrastructure policy, and making sure people can better sustain operations during grid disruptions.”

When OpenAI introduced ChatGPT to the world in 2022, it brought generative artificial intelligence into the mainstream and started a snowball effect that led to its rapid integration into industry, scientific research, health care, and the everyday lives of people who use the technology.

What comes next for this powerful but imperfect tool?

With that question in mind, hundreds of researchers, business leaders, educators, and students gathered at MIT’s Kresge Auditorium for the inaugural MIT Generative AI Impact Consortium (MGAIC) Symposium on Sept. 17 to share insights and discuss the potential future of generative AI.

“This is a pivotal moment — generative AI is moving fast. It is our job to make sure that, as the technology keeps advancing, our collective wisdom keeps pace,” said MIT Provost Anantha Chandrakasan to kick off this first symposium of the MGAIC, a consortium of industry leaders and MIT researchers launched in February to harness the power of generative AI for the good of society.

Underscoring the critical need for this collaborative effort, MIT President Sally Kornbluth said that the world is counting on faculty, researchers, and business leaders like those in MGAIC to tackle the technological and ethical challenges of generative AI as the technology advances.

“Part of MIT’s responsibility is to keep these advances coming for the world. … How can we manage the magic [of generative AI] so that all of us can confidently rely on it for critical applications in the real world?” Kornbluth said.

To keynote speaker Yann LeCun, chief AI scientist at Meta, the most exciting and significant advances in generative AI will most likely not come from continued improvements or expansions of large language models like Llama, GPT, and Claude. Through training, these enormous generative models learn patterns in huge datasets to produce new outputs.

Instead, LuCun and others are working on the development of “world models” that learn the same way an infant does — by seeing and interacting with the world around them through sensory input.

“A 4-year-old has seen as much data through vision as the largest LLM. … The world model is going to become the key component of future AI systems,” he said.

A robot with this type of world model could learn to complete a new task on its own with no training. LeCun sees world models as the best approach for companies to make robots smart enough to be generally useful in the real world.

But even if future generative AI systems do get smarter and more human-like through the incorporation of world models, LeCun doesn’t worry about robots escaping from human control.

Scientists and engineers will need to design guardrails to keep future AI systems on track, but as a society, we have already been doing this for millennia by designing rules to align human behavior with the common good, he said.

“We are going to have to design these guardrails, but by construction, the system will not be able to escape those guardrails,” LeCun said.

Keynote speaker Tye Brady, chief technologist at Amazon Robotics, also discussed how generative AI could impact the future of robotics.

For instance, Amazon has already incorporated generative AI technology into many of its warehouses to optimize how robots travel and move material to streamline order processing.

He expects many future innovations will focus on the use of generative AI in collaborative robotics by building machines that allow humans to become more efficient.

“GenAI is probably the most impactful technology I have witnessed throughout my whole robotics career,” he said.

Other presenters and panelists discussed the impacts of generative AI in businesses, from largescale enterprises like Coca-Cola and Analog Devices to startups like health care AI company Abridge.

Several MIT faculty members also spoke about their latest research projects, including the use of AI to reduce noise in ecological image data, designing new AI systems that mitigate bias and hallucinations, and enabling LLMs to learn more about the visual world.

After a day spent exploring new generative AI technology and discussing its implications for the future, MGAIC faculty co-lead Vivek Farias, the Patrick J. McGovern Professor at MIT Sloan School of Management, said he hoped attendees left with “a sense of possibility, and urgency to make that possibility real.”

In 2025, six faculty were granted tenure in the MIT School of Humanities, Arts, and Social Sciences.

Sara Brown is an associate professor in the Music and Theater Arts Section. She develops stage designs for theater, opera, and dance by approaching the scenographic space as a catalyst for collective imagination. Her work is rooted in curiosity and interdisciplinary collaboration, and spans virtual environments, immersive performance installations, and evocative stage landscapes. Her recent projects include “Carousel” at the Boston Lyric Opera; the virtual dance performance “The Other Shore” at the Massachusetts Museum of Contemporary Art and Jacob’s Pillow; and “The Lehman Trilogy” at the Huntington Theatre Company. Her upcoming co-directed work, “Circlusion,” takes place within a fully immersive inflatable space and reimagines the female body’s response to power and violence. Her designs have been seen at the BAM Next Wave Festival in New York, the Festival d’Automne in Paris, and the American Repertory Theater in Cambridge.

Naoki Egami is a professor in the Department of Political Science. He is also a faculty affiliate of the MIT Institute for Data, Systems, and Society. Egami specializes in political methodology and develops statistical methods for questions in political science and the social sciences. His current research programs focus on three areas: external validity and generalizability; machine learning and AI for the social sciences; and causal inference with network and spatial data. His work has appeared in various academic journals in political science, statistics, and computer science, such as American Political Science Review, American Journal of Political Science, Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B), NeurIPS, and Science Advances. Before joining MIT, Egami was an assistant professor at Columbia University. He received a PhD from Princeton University (2020) and a BA from the University of Tokyo (2015).

Rachel Fraser is an associate professor in the Department of Linguistics and Philosophy. Before coming to MIT, Fraser taught at the University of Oxford, where she also completed her graduate work in philosophy. She has interests in epistemology, language, feminism, aesthetics, and political philosophy. At present, her main project is a book manuscript on the epistemology of narrative.

Brian Hedden PhD ’12 is a professor in the Department of Linguistics and Philosophy, with a shared appointment in the MIT Schwarzman College of Computing in the Department of Electrical Engineering and Computer Science. His research focuses on how we ought to form beliefs and make decisions. He works in epistemology, decision theory, and ethics, including ethics of AI. He is the author of “Reasons without Persons: Rationality, Identity, and Time” (Oxford University Press, 2015) and articles on topics including collective action problems, legal standards of proof, algorithmic fairness, and political polarization, among others. Prior to joining MIT, he was a faculty member at the Australian National University and the University of Sydney, and a junior research fellow at Oxford. He received his BA From Princeton University in 2006 and his PhD from MIT in 2012.

Viola Schmitt is an associate professor in the Department of Linguistics and Philosophy. She is a linguist with a special interest in semantics. Much of her work focuses on trying to understand general constraints on human language meaning; that is, the principles regulating which meanings can be expressed by human languages and how languages can package meaning. Variants of this question were also central to grants she received from the Austrian and German research foundations. She earned her PhD in linguistics from the University of Vienna and worked as a postdoc and/or lecturer at the Universities of Vienna, Graz, Göttingen, and at the University of California at Los Angeles. Her most recent position was as a junior professor at Humboldt University in Berlin.

Miguel Zenón is an associate professor in the Music and Theater Arts Section. The Puerto Rican alto saxophonist, composer, band leader, music producer, and educator is a Grammy Award winner, the recipient of a Guggenheim Fellowship, a MacArthur Fellowship, and a Doris Duke Artist Award. He also holds an honorary doctorate degree in the arts from Universidad del Sagrado Corazón. Zenón has released 18 albums as a band leader and collaborated with some of the great musicians and ensembles of his time. As a composer, Zenón has been commissioned by Chamber Music America, Logan Center for The Arts, The Hyde Park Jazz Festival, Miller Theater, The Hewlett Foundation, Peak Performances, and many of his peers. Zenón has given hundreds of lectures and master classes at institutions all over the world, and in 2011 he founded Caravana Cultural — a program that presents jazz concerts free of charge in rural areas of Puerto Rico.

Cancer cells have one relentless goal: to grow and divide. While most stick together within the original tumor, some rogue cells break away to traverse to distant organs. There, they can lie dormant — undetectable and not dividing — for years, like landmines waiting to go off.

This migration of cancer cells, called metastasis, is especially common in breast cancer. For many patients, the disease can return months — or even decades — after initial treatment, this time in an entirely different organ.

Robert Weinberg, the Daniel K. Ludwig Professor for Cancer Research at MIT and a Whitehead Institute for Biomedical Research founding member, has spent decades unraveling the complex biology of metastasis and pursuing research that could improve survival rates among patients with metastatic breast cancer — or prevent metastasis altogether.

In his latest study, Weinberg, postdoc Jingwei Zhang, and colleagues ask a critical question: What causes these dormant cancer cells to erupt into a frenzy of growth and division? The group’s findings, published Sept. 1 in The Proceedings of the National Academy of Sciences (PNAS), point to a unique culprit.

This awakening of dormant cancer cells, they’ve discovered, isn’t a spontaneous process. Instead, the wake-up call comes from the inflamed tissue surrounding the cells. One trigger for this inflammation is bleomycin, a common chemotherapy drug that can scar and thicken lung tissue.

“The inflammation jolts the dormant cancer cells awake,” Weinberg says. “Once awakened, they start multiplying again, seeding new life-threatening tumors in the body.”

Decoding metastasis

There’s a lot that scientists still don’t know about metastasis, but this much is clear: Cancer cells must undergo a long and arduous journey to achieve it. The first step is to break away from their neighbors within the original tumor.

Normally, cells stick to one another using surface proteins that act as molecular “velcro,” but some cancer cells can acquire genetic changes that disrupt the production of these proteins and make them more mobile and invasive, allowing them to detach from the parent tumor. 

Once detached, they can penetrate blood vessels and lymphatic channels, which act as highways to distant organs.

While most cancer cells die at some point during this journey, a few persist. These cells exit the bloodstream and invade different tissues—lungs, liver, bone, and even the brain — to give birth to new, often more-aggressive tumors.

“Almost 90 percent of cancer-related deaths occur not from the original tumor, but when cancer cells spread to other parts of the body,” says Weinberg. “This is why it’s so important to understand how these ‘sleeping’ cancer cells can wake up and start growing again.”

Setting up shop in new tissue comes with changes in surroundings — the “tumor microenvironment” — to which the cancer cells may not be well-suited. These cells face constant threats, including detection and attack by the immune system. 

To survive, they often enter a protective state of dormancy that puts a pause on growth and division. This dormant state also makes them resistant to conventional cancer treatments, which often target rapidly dividing cells.

To investigate what makes this dormancy reversible months or years down the line, researchers in the Weinberg Lab injected human breast cancer cells into mice. These cancer cells were modified to produce a fluorescent protein, allowing the scientists to track their behavior in the body.

The group then focused on cancer cells that had lodged themselves in the lung tissue. By examining them for specific proteins — Ki67, ITGB4, and p63 — that act as markers of cell activity and state, the researchers were able to confirm that these cells were in a non-dividing, dormant state.

Previous work from the Weinberg Lab had shown that inflammation in organ tissue can provoke dormant breast cancer cells to start growing again. In this study, the team tested bleomycin — a chemotherapy drug known to cause lung inflammation — that can be given to patients after surgery to lower the risk of cancer recurrence.

The researchers found that lung inflammation from bleomycin was sufficient to trigger the growth of large lung cancer colonies in treated mice — and to shift the character of these once-dormant cells to those that are more invasive and mobile.

Zeroing in on the tumor microenvironment, the team identified a type of immune cells, called M2 macrophages, as drivers of this process. These macrophages release molecules called epidermal growth factor receptor (EGFR) ligands, which bind to receptors on the surface of dormant cancer cells. This activates a cascade of signals that provoke dormant cancer cells to start multiplying rapidly. 

But EGFR signaling is only the initial spark that ignites the fire. “We found that once dormant cancer cells are awakened, they retain what we call an ‘awakening memory,’” Zhang says. “They no longer require ongoing inflammatory signals from the microenvironment to stay active [growing and multiplying] — they remember the awakened state.”

While signals related to inflammation are necessary to awaken dormant cancer cells, exactly how much signaling is needed remains unclear. “This aspect of cancer biology is particularly challenging, because multiple signals contribute to the state change in these dormant cells,” Zhang says.

The team has already identified one key player in the awakening process, but understanding the full set of signals and how each contributes is far more complex — a question they are continuing to investigate in their new work. 

Studying these pivotal changes in the lives of cancer cells — such as their transition from dormancy to active growth — will deepen our scientific understanding of metastasis and, as researchers in the Weinberg Lab hope, lead to more effective treatments for patients with metastatic cancers.

Nearly 300 people gathered Tuesday to mark the ceremonial groundbreaking for Biogen’s new state-of-the-art facility in Kendall Square. The project is the first building to be constructed at MIT’s Kendall Common on the former Volpe federal site, and will serve as a consolidated headquarters for the pioneering biotechnology company which has called Cambridge home for more than 40 years.

In marking the start of construction, Massachusetts Governor Maura Healey addressed the enthusiastic crowd, saying, “Massachusetts science saves lives — saves lives here, saves lives around the world. We celebrate that in Biogen today, we celebrate that in Kendall Common, and we celebrate that in this incredible ecosystem that extends all across our great state. Today, Biogen is not just building a new facility, they are building the future of medicine and innovation.”

Emceed by Kirk Taylor, president and CEO of the Massachusetts Life Sciences Center, the event featured a specially created Lego model of the new building and a historic timeline of Biogen’s origin story overlaid on Kendall Square’s transformation. The program’s theme — “Making breakthroughs happen in Kendall Square” — seemed to elicit a palpable sense of pride among the Biogen and MIT employees, business leaders, and public officials in attendance.

MIT President Sally Kornbluth reflected on the vibrancy of the local innovation ecosystem: “I sometimes say that Kendall Square’s motto might as well be ‘talent in proximity.’ By following that essential recipe, Biogen’s latest decision to intensify its presence here promises great things for the whole region.” Kornbluth described Biogen’s move as “a very important signal to the world right now.”

Biogen’s March 2025 announcement that it will centralize operations at 75 Broadway was lauded as a show of strength for the historic company and the life sciences sector. The 580,000-square-foot research and development headquarters, designed by Elkus Manfredi Architects, will optimize Biogen’s scientific discovery and clinical processes. The new facility is scheduled to open in 2028.

CEO Chris Veihbacher shared his thoughts on Biogen’s decision: “I am proud to stand here with so many individuals who have shaped our past and who are dedicated to our future in Kendall Square. … We decided to invest in the next chapter of Kendall Square because of what this community represents: talent, energy, ingenuity, and collaboration.” Biogen was founded in 1978 by Nobel laureates Phillip Sharp (an MIT Institute Professor and professor of biology emeritus) and Wally Gilbert, both of whom were not only present, but received an impromptu standing ovation, led by Viehbacher.

Kendall Common is being developed by MIT’s Investment Management Company (MITIMCo) and will ultimately include four commercial buildings, four residential buildings (including affordable housing), open space, retail, entertainment, and a community center. MITIMCo’s joint venture partner for the Biogen project is BioMed Realty, a Blackstone Real Estate portfolio company.

Senior Vice President Patrick Rowe, who oversees MITIMCo’s real estate group, says, “Biogen is such a critical anchor for the area. I’m excited for the impact that this project will have on Kendall Square, and for the way that the Kendall Common development can help to further advance our innovation ecosystem.”

The last gasp of a primordial black hole may be the source of the highest-energy “ghost particle” detected to date, a new MIT study proposes.

In a paper appearing today in Physical Review Letters, MIT physicists put forth a strong theoretical case that a recently observed, highly energetic neutrino may have been the product of a primordial black hole exploding outside our solar system.

Neutrinos are sometimes referred to as ghost particles, for their invisible yet pervasive nature: They are the most abundant particle type in the universe, yet they leave barely a trace. Scientists recently identified signs of a neutrino with the highest energy ever recorded, but the source of such an unusually powerful particle has yet to be confirmed.

The MIT researchers propose that the mysterious neutrino may have come from the inevitable explosion of a primordial black hole. Primordial black holes (PBHs) are hypothetical black holes that are microscopic versions of the much more massive black holes that lie at the center of most galaxies. PBHs are theorized to have formed in the first moments following the Big Bang. Some scientists believe that primordial black holes could constitute most or all of the dark matter in the universe today.

Like their more massive counterparts, PBHs should leak energy and shrink over their lifetimes, in a process known as Hawking radiation, which was predicted by the physicist Stephen Hawking. The more a black hole radiates, the hotter it gets and the more high-energy particles it releases. This is a runaway process that should produce an incredibly violent explosion of the most energetic particles just before a black hole evaporates away.

The MIT physicists calculate that, if PBHs make up most of the dark matter in the universe, then a small subpopulation of them would be undergoing their final explosions today throughout the Milky Way galaxy. And, there should be a statistically significant possibility that such an explosion could have occurred relatively close to our solar system. The explosion would have released a burst of high-energy particles, including neutrinos, one of which could have had a good chance of hitting a detector on Earth.

If such a scenario had indeed occurred, the recent detection of the highest-energy neutrino would represent the first observation of Hawking radiation, which has long been assumed, but has never been directly observed from any black hole. What’s more, the event might indicate that primordial black holes exist and that they make up most of dark matter — a mysterious substance that comprises 85 percent of the total matter in the universe, the nature of which remains unknown.

“It turns out there’s this scenario where everything seems to line up, and not only can we show that most of the dark matter [in this scenario] is made of primordial black holes, but we can also produce these high-energy neutrinos from a fluke nearby PBH explosion,” says study lead author Alexandra Klipfel, a graduate student in MIT’s Department of Physics. “It’s something we can now try to look for and confirm with various experiments.”

The study’s other co-author is David Kaiser, professor of physics and the Germeshausen Professor of the History of Science at MIT.

High-energy tension

In February, scientists at the Cubic Kilometer Neutrino Telescope, or KM3NeT, reported the detection of the highest-energy neutrino recorded to date. KM3NeT is a large-scale underwater neutrino detector located at the bottom of the Mediterranean Sea, where the environment is meant to mute the effects of any particles other than neutrinos.

The scientists operating the detector picked up signatures of a passing neutrino with an energy of over 100 peta-electron-volts. One peta-electron volt is equivalent to the energy of 1 quadrillion electron volts.

“This is an incredibly high energy, far beyond anything humans are capable of accelerating particles up to,” Klipfel says. “There’s not much consensus on the origin of such high-energy particles.”

Similarly high-energy neutrinos, though not as high as what KM3NeT observed, have been detected by the IceCube Observatory — a neutrino detector embedded deep in the ice at the South Pole. IceCube has detected about half a dozen such neutrinos, whose unusually high energies have also eluded explanation. Whatever their source, the IceCube observations enable scientists to work out a plausible rate at which neutrinos of those energies typically hit Earth. If this rate were correct, however, it would be extremely unlikely to have seen the ultra-high-energy neutrino that KM3NeT recently detected. The two detectors’ discoveries, then, seemed to be what scientists call “in tension.”

Kaiser and Klipfel, who had been working on a separate project involving primordial black holes, wondered: Could a PBH have produced both the KM3NeT neutrino and the handful of IceCube neutrinos, under conditions in which PBHs comprise most of the dark matter in the galaxy? If they could show a chance existed, it would raise an even more exciting possibility — that both observatories observed not only high-energy neutrinos but also the remnants of Hawking radiation.

“Our best chance”

The first step the scientists took in their theoretical analysis was to calculate how many particles would be emitted by an exploding black hole. All black holes should slowly radiate over time. The larger a black hole, the colder it is, and the lower-energy particles it emits as it slowly evaporates. Thus, any particles that are emitted as Hawking radiation from heavy stellar-mass black holes would be near impossible to detect. By the same token, however, much smaller primordial black holes would be very hot and emit high-energy particles in a process that accelerates the closer the black hole gets to disappearing entirely.

“We don’t have any hope of detecting Hawking radiation from astrophysical black holes,” Klipfel says. “So if we ever want to see it, the smallest primordial black holes are our best chance.”

The researchers calculated the number and energies of particles that a black hole should emit, given its temperature and shrinking mass. In its final nanosecond, they estimate that once a black hole is smaller than an atom, it should emit a final burst of particles, including about 1020 neutrinos, or about a sextillion of the particles, with energies of about 100 peta-electron-volts (around the energy that KM3NeT observed).

They used this result to calculate the number of PBH explosions that would have to occur in a galaxy in order to explain the reported IceCube results. They found that, in our region of the Milky Way galaxy, about 1,000 primordial black holes should be exploding per cubic parsec per year. (A parsec is a unit of distance equal to about 3 light years, which is more than 10 trillion kilometers.)

They then calculated the distance at which one such explosion in the Milky Way could have occurred, such that just a handful of the high-energy neutrinos could have reached Earth and produced the recent KM3NeT event. They find that a PBH would have to explode relatively close to our solar system — at a distance about 2,000 times further than the distance between the Earth and our sun.

The particles emitted from such a nearby explosion would radiate in all directions. However, the team found there is a small, 8 percent chance that an explosion can happen close enough to the solar system, once every 14 years, such that enough ultra-high-energy neutrinos hit the Earth.

“An 8 percent chance is not terribly high, but it’s well within the range for which we should take such chances seriously — all the more so because so far, no other explanation has been found that can account for both the unexplained very-high-energy neutrinos and the even more surprising ultra-high-energy neutrino event,” Kaiser says.

The team’s scenario seems to hold up, at least in theory. To confirm their idea will require many more detections of particles, including neutrinos at “insanely high energies.” Then, scientists can build up better statistics regarding such rare events.

“In that case, we could use all of our combined experience and instrumentation, to try to measure still-hypothetical Hawking radiation,” Kaiser says. “That would provide the first-of-its-kind evidence for one of the pillars of our understanding of black holes — and could account for these otherwise anomalous high-energy neutrino events as well. That’s a very exciting prospect!”

In tandem, other efforts to detect nearby PBHs could further bolster the hypothesis that these unusual objects make up most or all of the dark matter.

This work was supported, in part, by the National Science Foundation, MIT’s Center for Theoretical Physics – A Leinweber Institute, and the U.S. Department of Energy.