The composition of bacterial populations living on our faces plays a significant role in the development of acne and other skin conditions such as eczema. Two species of bacteria predominate in most people, but how they interact with each other, and how those interactions may contribute to disease, has been difficult to study.

MIT researchers have now revealed the dynamics of those interactions in more detail than previously possible, shedding light on when and how new bacterial strains emerge on the skin of the face. Their findings could help guide the development of new treatments for acne and other conditions, and may also help to optimize the timing of such treatments.

The researchers found that many new strains of Cutibacterium acnes, a species believed to contribute to the development of acne, are acquired during the early teenage years. But after that, the makeup of these populations becomes very stable and doesn’t change much even when exposed to new strains.

That suggests that this transitional stage could be the best window for introducing probiotic strains of C. acnes, says Tami Lieberman, an associate professor of civil and environmental engineering, a member of MIT’s Institute for Medical Engineering and Science, and the senior author of the study.

“We found that there are some surprising dynamics, and these dynamics provide insights for how to design probiotic therapy,” Lieberman says. “If we had a strain that we knew could prevent acne, these results would suggest we should make sure we apply them early during the transition to adulthood, to really get them to engraft.”

Jacob Baker PhD ’24, who is now the chief scientific officer at Taxa Technologies, is the lead author of the paper, which appears today in Cell Host and Microbe. Other authors include MIT graduate student Evan Qu, MIT postdoc Christopher Mancuso, Harvard University graduate student A. Delphine Tripp, and former MIT postdoc Arolyn Conwill PhD ’18.

Microbial dynamics

Although C. acnes has been implicated in the development of acne, it is still unclear exactly why acne develops in some people but not others — it may be that some strains are more likely to cause skin inflammation, or there may be differences in how the host immune system responds to the bacteria, Lieberman says. There are probiotic strains of C. acnes now available, which are thought to help prevent acne, but the benefits of these strains have not been proven.

Along with C. acnes, the other predominant bacterium found on the face is Staphylococcus epidermidis. Together, these two strains make up about 80 percent of the strains in the adult facial skin microbiome. Both of these species exist in different strains, or lineages, that vary by a small number of genetic mutations. However, until now, researchers had not been able to accurately measure this diversity or track how it changes over time.

Learning more about those dynamics could help researchers answer key questions that could help them develop new probiotic treatments for acne: How easy is it for new lineages to establish themselves on the skin, and what is the best time to introduce them?

To study these population shifts, the researchers had to measure how individual cells evolve over time. To do that, they began by obtaining microbiome samples from 30 children at a Boston-area school and from 27 of their parents. Studying members of the same family enabled the researchers to analyze the likelihood of different lineages being transferred between people in close contact.

For about half of the individuals, the researchers were able to take samples at multiple time points, and for the rest, only once. For each sample, they isolated individual cells and grew them into colonies, then sequenced their genomes.

This allowed the researchers to learn how many lineages were found on each person, how they changed over time, and how different cells from the same lineage were. From that information, the researchers could infer what had happened to those lineages in the recent past and how long they had been present on the individual.

Overall, the researchers identified a total of 89 C. acnes lineages and 78 S. epidermidis lineages, with up to 11 of each found in each person’s microbiome. Previous work had suggested that in each person’s facial skin microbiome, lineages of these two skin bacteria remain stable over long periods of time, but the MIT team found that these populations are actually more dynamic than previously thought.

“We wanted to know if these communities were truly stable, and if there could be times where they weren’t stable. In particular, if the transition to an adult skin like microbiome would have a higher rate of acquisition of new lineages,” Lieberman says.

During the early teens, an increase in hormone production results in increased oil on the skin, which is a good food source for bacteria. It has previously been shown that during this time, the density of bacteria on the skin of the face increases by about 10,000-fold. In this study, the researchers found that while the composition of C. acnes populations tended to remain very stable over time, the early teenage years present an opportunity for many more lineages of C. acnes to appear.

“For C. acnes, what we were able to show was that people do get strains throughout life, but very rarely,” Lieberman says. “We see the highest rate of influx when teenagers are transitioning to a more adult-like skin microbiome.”

The findings suggest that for topical probiotic treatments for acne, the best time to apply them is during the early teenage years, when there could be more opportunity for probiotic strains to become established.

Population turnover

Later in adulthood, there is a little bit of sharing of C. acnes strains between parents living in the same household, but the rate of turnover in any individual person’s microbiome is still very low, Lieberman says.

The researchers found that S. epidermidis has a much higher turnover rate than C. acnes — each S. epidermidis strain lives on the face for an average of less than two years. However, there was not very much overlap in the S. epidermidis lineages shared by members of the same household, suggesting that transfer of strains between people is not causing the high turnover rate.

“That suggests that something is preventing homogenization between people,” Lieberman says. “It could be host genetics or host behavior, or people using different topicals or different moisturizers, or it could be active restriction of new migrants from the bacteria that are already there at that moment.”

Now that they’ve shown that new C. acnes strains can be acquired during the early teenage years, the researchers hope to study whether the timing of this acquisition affects how the immune system responds to them. They also hope to learn more about how people maintain such different microbiome populations even when exposed to new lineages through close contact with family members.

“We want to understand why we each have unique strain communities despite the fact that there is this constant accessibility and high turnover, specifically for S. epidermidis,” Lieberman says. “What’s driving this constant turnover in S. epidermidis, and what are the implications of these new colonizations for acne during adolescence?”

The research was funded by the MIT Center for Microbiome Informatics and Therapeutics, a Smith Family Foundation Award for Excellence in Biomedical Research, and the National Institutes of Health.

The ambiguity in medical imaging can present major challenges for clinicians who are trying to identify disease. For instance, in a chest X-ray, pleural effusion, an abnormal buildup of fluid in the lungs, can look very much like pulmonary infiltrates, which are accumulations of pus or blood.

An artificial intelligence model could assist the clinician in X-ray analysis by helping to identify subtle details and boosting the efficiency of the diagnosis process. But because so many possible conditions could be present in one image, the clinician would likely want to consider a set of possibilities, rather than only having one AI prediction to evaluate.

One promising way to produce a set of possibilities, called conformal classification, is convenient because it can be readily implemented on top of an existing machine-learning model. However, it can produce sets that are impractically large. 

MIT researchers have now developed a simple and effective improvement that can reduce the size of prediction sets by up to 30 percent while also making predictions more reliable.

Having a smaller prediction set may help a clinician zero in on the right diagnosis more efficiently, which could improve and streamline treatment for patients. This method could be useful across a range of classification tasks — say, for identifying the species of an animal in an image from a wildlife park — as it provides a smaller but more accurate set of options.

“With fewer classes to consider, the sets of predictions are naturally more informative in that you are choosing between fewer options. In a sense, you are not really sacrificing anything in terms of accuracy for something that is more informative,” says Divya Shanmugam PhD ’24, a postdoc at Cornell Tech who conducted this research while she was an MIT graduate student.

Shanmugam is joined on the paper by Helen Lu ’24; Swami Sankaranarayanan, a former MIT postdoc who is now a research scientist at Lilia Biosciences; and senior author John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the Conference on Computer Vision and Pattern Recognition in June.

Prediction guarantees

AI assistants deployed for high-stakes tasks, like classifying diseases in medical images, are typically designed to produce a probability score along with each prediction so a user can gauge the model’s confidence. For instance, a model might predict that there is a 20 percent chance an image corresponds to a particular diagnosis, like pleurisy.

But it is difficult to trust a model’s predicted confidence because much prior research has shown that these probabilities can be inaccurate. With conformal classification, the model’s prediction is replaced by a set of the most probable diagnoses along with a guarantee that the correct diagnosis is somewhere in the set.

But the inherent uncertainty in AI predictions often causes the model to output sets that are far too large to be useful.

For instance, if a model is classifying an animal in an image as one of 10,000 potential species, it might output a set of 200 predictions so it can offer a strong guarantee.

“That is quite a few classes for someone to sift through to figure out what the right class is,” Shanmugam says.

The technique can also be unreliable because tiny changes to inputs, like slightly rotating an image, can yield entirely different sets of predictions.

To make conformal classification more useful, the researchers applied a technique developed to improve the accuracy of computer vision models called test-time augmentation (TTA).

TTA creates multiple augmentations of a single image in a dataset, perhaps by cropping the image, flipping it, zooming in, etc. Then it applies a computer vision model to each version of the same image and aggregates its predictions.

“In this way, you get multiple predictions from a single example. Aggregating predictions in this way improves predictions in terms of accuracy and robustness,” Shanmugam explains.

Maximizing accuracy

To apply TTA, the researchers hold out some labeled image data used for the conformal classification process. They learn to aggregate the augmentations on these held-out data, automatically augmenting the images in a way that maximizes the accuracy of the underlying model’s predictions.

Then they run conformal classification on the model’s new, TTA-transformed predictions. The conformal classifier outputs a smaller set of probable predictions for the same confidence guarantee.

“Combining test-time augmentation with conformal prediction is simple to implement, effective in practice, and requires no model retraining,” Shanmugam says.

Compared to prior work in conformal prediction across several standard image classification benchmarks, their TTA-augmented method reduced prediction set sizes across experiments, from 10 to 30 percent.

Importantly, the technique achieves this reduction in prediction set size while maintaining the probability guarantee.

The researchers also found that, even though they are sacrificing some labeled data that would normally be used for the conformal classification procedure, TTA boosts accuracy enough to outweigh the cost of losing those data.

“It raises interesting questions about how we used labeled data after model training. The allocation of labeled data between different post-training steps is an important direction for future work,” Shanmugam says.

In the future, the researchers want to validate the effectiveness of such an approach in the context of models that classify text instead of images. To further improve the work, the researchers are also considering ways to reduce the amount of computation required for TTA.

This research is funded, in part, by the Wistrom Corporation.

For policymakers investigating the effective transition of an economy from agriculture to manufacturing and services, there are complex economic, institutional, and practical considerations. “Are certain regions trapped in an under-industrialization state?” asks Tishara Garg, an economics doctoral student at MIT. “If so, can government policy help them escape this trap and transition to an economy characterized by higher levels of industrialization and better-paying jobs?” 

Garg’s research focuses on trade, economic geography, and development. Her studies yielded the paper “Can Industrial Policy Overcome Coordination Failures: Theory and Evidence from Industrial Zones,” which investigates whether economic policy can shift an economy from an undesirable state to a desirable state. 

Garg’s work combines tools from industrial organization and numerical algebraic geometry. Her paper finds that regions in India with state-developed industrial zones are 38 percent more likely to shift from a low to high industrialization state over a 15-year period than those without such zones.  

The kinds of questions uncovered during her studies aren’t easily answered using standard technical and econometric tools, so she’s developing new ones. “One of my study’s main contributions is a methodological framework that draws on ideas from different areas,” she notes. “These tools not only help me study the question I want to answer, but are also general enough to help study a broader set of questions around multiple challenges.”

The new tools she’s developed, along with a willingness to engage with other disciplines, have helped her discover innovative ways to approach these challenges while learning to work with new ones, options she asserts are actively encouraged at an institution like MIT.

“I benefited from having an open mind and learning different things,” she says.

“I was introduced to academia late”

Garg’s journey from Kaithal, India, to MIT wasn’t especially smooth, as societal pressures exerted a powerful influence. “The traditional path for someone like me is to finish school, enter an arranged marriage, and start a family,” she says. “But I was good at school and wanted to do more.” 

Garg, who hails from a background with limited access to information on career development opportunities, took to math early. “I chose business in high school because I planned to become an accountant,” she recalls. “My uncle was an accountant.”

While pursuing the successful completion of a high school business track, she became interested in economics. “I didn’t know much about economics, but I came to enjoy it,” she says. Garg relishes the pursuit of deductive reasoning that begins with a set of assumptions and builds, step by step, toward a well-defined, clear conclusion. She especially enjoys grappling with the arguments she found in textbooks. She continued to study economics as an undergraduate at the University of Delhi, and later earned her master’s from the Indian Statistical Institute. Doctoral study wasn’t an option until she made it one.

“It took me some time to convince my parents,” she says. She spent a year at a hedge fund before applying to economics doctoral programs in the United States and choosing MIT. “I was introduced to academia late,” she notes. “But my heart was being drawn to the academic path.”

Answering ambitious and important questions

Garg, who hadn’t left India before her arrival in Cambridge, Massachusetts, found the transition challenging. “There were new cultural norms, a language barrier, different foods, and no preexisting social network,” she says. Garg relied on friends and MIT faculty for support when she arrived in 2019. 

“When Covid hit, the department looked out for me,” she says. Garg recalls regular check-ins from a faculty advisor and the kind of camaraderie that can grow from shared circumstances, like Covid-related sheltering protocols. A world that forced her to successfully navigate a new and unfamiliar reality helped reshape how she viewed herself. “Support from the community at MIT helped me grow in many ways,” she recalls, “I found my voice here.”

Once she began her studies, one of the major differences Garg found was the diversity of opinions in her field of inquiry. “At MIT, I could speak with students and faculty specializing in trade, development economics, industrial organization, macroeconomics, and more,” she says. “I had limited exposure to many of these subfields before coming to MIT.” 

She quickly found her footing, leaning heavily on both her past successes and the academic habits she developed during her studies in India. “I’m not a passive learner,” she says. “My style is active, critical, and engaged.”

Conducting her research exposes Garg to new ideas. She learned the value of exploring other disciplines’ approaches to problem-solving, which was encouraged and enabled at MIT. 

One of the classes she came to enjoy most was a course in industrial organization taught by Tobias Salz. “I had little familiarity with the material, and it was highly technical — but he taught it in such a clear and intuitive way that I found myself truly enjoying the class, even though it was held during the pandemic,” she recalls. That early experience laid the groundwork for future research. Salz went on to advise her dissertation, helping her engage with work she would build upon.

“Answering ambitious and important questions is what draws me to the work,” Garg says. “I enjoy learning, I enjoy the creative process of bringing different ideas together and MIT's environment has made it easy for me to pick up new things.”

Working with her advisors at MIT helped Garg formalize her research and appreciate the value of uncovering questions and developing approaches to answer them. Professor Abhijit Banerjee, an advisor and Nobel laureate, helped her understand the importance of appreciating different traditions while also staying true to how you think about the problem, she recalls. “That gave me the confidence to pursue the questions in ways that felt most compelling and personal to me,” she says, “even if they didn’t fit neatly into disciplinary boundaries.”

This encouragement, combined with the breadth of perspectives at MIT, pushed her to think creatively about research challenges and to look beyond traditional tools to discover solutions. “MIT’s faculty have helped me improve the way I think and refine my approach to this work,” she says.

Paying it forward

Garg, who will continue her research as a postdoc at Princeton University in the fall and begin her career as a professor at Stanford University in 2026, singles out her network of friends and advisors for special praise.

“From regular check-ins with my advisors to the relationships that help me find balance with my studies, the people at MIT have been invaluable,” she says. 

Garg is especially invested in mentorship opportunities available as a researcher and professor. “I benefited from the network of friends and mentors at MIT and I want to pay it forward — especially for women, and others from backgrounds like mine,” she says.

She cites the work of her advisors, David Atkin and Dave Donaldson — with whom she is also collaborating on research studying incidences of economic distortions — as both major influences on her development and a key reason she’s committed to mentoring others. “They’ve been with me every step of the way,” she says. 

Garg recommends keeping an open mind, above all. “Some of my students didn’t come from a math-heavy background and would restrict themselves or otherwise get discouraged from pursuing theoretical work,” she says. “But I always encouraged them to pursue their interests above all, even if it scared them.” 

The variety of ideas available in her area of inquiry still fascinates Garg, who’s excited about what’s next. “Don’t shy from big questions,” she says. “Explore the big idea.”

With war continuing to disrupt education for millions of Ukrainian high school and college students, many are turning to online resources, including MIT OpenCourseWare, a part of MIT Open Learning offering educational materials from more than 2,500 MIT undergraduate and graduate courses.

For Ukrainian high school senior Sofiia Lipkevych and other students, MIT OpenCourseWare has provided valuable opportunities to take courses in key subject areas. However, while multiple Ukrainian students study English, many do not yet have sufficient command of the language to be able to fully understand and use the often very technical and complex OpenCourseWare content and materials.

“At my school, I saw firsthand how language barriers prevented many Ukrainian students from accessing world-class education,” says Lipkevych.

She was able to address this challenge as a participant in the Ukrainian Leadership and Technology Academy (ULTA), established by Ukrainian MIT students Dima Yanovsky and Andrii Zahorodnii. During summer 2024 at ULTA, Lipkevych worked on a browser extension that translated YouTube videos in real-time. Since MIT OpenCourseWare was a main source of learning materials for students participating in ULTA, she was inspired to translate OpenCourseWare lectures directly and to have this translation widely available on the OpenCourseWare website and YouTube channel. She reached out to Professor Elizabeth Wood, founding director of the MIT Ukraine Program, who connected her with MIT OpenCourseWare Director Curt Newton.

Although there had been some translations of MIT OpenCourseWare’s educational resources available beginning in 2004, these initial translations were conducted manually by several global partners, without the efficiencies of the latest artificial intelligence tools, and over time the programs couldn’t be sustained, and shut down.

“We were thrilled to have this contact with ULTA,” says Newton. “We’ve been missing having a vibrant translation community, and we are excited to have a ‘phase 2’ of translations emerge.”

The ULTA team selected courses to translate based on demand among Ukrainian students, focusing on foundational subjects that are prerequisites for advanced learning — particularly those for which high-quality, Ukrainian-language materials are scarce. Starting with caption translations on videos of lectures, the team has translated the following courses so far: 18.06 (Linear Algebra), 2.003SC (Engineering Dynamics), 5.60 (Thermodynamics & Kinetics), 6.006 (Introduction to Algorithms), and 6.0001 (Introduction to Computer Science and Programming in Python). They also worked directly with Andy Eskenazi, a PhD student in the MIT Department of Aeronautics and Astronautics, to translate 16.002 (How to CAD Almost Anything - Siemens NX Edition).

The ULTA team developed multiple tools to help break language barriers. For MIT OpenCourseWare’s PDF content available through the ULTA program, they created a specialized tool that uses optical character recognition to recognize LaTeX in documents — such as problem sets and other materials — and then used a few large language models to translate them, all while maintaining technical accuracy. The team built a glossary of technical terms used in the courses and their corresponding Ukrainian translations, to help make sure that the wording was correct and consistent. Each translation also undergoes human review to further ensure accuracy and high quality.

For video content, the team initially created a browser extension that can translate YouTube video captions in real-time. They ultimately collaborated with ElevenLabs, implementing their advanced AI dubbing editor that preserves the original speaker's tone, pace, and emotional delivery. The lectures are translated in the ElevenLabs dubbing editor, and then the audio is uploaded to the MIT OpenCourseWare YouTube channel.

The team is currently finalizing the translation of the audio for class 9.13 (The Human Brain), taught by MIT Professor Nancy Kanwisher, which Lipkevych says they selected for its interdisciplinary nature and appeal to a wide variety of learners.

This Ukrainian translation project highlights the transformative potential of the latest translation technologies, building upon a 2023 MIT OpenCourseWare experiment using the Google Aloud AI dubbing prototype on a few courses, including MIT Professor Patrick Winston’s How to Speak. The advanced capabilities of the dubbing editor used in this project are opening up possibilities for a much greater variety of language offerings throughout MIT OpenCourseWare materials.

“I expect that in a few years we’ll look back and see that this was the moment when things shifted for OpenCourseWare to be truly usable for the whole world,” says Newton.

Community-led language translations of MIT OpenCourseWare materials serve as a high-impact example of the power of OpenCourseWare’s Creative Commons licensing, which grants everyone the right to revise materials to suit their particular needs and redistribute those revisions to the world.

While there isn’t currently a way for users of the MIT OpenCourseWare platform to quickly identify which videos are available in which languages, MIT OpenCourseWare is working toward building this capability into its website, as well as expanding its number of offerings in different languages.

“This project represents more than just translation,” says Lipkevych. “We’re enabling thousands of Ukrainians to build skills that will be essential for the country’s eventual reconstruction. We’re also hoping this model of collaboration can be extended to other languages and institutions, creating a template for making high-quality education accessible worldwide.”

Since its founding 19 years ago as a pioneering collaboration with Portuguese universities, research institutions and corporations, the MIT-Portugal Program (MPP) has achieved a slew of successes — from enabling 47 entrepreneurial spinoffs and funding over 220 joint projects between MIT and Portuguese researchers to training a generation of exceptional researchers on both sides of the Atlantic.

In March, with nearly two decades of collaboration under their belts, MIT and the Portuguese Science and Technology Foundation (FCT) signed an agreement that officially launches the program’s next chapter. Running through 2030, MPP’s Phase 4 will support continued exploration of innovative ideas and solutions in fields ranging from artificial intelligence and nanotechnology to climate change — both on the MIT campus and with partners throughout Portugal.  

“One of the advantages of having a program that has gone on so long is that we are pretty well familiar with each other at this point. Over the years, we’ve learned each other’s systems, strengths and weaknesses and we’ve been able to create a synergy that would not have existed if we worked together for a short period of time,” says Douglas Hart, MIT mechanical engineering professor and MPP co-director.

Hart and John Hansman, the T. Wilson Professor of Aeronautics and Astronautics at MIT and MPP co-director, are eager to take the program’s existing research projects further, while adding new areas of focus identified by MIT and FCT. Known as the Fundação para a Ciência e Tecnologia in Portugal, FCT is the national public agency supporting research in science, technology and innovation under Portugal’s Ministry of Education, Science and Innovation.

“Over the past two decades, the partnership with MIT has built a foundation of trust that has fostered collaboration among researchers and the development of projects with significant scientific impact and contributions to the Portuguese economy,” Fernando Alexandre, Portugal’s minister for education, science, and innovation, says. “In this new phase of the partnership, running from 2025 to 2030, we expect even greater ambition and impact — raising Portuguese science and its capacity to transform the economy and improve our society to even higher levels, while helping to address the challenges we face in areas such as climate change and the oceans, digitalization, and space.”

“International collaborations like the MIT-Portugal Program are absolutely vital to MIT’s mission of research, education and service. I’m thrilled to see the program move into its next phase,” says MIT President Sally Kornbluth. “MPP offers our faculty and students opportunities to work in unique research environments where they not only make new findings and learn new methods but also contribute to solving urgent local and global problems. MPP’s work in the realm of ocean science and climate is a prime example of how international partnerships like this can help solve important human problems."

Sharing MIT’s commitment to academic independence and excellence, Kornbluth adds, “the institutions and researchers we partner with through MPP enhance MIT’s ability to achieve its mission, enabling us to pursue the exacting standards of intellectual and creative distinction that make MIT a cradle of innovation and world leader in scientific discovery.”

The epitome of an effective international collaboration, MPP has stayed true to its mission and continued to deliver results here in the U.S. and in Portugal for nearly two decades — prevailing amid myriad shifts in the political, social, and economic landscape. The multifaceted program encompasses an annual research conference and educational summits such as an Innovation Workshop at MIT each June and a Marine Robotics Summer School in the Azores in July, as well as student and faculty exchanges that facilitate collaborative research. During the third phase of the program alone, 59 MIT students and 53 faculty and researchers visited Portugal, and MIT hosted 131 students and 49 faculty and researchers from Portuguese universities and other institutions.

In each roughly five-year phase, MPP researchers focus on a handful of core research areas. For Phase 3, MPP advanced cutting-edge research in four strategic areas: climate science and climate change; Earth systems: oceans to near space; digital transformation in manufacturing; and sustainable cities. Within these broad areas, MIT and FCT researchers worked together on numerous small-scale projects and several large “flagship” ones, including development of Portugal’s CubeSat satellite, a collaboration between MPP and several Portuguese universities and companies that marked the country’s second satellite launch and the first in 30 years.

While work in the Phase 3 fields will continue during Phase 4, researchers will also turn their attention to four more areas: chips/nanotechnology, energy (a previous focus in Phase 2), artificial intelligence, and space.

“We are opening up the aperture for additional collaboration areas,” Hansman says.

In addition to focusing on distinct subject areas, each phase has emphasized the various parts of MPP’s mission to differing degrees. While Phase 3 accentuated collaborative research more than educational exchanges and entrepreneurship, those two aspects will be given more weight under the Phase 4 agreement, Hart said.

“We have approval in Phase 4 to bring a number of Portuguese students over, and our principal investigators will benefit from close collaborations with Portuguese researchers,” he says.

The longevity of MPP and the recent launch of Phase 4 are evidence of the program’s value. The program has played a role in the educational, technological and economic progress Portugal has achieved over the past two decades, as well.  

“The Portugal of today is remarkably stronger than the Portugal of 20 years ago, and many of the places where they are stronger have been impacted by the program,” says Hansman, pointing to sustainable cities and “green” energy, in particular. “We can’t take direct credit, but we’ve been part of Portugal’s journey forward.”

Since MPP began, Hart adds, “Portugal has become much more entrepreneurial. Many, many, many more start-up companies are coming out of Portuguese universities than there used to be.”  

A recent analysis of MPP and FCT’s other U.S. collaborations highlighted a number of positive outcomes. The report noted that collaborations with MIT and other US universities have enhanced Portuguese research capacities and promoted organizational upgrades in the national R&D ecosystem, while providing Portuguese universities and companies with opportunities to engage in complex projects that would have been difficult to undertake on their own.

Regarding MIT in particular, the report found that MPP’s long-term collaboration has spawned the establishment of sustained doctoral programs and pointed to a marked shift within Portugal’s educational ecosystem toward globally aligned standards. MPP, it reported, has facilitated the education of 198 Portuguese PhDs.

Portugal’s universities, students and companies are not alone in benefitting from the research, networks, and economic activity MPP has spawned. MPP also delivers unique value to MIT, as well as to the broader US science and research community. Among the program’s consistent themes over the years, for example, is “joint interest in the Atlantic,” Hansman says.

This summer, Faial Island in the Azores will host MPP’s fifth annual Marine Robotics Summer School, a two-week course open to 12 Portuguese Master’s and first year PhD students and 12 MIT upper-level undergraduates and graduate students. The course, which includes lectures by MIT and Portuguese faculty and other researchers, workshops, labs and hands-on experiences, “is always my favorite,” said Hart.

“I get to work with some of the best researchers in the world there, and some of the top students coming out of Woods Hole Oceanographic Institution, MIT, and Portugal,” he says, adding that some of his previous Marine Robotics Summer School students have come to study at MIT and then gone on to become professors in ocean science.

“So, it’s been exciting to see the growth of students coming out of that program, certainly a positive impact,” Hart says.

MPP provides one-of-a-kind opportunities for ocean research due to the unique marine facilities available in Portugal, including not only open ocean off the Azores but also Lisbon’s deep-water port and a Portuguese Naval facility just south of Lisbon that is available for collaborative research by international scientists. Like MIT, Portuguese universities are also strongly invested in climate change research — a field of study keenly related to ocean systems.

“The international collaboration has allowed us to test and further develop our research prototypes in different aquaculture environments both in the US and in Portugal, while building on the unique expertise of our Portuguese faculty collaborator Dr. Ricardo Calado from the University of Aveiro and our industry collaborators,” says Stefanie Mueller, the TIBCO Career Development Associate Professor in MIT’s departments of Electrical Engineering and Computer Science and Mechanical Engineering and leader of the Human-Computer Interaction Group at the MIT Computer Science and Artificial Intelligence Lab.

Mueller points to the work of MIT mechanical engineering PhD student Charlene Xia, a Marine Robotics Summer School participant, whose research is aimed at developing an economical system to monitor the microbiome of seaweed farms and halt the spread of harmful bacteria associated with ocean warming. In addition to participating in the summer school as a student, Xia returned to the Azores for two subsequent years as a teaching assistant.

“The MIT-Portugal Program has been a key enabler of our research on monitoring the aquatic microbiome for potential disease outbreaks,” Mueller says.

As MPP enters its next phase, Hart and Hansman are optimistic about the program’s continuing success on both sides of the Atlantic and envision broadening its impact going forward.

“I think, at this point, the research is going really well, and we’ve got a lot of connections. I think one of our goals is to expand not the science of the program necessarily, but the groups involved,” Hart says, noting that MPP could have a bigger presence in technical fields such as AI and micro-nano manufacturing, as well as in social sciences and humanities.

“We’d like to involve many more people and new people here at MIT, as well as in Portugal,” he says, “so that we can reach a larger slice of the population.” 

In the future, quantum computers could rapidly simulate new materials or help scientists develop faster machine-learning models, opening the door to many new possibilities.

But these applications will only be possible if quantum computers can perform operations extremely quickly, so scientists can make measurements and perform corrections before compounding error rates reduce their accuracy and reliability.

The efficiency of this measurement process, known as readout, relies on the strength of the coupling between photons, which are particles of light that carry quantum information, and artificial atoms, units of matter that are often used to store information in a quantum computer.

Now, MIT researchers have demonstrated what they believe is the strongest nonlinear light-matter coupling ever achieved in a quantum system. Their experiment is a step toward realizing quantum operations and readout that could be performed in a few nanoseconds.

The researchers used a novel superconducting circuit architecture to show nonlinear light-matter coupling that is about an order of magnitude stronger than prior demonstrations, which could enable a quantum processor to run about 10 times faster.

There is still much work to be done before the architecture could be used in a real quantum computer, but demonstrating the fundamental physics behind the process is a major step in the right direction, says Yufeng “Bright” Ye SM ’20, PhD ’24, lead author of a paper on this research.

“This would really eliminate one of the bottlenecks in quantum computing. Usually, you have to measure the results of your computations in between rounds of error correction. This could accelerate how quickly we can reach the fault-tolerant quantum computing stage and be able to get real-world applications and value out of our quantum computers,” says Ye.

He is joined on the paper by senior author Kevin O’Brien, an associate professor and principal investigator in the Research Laboratory of Electronics at MIT who leads the Quantum Coherent Electronics Group in the Department of Electrical Engineering and Computer Science (EECS), as well as others at MIT, MIT Lincoln Laboratory, and Harvard University. The research appears today in Nature Communications.

A new coupler

This physical demonstration builds on years of theoretical research in the O’Brien group.

After Ye joined the lab as a PhD student in 2019, he began developing a specialized photon detector to enhance quantum information processing.

Through that work, he invented a new type of quantum coupler, which is a device that facilitates interactions between qubits. Qubits are the building blocks of a quantum computer. This so-called quarton coupler had so many potential applications in quantum operations and readout that it quickly became a focus of the lab.

This quarton coupler is a special type of superconducting circuit that has the potential to generate extremely strong nonlinear coupling, which is essential for running most quantum algorithms. As the researchers feed more current into the coupler, it creates an even stronger nonlinear interaction. In this sense, nonlinearity means a system behaves in a way that is greater than the sum of its parts, exhibiting more complex properties.

“Most of the useful interactions in quantum computing come from nonlinear coupling of light and matter. If you can get a more versatile range of different types of coupling, and increase the coupling strength, then you can essentially increase the processing speed of the quantum computer,” Ye explains.

For quantum readout, researchers shine microwave light onto a qubit and then, depending on whether that qubit is in state 0 or 1, there is a frequency shift on its associated readout resonator. They measure this shift to determine the qubit’s state.

Nonlinear light-matter coupling between the qubit and resonator enables this measurement process.

The MIT researchers designed an architecture with a quarton coupler connected to two superconducting qubits on a chip. They turn one qubit into a resonator and use the other qubit as an artificial atom which stores quantum information. This information is transferred in the form of microwave light particles called photons.

“The interaction between these superconducting artificial atoms and the microwave light that routes the signal is basically how an entire superconducting quantum computer is built,” Ye explains.

Enabling faster readout

The quarton coupler creates nonlinear light-matter coupling between the qubit and resonator that’s about an order of magnitude stronger than researchers had achieved before. This could enable a quantum system with lightning-fast readout.

“This work is not the end of the story. This is the fundamental physics demonstration, but there is work going on in the group now to realize really fast readout,” O’Brien says.

That would involve adding additional electronic components, such as filters, to produce a readout circuit that could be incorporated into a larger quantum system.

The researchers also demonstrated extremely strong matter-matter coupling, another type of qubit interaction that is important for quantum operations. This is another area they plan to explore with future work.

Fast operations and readout are especially important for quantum computers because qubits have finite lifespans, a concept known as coherence time.

Stronger nonlinear coupling enables a quantum processor to run faster and with lower error, so the qubits can perform more operations in the same amount of time. This means the qubits can run more rounds of error correction during their lifespans.

“The more runs of error correction you can get in, the lower the error will be in the results,” Ye says.

In the long run, this work could help scientists build a fault-tolerant quantum computer, which is essential for practical, large-scale quantum computation.

This research was supported, in part, by the Army Research Office, the AWS Center for Quantum Computing, and the MIT Center for Quantum Engineering.

Newly published results of a randomized, controlled clinical trial in Japan among more than 170 children aged 1 to 6 who underwent surgery show that by using electroencephalogram (EEG) readings of brain waves to monitor unconsciousness, an anesthesiologist can significantly reduce the amount of the anesthesia administered to safely induce and sustain each patient’s anesthetized state. On average, the little patients experienced significant improvements in several post-operative outcomes, including quicker recovery and reduced incidence of delirium.

“I think the main takeaway is that in kids, using the EEG, we can reduce the amount of anesthesia we give them and maintain the same level of unconsciousness,” says study co-author Emery N. Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT, an anesthesiologist at Massachusetts General Hospital, and a professor at Harvard Medical School. The study appeared April 21 in JAMA Pediatrics.

Yasuko Nagasaka, chair of anesthesiology at Tokyo Women’s Medical University and a former colleague of Brown’s in the United States, designed the study. She asked Brown to train and advise lead author Kiyoyuki Miyasaka of St. Luke’s International Hospital in Tokyo on how to use EEG to monitor unconsciousness and adjust anesthesia dosing in children. Miyasaka then served as the anesthesiologist for all patients in the trial. Attending anesthesiologists not involved in the study were always on hand to supervise.

Brown’s research in The Picower Institute for Learning and Memory, the Institute for Medical Engineering and Science, and the Department of Brain and Cognitive Sciences at MIT has shown that a person’s level of consciousness under any particular anesthetic drug is discernible from patterns of their brain waves. Each child’s brain waves were measured with EEG, but in the control group Miyasaka adhered to standard anesthesia dosing protocols while in the experimental group he used the EEG measures as a guide for dosing. The results show that when he used EEG, he was able to induce the desired level of unconsciousness with a concentration of 2 percent sevoflurane gas, rather than the standard 5 percent. Maintenance of unconsciousness, meanwhile, only turned out to require 0.9 percent concentration, rather than the standard 2.5 percent.

Meanwhile, a separate researcher, blinded to whether EEG or standard protocols were used, assessed the kids for “pediatric anesthesia emergence delirium” (PAED), in which children sometimes wake up from anesthesia with a set of side effects including lack of eye contact, inconsolability, unawareness of surroundings, restlessness, and non-purposeful movements. Children who received standard anesthesia dosing met the threshold for PAED in 35 percent of cases (30 out of 86), while children who received EEG-guided dosing met the threshold in 21 percent of cases (19 out of 91). The difference of 14 percentage points was statistically significant.

Meanwhile, the authors reported that, on average, EEG-guided patients had breathing tubes removed 3.3 minutes earlier, emerged from anesthesia 21.4 minutes earlier, and were discharged from post-acute care 16.5 minutes earlier than patients who received anesthesia according to the standard protocol. All of these differences were statistically significant. Also, no child in the study ever became aware during surgery.

The authors noted that the quicker recovery among patients who received EEG-guided anesthesia was not only better medically, but also reduced health-care costs. Time in post-acute care in the United States costs about $46 a minute, so the average reduced time of 16.5 minutes would save about $750 per case. Sevoflurane is also a potent greenhouse gas, Brown notes, so reducing its use is better for the environment.

In the study, the authors also present comparisons of the EEG recordings from children in the control and experimental groups. There are notable differences in the “spectrograms” that charted the power of individual brain wave frequencies both as children were undergoing surgery and while they were approaching emergence from anesthesia, Brown says.

For instance, among children who received EEG-guided dosing, there are well-defined bands of high power at about 1-3 Hertz and 10-12 Hz. In children who received standard protocol dosing, the entire range of frequencies up to about 15 Hz are at high power. In another example, children who experienced PAED showed higher power at several frequencies up to 30Hz than children who did not experience PAED.

The findings further validate the idea that monitoring brain waves during surgery can provide anesthesiologists with actionable guidance to improve patient care, Brown says. Training in reading EEGs and guiding dosing can readily be integrated in the continuing medical education practices of hospitals, he adds.

In addition to Miyasuka, Brown, and Nagasaka, Yasuyuki Suzuki is a study co-author.

Funding sources for the study include the MIT-Massachusetts General Brigham Brain Arousal State Control Innovation Center, the Freedom Together Foundation, and the Picower Institute.

For more than 30 years, Course 7 (Biology) students have descended to the expansive, windowless basement of Building 68 to learn practical skills that are the centerpiece of undergraduate biology education at the Institute. The lines of benches and cabinets of supplies that make up the underground MIT Biology Teaching Lab could easily feel dark and isolated. 

In the corner of this room, however, sits Senior Technical Instructor Vanessa Cheung ’02, who manages to make the space seem sunny and communal.

“We joke that we could rig up a system of mirrors to get just enough daylight to bounce down from the stairwell,” Cheung says with a laugh. “It is a basement, but I am very lucky to have this teaching lab space. It is huge and has everything we need.”

This optimism and gratitude fostered by Cheung is critical, as MIT undergrad students enrolled in classes 7.002 (Fundamentals of Experimental Molecular Biology) and 7.003 (Applied Molecular Biology Laboratory) spend four-hour blocks in the lab each week, learning the foundations of laboratory technique and theory for biological research from Cheung and her colleagues.

Running toward science education

Cheung’s love for biology can be traced back to her high school cross country and track coach, who also served as her second-year biology teacher. The sport and the fundamental biological processes she was learning about in the classroom were, in fact, closely intertwined. 

“He told us about how things like ATP [adenosine triphosphate] and the energy cycle would affect our running,” she says. “Being able to see that connection really helped my interest in the subject.”

That inspiration carried her through a move from her hometown of Pittsburgh, Pennsylvania, to Cambridge, Massachusetts, to pursue an undergraduate degree at MIT, and through her thesis work to earn a PhD in genetics at Harvard Medical School. She didn’t leave running behind either: To this day, she can often be found on the Charles River Esplanade, training for her next marathon. 

She discovered her love of teaching during her PhD program. She enjoyed guiding students so much that she spent an extra semester as a teaching assistant, outside of the one required for her program. 

“I love research, but I also really love telling people about research,” Cheung says.

Cheung herself describes lab instruction as the “best of both worlds,” enabling her to pursue her love of teaching while spending every day at the bench, doing experiments. She emphasizes for students the importance of being able not just to do the hands-on technical lab work, but also to understand the theory behind it.

“The students can tend to get hung up on the physical doing of things — they are really concerned when their experiments don’t work,” she says. “We focus on teaching students how to think about being in a lab — how to design an experiment and how to analyze the data.”

Although her talent for teaching and passion for science led her to the role, Cheung doesn’t hesitate to identify the students as her favorite part of the job. 

“It sounds cheesy, but they really do keep the job very exciting,” she says.

Using mind and hand in the lab

Cheung is the type of person who lights up when describing how much she “loves working with yeast.” 

“I always tell the students that maybe no one cares about yeast except me and like three other people in the world, but it is a model organism that we can use to apply what we learn to humans,” Cheung explains.

Though mastering basic lab skills can make hands-on laboratory courses feel “a bit cookbook,” Cheung is able to get the students excited with her enthusiasm and clever curriculum design. 

“The students like things where they can get their own unique results, and things where they have a little bit of freedom to design their own experiments,” she says. So, the lab curriculum incorporates opportunities for students to do things like identify their own unique yeast mutants and design their own questions to test in a chemical engineering module.

Part of what makes theory as critical as technique is that new tools and discoveries are made frequently in biology, especially at MIT. For example, there has been a shift from a focus on RNAi to CRISPR as a popular lab technique in recent years, and Cheung muses that CRISPR itself may be overshadowed within only a few more years — keeping students learning at the cutting edge of biology is always on Cheung’s mind. 

“Vanessa is the heart, soul, and mind of the biology lab courses here at MIT, embodying ‘mens et manus’ [‘mind and hand’],” says technical lab instructor and Biology Teaching Lab Manager Anthony Fuccione. 

Support for all students

Cheung’s ability to mentor and guide students earned her a School of Science Dean’s Education and Advising Award in 2012, but her focus isn’t solely on MIT undergraduate students. 

In fact, according to Cheung, the earlier students can be exposed to science, the better. In addition to her regular duties, Cheung also designs curriculum and teaches in the LEAH Knox Scholars Program. The two-year program provides lab experience and mentorship for low-income Boston- and Cambridge-area high school students. 

Paloma Sanchez-Jauregui, outreach programs coordinator who works with Cheung on the program, says Cheung has a standout “growth mindset” that students really appreciate.

“Vanessa teaches students that challenges — like unexpected PCR results — are part of the learning process,” Sanchez-Jauregui says. “Students feel comfortable approaching her for help troubleshooting experiments or exploring new topics.”

Cheung’s colleagues report that they admire not only her talents, but also her focus on supporting those around her. Technical Instructor and colleague Eric Chu says Cheung “offers a lot of help to me and others, including those outside of the department, but does not expect reciprocity.”

Professor of biology and co-director of the Department of Biology undergraduate program Adam Martin says he “rarely has to worry about what is going on in the teaching lab.” According to Martin, Cheung is ”flexible, hard-working, dedicated, and resilient, all while being kind and supportive to our students. She is a joy to work with.” 

The ocean’s deep-sea bed is scattered with ancient rocks, each about the size of a closed fist, called “polymetallic nodules.” Elsewhere, along active and inactive hydrothermal vents and the deep ocean’s ridges, volcanic arcs, and tectonic plate boundaries, and on the flanks of seamounts, lie other types of mineral-rich deposits containing high-demand minerals.

The minerals found in the deep ocean are used to manufacture products like the lithium-ion batteries used to power electric vehicles, cell phones, or solar cells. In some cases, the estimated resources of critical mineral deposits in parts of the abyssal ocean exceed global land-based reserves severalfold.

“Society wants electric-powered vehicles, solar cells for clean energy, but all of this requires resources,” says Thomas Peacock, professor of mechanical engineering at MIT, in a video discussing his research. “Land-based resources are getting depleted, or are more challenging to access. In parts of the ocean, there are much more of these resources than in land-based reserve. The question is: Can it be less impactful to mine some of these resources from the ocean, rather than from land?”

Deep-sea mining is a new frontier in mineral extraction, with potentially significant implications for industry and the global economy, and important environmental and societal considerations. Through research, scientists like Peacock study the impacts of deep-sea mining activity objectively and rigorously, and can bring evidence to bear on decision-making. 

Mining activities, whether on land or at sea, can have significant impacts on the environment at local, regional, and global scales. As interest in deep-seabed mining is increasing, driven by the surging demand for critical minerals, scientific inquiries help illuminate the trade-offs.

Peacock has long studied the potential impacts of deep-sea mining in a region of the Pacific Ocean known as the Clarion Clipperton Zone (CCZ), where polymetallic nodules abound. A decade ago, his research group began studying deep-sea mining, seeing a critical need to develop monitoring and modeling capabilities for assessing the scale of impact.

Today, his MIT Environmental Dynamics Laboratory (ENDLab) is at the forefront of advancing understanding for emerging ocean utilization technologies. With research anchored in fundamental fluid dynamics, the team is developing cutting-edge monitoring programs, novel sensors, and modeling tools.

“We are studying the form of suspended sediment from deep sea mining operations, testing a new sensor for sediment and another new sensor for turbulence, studying the initial phases of the sediment plume development, and analyzing data from the 2021 and 2022 technology trials in the Pacific Ocean,” he explains.

In deep-sea nodule mining, vehicles collect nodules from the ocean floor and convey them back to a vessel above. After the critical materials are collected on the vessel, some leftover sediment may be returned to the deep-water column. The resulting sediment plumes, and their potential impacts, are a key focus of the team’s work.

A 2022 study conducted in the CCZ investigated the dynamics of sediment plumes near a deep-seabed polymetallic nodule mining vehicle. The experiments reveal most of the released sediment-laden water, between 92 and 98 percent, stayed close to the sea-bed floor, spreading laterally. The results suggest that turbidity current dynamics set the fraction of sediment that remains suspended in the water, along with the scale of the subsequent ambient sediment plume. The implications of the process, which had been previously overlooked, are substantial for plume modeling and informative for environmental impact statements.

“New model breakthroughs can help us make increasingly trustworthy predictions,” he says. The team also contributed to a recent study, published in the journal Nature, which showed that sediment deposited away from a test mining site gets cleared away, most likely by ocean currents, and reported on any observed biological recovery.

Researchers observed a site four decades after a nodule test mining experiment. Although biological impacts in many groups of organisms were present, populations of several organisms, including sediment macrofauna, mobile deposit feeders, and even large-sized sessile fauna, had begun to reestablish despite persistent physical changes at the seafloor. The study was led by the National Oceanography Centre in the U.K.

“A great deal has been learned about the fluid mechanics of deep-sea mining, in particular when it comes to deep-sea mining sediment plumes,” says Peacock, adding that the scientific progress continues with more results on the way. The work is setting new standards for in-situ monitoring of suspended sediment properties, and for how to interpret field data from recent technical trials.

Whether you are a person about town or a worm in a dish, life can throw all kinds of circumstances your way. What you need is a nervous system flexible enough to cope. In a new study, MIT neuroscientists show how even a simple animal can repurpose brain circuits and the chemical signals, or “neuromodulators,” in its brain to muster an adaptive response to an infection. The study therefore may provide a model for understanding how brains in more complex organisms, including ourselves, manage to use what they have to cope with shifting internal states. 

“Neuromodulators play pivotal roles in coupling changes in animals’ internal states to their behavior,” the scientists write in their paper, recently published in Nature Communications. “How combinations of neuromodulators released from different neuronal sources control the diverse internal states that animals exhibit remains an open question.”

When C. elegans worms fed on infectious Pseudomonas bacteria, they ate less and became more lethargic. When the researchers looked across the nervous system to see how that behavior happened, they discovered that the worm had completely revamped the roles of several of its 302 neurons and some of the peptides they secrete across the brain to modulate behavior. Systems that responded to stress in one case or satiety in another became reconfigured to cope with the infection.

“This is a question of, how do you adapt to your environment with the highest level of flexibility given the set of neurons and neuromodulators you have,” says postdoc Sreeparna Pradhan, co-lead author of the new study in Nature Communications. “How do you make the maximum set of options available to you?”

The research to find out took place in the lab of senior author Steve Flavell, an associate professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences and an investigator of the Howard Hughes Medical Institute. Pradhan, who was supported by a fellowship from MIT’s K. Lisa Yang Brain-Body Center during the work, teamed up with former Flavell Lab graduate student Gurrein Madan to lead the research.

Pradhan says the team discovered several surprises in the course of the study, including that a neuropeptide called FLP-13 completely flipped its function in infected animals versus animals experiencing other forms of stress. Previous research had shown that when worms are stressed by heat, a neuron called ALA releases FLP-13 to cause the worms to go into quiescence, a sleep-like state. But when the worms in the new study ate Pseudomonas bacteria, a band of other neurons released FLP-13 to fight off quiescence, enabling the worms to survive longer. Meanwhile, ALA took on a completely different role during sickness: leading the charge to suppress feeding by emitting a different group of peptides.

A comprehensive approach

To understand how the worms responded to infection, the team tracked many features of the worms’ behavior for days and made genetic manipulations to probe the underlying mechanisms at play. They also recorded activity across the worms' whole brains. This kind of a comprehensive observation and experimentation is difficult to achieve in more complex animals, but C. elegans’ relative simplicity makes it a tractable testbed, Pradhan says. The team’s approach also is what allowed it to make so many unexpected findings.

For instance, Pradhan didn’t suspect that the ALA neuron would turn out to be the neuron that suppressed feeding, but when she observed their behavior for long enough, she started to realize the reduced feeding arose from the worms taking little breaks that they wouldn’t normally take. As she and Madan were manipulating more than a dozen genes they thought might be affecting behavior and feeding in the worm, she included another called ceh-17 that she had read about years ago that seemed to promote bouts of “microsleep” in the worms. When they knocked out ceh-17, they found that those worms didn’t reduce feeding when they got infected, unlike normal animals. It just so happens that ceh-17 is specifically needed for ALA to function properly, so that’s when the team realized ALA might be involved in the feeding-reduction behavior.

To know for sure, they then knocked out the various peptides that ALA releases and saw that when they knocked out three in particular, flp-24, nlp-8 and flp-7, infected worms didn’t exhibit reduced feeding upon infection. That clinched that ALA drives the reduced feeding behavior by emitting those three peptides.

Meanwhile, Pradhan and Madan’s screens also revealed that when infected worms were missing flp-13, they would go into a quiescence state much sooner than infected worms with the peptide available. Notably, the worms that fought off the quiescence state lived longer. They found that fighting off quiescence depended on the FLP-13 coming from four neurons (I5, I1, ASH and OLL), but not from ALA. Further experiments showed that FLP-13 acted on a widespread neuropeptide receptor called DMSR-1 to prevent quiescence.

Having a little nap

The last major surprise of the study was that the quiescence that Pseudomonas infection induces in worms is not the same as other forms of sleepiness that show up in other contexts, such as after satiety or heat stress. In those cases, worms don’t wake easily (with a little poke), but amid infection their quiescence was readily reversible. It seemed more like lethargy than sleep. Using the lab’s ability image all neural activity during behavior, Pradhan and Madan discerned that a neuron called ASI was particularly active during the bouts of lethargy. That observation solidified further when they showed that ASI’s secretion of the peptide DAF-7 was required for the quiescence to emerge in infected animals.

In all, the study showed that the worms repurpose and reconfigure — sometimes to the point of completely reversing — the functions of neurons and peptides to mount an adaptive response to infection, versus a different problem like stress. The results therefore shed light on what has been a tricky question to resolve. How do brains use their repertoire of cells, circuits, and neuromodulators to deal with what life hands them? At least part of the answer seems to be by reshuffling existing components, rather than creating unique ones for each situation.

“The states of stress, satiety, and infection are not induced by unique sets of neuromodulators," the authors wrote in their paper. "Instead, one larger set of neuromodulators may be deployed from different sources and in different combinations to specify these different internal states.”

In addition to Pradhan, Madan, and Flavell, the paper’s other authors are Di Kang, Eric Bueno, Adam Atanas, Talya Kramer, Ugur Dag, Jessica Lage, Matthew Gomes, Alicia Kun-Yang Lu, and Jungyeon Park.

Support for the research came from the the Picower Institute, the Freedom Together Foundation, the K. Lisa Yang Brain-Body Center, and the Yang Tan Collective at MIT; the National Institutes of Health; the McKnight Foundation; the Alfred P. Sloan Foundation; and the Howard Hughes Medical Institute.

When farmers apply pesticides to their crops, 30 to 50 percent of the chemicals end up in the air or soil instead of on the plants. Now, a team of researchers from MIT and Singapore has developed a much more precise way to deliver substances to plants: tiny needles made of silk.

In a study published today in Nature Nanotechnology, the researchers developed a way to produce large amounts of these hollow silk microneedles. They used them to inject agrochemicals and nutrients into plants, and to monitor their health.

“There’s a big need to make agriculture more efficient,” says Benedetto Marelli, the study’s senior author and an associate professor of civil and environmental engineering at MIT. “Agrochemicals are important for supporting our food system, but they’re also expensive and bring environmental side effects, so there’s a big need to deliver them precisely.”

Yunteng Cao PhD ’22, currently a postdoc Yale University, and Doyoon Kim, a former postdoc in the Marelli lab, led the study, which included a collaboration with the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group at the Singapore-MIT Alliance for Research and Technology (SMART).

In demonstrations, the team used the technique to give plants iron to treat a disease known as chlorosis, and to add vitamin B12 to tomato plants to make them more nutritious. The researchers also showed the microneedles could be used to monitor the quality of fluids flowing into plants and to detect when the surrounding soil contained heavy metals.

Overall, the researchers believe the microneedles could serve as a new kind of plant interface for real-time health monitoring and biofortification.

“These microneedles could be a tool for plant scientists so they can understand more about plant health and how they grow,” Marelli says. “But they can also be used to add value to crops, making them more resilient and possibly even increasing yields.”

The inner workings of plants

Accessing the inner tissues of living plants requires scientists to get through the plants’ waxy skin without causing too much stress. In previous work, the researchers used silk-based microneedles to deliver agrochemicals to plants in lab environments and to detect pH changes in living plants. But these initial efforts involved small payloads, limiting their applications in commercial agriculture.

“Microneedles were originally developed for the delivery of vaccines or other drugs in humans,” Marelli explains. “Now we’ve adapted it so that the technology can work with plants, but initially we could not deliver sufficient doses of agrochemicals and nutrients to mitigate stressors or enhance crop nutritional values.”

Hollow structures could increase the amount of chemicals microneedles can deliver, but Marelli says creating those structures at scale has historically required clean rooms and expensive facilities like the ones found inside the MIT.nano building.

For this study, Cao and Kim created a new way to manufacture hollow silk microneedles by combining silk fibroin protein with a salty solution inside tiny, cone-shaped molds. As water evaporated from the solution, the silk solidified into the mold while the salt forms crystalline structures inside the molds. When the salt was removed, it left behind in each needle a hollow structure or tiny pores, depending on the salt concentration and the separation of the organic and inorganic phases.

“It’s a pretty simple fabrication process. It can be done outside of a clean room — you could do it in your kitchen if you wanted,” Kim says. “It doesn’t require any expensive machinery.”

The researchers then tested their microneedles’ ability to deliver iron to iron-deficient tomato plants, which can cause a disease known as chlorosis. Chlorosis can decrease yields, but treating it by spraying crops is inefficient and can have environmental side effects. The researchers showed that their hollow microneedles could be used for the sustained delivery of iron without harming the plants.

The researchers also showed their microneedles could be used to fortify crops while they grow. Historically, crop fortification efforts have focused on minerals like zinc or iron, with vitamins only added after the food is harvested.

In each case, the researchers applied the microneedles to the stalks of plants by hand, but Marelli envisions equipping autonomous vehicles and other equipment already used in farms to automate and scale the process.

As part of the study, the researchers used microneedles to deliver vitamin B12, which is primarily found naturally in animal products, into the stalks of growing tomatoes, showing that vitamin B12 moved into the tomato fruits before harvest. The researchers propose their method could be used to fortify more plants with the vitamin.

Co-author Daisuke Urano, a plant scientist with DiSTAP, explains that “through a comprehensive assessment, we showed minimal adverse effects from microneedle injections in plants, with no observed short- or long-term negative impacts.”

“This new delivery mechanism opens up a lot of potential applications, so we wanted to do something nobody had done before,” Marelli explains.

Finally, the researchers explored the use of their microneedles to monitor the health of plants by studying tomatoes growing in hydroponic solutions contaminated with cadmium, a toxic metal commonly found in farms close to industrial and mining sites. They showed their microneedles absorbed the toxin within 15 minutes of being injected into the tomato stalks, offering a path to rapid detection.

Current advanced techniques for monitoring plant health, such as colorimetric and hyperspectral lead analyses, can only detect problems after plants growth is already being stunted. Other methods, such as sap sampling, can be too time-consuming.

Microneedles, in contrast, could be used to more easily collect sap for ongoing chemical analysis. For instance, the researchers showed they could monitor cadmium levels in tomatoes over the course of 18 hours.

A new platform for farming

The researchers believe the microneedles could be used to complement existing agricultural practices like spraying. The researchers also note the technology has applications beyond agriculture, such as in biomedical engineering.

“This new polymeric microneedle fabrication technique may also benefit research in microneedle-mediated transdermal and intradermal drug delivery and health monitoring,” Cao says.

For now, though, Marelli believes the microneedles offer a path to more precise, sustainable agriculture practices.

“We want to maximize the growth of plants without negatively affecting the health of the farm or the biodiversity of surrounding ecosystems,” Marelli says. “There shouldn’t be a trade-off between the agriculture industry and the environment. They should work together.”

This work was supported, in part, by the U.S. Office of Naval Research, the U.S. National Science Foundation, SMART, the National Research Foundation of Singapore, and the Singapore Prime Minister’s Office.

When the Venice Biennale’s 19th International Architecture Exhibition launches on May 10, its guiding theme will be applying nimble, flexible intelligence to a demanding world — an ongoing focus of its curator, MIT faculty member Carlo Ratti.

The Biennale is the world’s most renowned exhibition of its kind, an international event whose subject matter shifts over time, with a new curator providing new focus every two years. This year, the Biennale’s formal theme is “Intelligens,” the Latin word behind “intelligence,” in English, and “intelligenza,” in Italian — a word that evokes both the exhibition’s international scope and the many ways humans learn, adapt, and create.

“Our title is ‘Intelligens. Natural, artificial, collective,’” notes Ratti, who is a professor of the practice of urban technologies and planning in the MIT School of Architecture and Planning. “One key point is how we can go beyond what people normally think about intelligence, whether in people or AI. In the built environment we deal with many types of feedback and need to leverage all types of intelligence to collect and use it all.”

That applies to the subject of climate change, as adaptation is an ongoing focal point for the design community, whether facing the need to rework structures or to develop new, resilient designs for cities and regions.

“I would emphasize how eager architects are today to play a big role in addressing the big crises we face on the planet we live in,” Ratti says. “Architecture is the only discipline to bring everybody together, because it means rethinking the built environment, the places we all live.”

He adds: “If you think about the fires in Los Angeles, or the floods in Valencia or Bangladesh, or the drought in Sicily, these are cases where architecture and design need to apply feedback and use intelligence.”

Not just sharing design, but creating it

The Venice Biennale is the leading event of its kind globally and one of the earliest: It started with art exhibitions in 1895 and later added biannual shows focused on other facets of culture. Since 1980, the Biennale of Architecture was held every two years, until the 2020 exhibition — curated by MIT’s Hashim Sarkis — was rescheduled to 2021 due to the Covid-19 pandemic. It is now continuing in odd-numbered years.

After its May 10 opening, this year’s exhibition runs until Nov. 23.

Ratti is a wide-ranging scholar, designer, and writer, and the long-running director of MIT’s Senseable City Lab, which has been on the leading edge of using data to understand cities as living systems.

Additionally, Ratti is a founding partner of the international design firm Carlo Ratti Associati. He graduated from the Politecnico di Torino and the École Nationale des Ponts et Chaussées in Paris, then earned his MPhil and PhD at Cambridge University. He has authored and co-authored hundeds of publications, including the books “Atlas of the Senseable City” (2023) and “The City of Tomorrow” (2016). Ratti’s work has been exhibited at the Venice Biennale, the Design Museum in Barcelona, the Science Museum in London, and the Museum of Modern Art in New York, among other venues.

In his role as curator of this year’s Biennale, Ratti adapted the traditional format to engage with some of the leading questions design faces. Ratti and the organizers created multiple forums to gather feedback about the exhibition’s possibilities, sifting through responses during the planning process.

Ratti has also publicly called this year’s Biennale a “living lab,” not just an exhibition, in accordance with the idea of learning from feedback and developing designs in response.

Back in 1895, Ratti notes, the Biennale was principally “a place to share existing knowledge, with artists and architectures coming together every two years. Today, and for a few decades, you can find almost anything in architecture and art immediately online. I think Biennales can not only be places where you share existing knowledge, but places where you create new knowledge.”

At this moment, he emphasizes, that will often mean listening to nature as we grapple with climate solutions. It also implies recognizing that nature itself inevitably responds to inputs, too.

In this vein, Ratti says, “Remember what the great architect Carlo Scarpa once said: ‘Between a tree and a house, choose the tree.’ I see that as a powerful call to learn from nature — a vast lab of trial and error, guided by feedback loops. Too often in the 20th century, architects believed they had the solution and simply needed to scale it up. The results? Frequently disastrous. Especially now, when adaptability is everything, I believe in a different approach: experimentation, feedback, iteration. That’s the spirit I hope defines this year’s Biennale.”

An MIT touch

This year, MIT will again have a robust presence at the Biennale, even beyond Ratti’s presence as curator. In the first place, he emphasizes, there is a strong team organizing the Biennale. That includes MIT graduate student Claire Gorman, who has taken a year out of her studies to serve as principal assistant to the Biennale curator.

Many of the Biennale’s projects, Gorman observes, “align ecology, technology, and culture in stunning illustrations of the fact that intelligence emerges from the complex behaviors of many parts working together. Visitors to the exhibition will discover robots and artisans collaborating alongside algae, 3D printers, ancient building practices, and new materials. … One of the strengths of the exhibition is that it includes participants who approach similar topics from different points of view.”

Overall, Gorman adds, “Our hope is that visitors will come away from the exhibition with a sense of optimism about the capacity of design fields to unite many forms of expertise.”

Numerous other Institute faculty and researchers are represented as well. For instance, Daniela Rus, head of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), has helped design an installation about using robotics in the restoration of ancient structures. And famed MIT computer scientist Tim Berners-Lee, creator of the World Wide Web, is participating in a Biennale event on intelligence.

“In choosing ‘Intelligens’ as the Venice Biennale theme, Carlo Ratti recognizes that our moment requires a holistic understanding of how different forms of intelligence — from social and ecological to computational and spatial — converge to shape our built environment,” Rus says. “The Biennale offers a timely platform to explore how architecture can mediate between these intelligences, creating buildings and cities that think with and for us.”

Even as the Biennale runs, there is also a separate exhibit in Venice showcasing MIT work in architecture and design. Running from May 10 through Nov. 23, at the Palazzo Diedo, the show, “The Next Earth: Computation, Crisis, Cosmology,” features the work of 40 faculty members in MIT’s Department of Architecture, along with entries from the think tank Antikythera.

Meanwhile, for the Biennale itself, the main exhibition hall, the Arsenale, is open, but other event spaces are being renovated. That means the organizers are using additional spaces in the city of Venice this year to showcase cutting-edge design work and installations.

“We’re turning Venice into a living lab — taking the Biennale beyond its usual borders,” Ratti says. “But there’s a bigger picture: Venice may be the world’s most fragile city, caught between rising seas and the crush of mass tourism. That’s why it could become a true laboratory for the future. Venice today could be a glimpse of the world tomorrow.” 

The speed with which new technologies hit the market is nothing compared to the speed with which talented researchers find creative ways to use them, train them, even turn them into things we can’t live without. One such researcher is MIT MAD Fellow Alexander Htet Kyaw, a graduate student pursuing dual master’s degrees in architectural studies in computation and in electrical engineering and computer science.

Kyaw takes technologies like artificial intelligence, augmented reality, and robotics, and combines them with gesture, speech, and object recognition to create human-AI workflows that have the potential to interact with our built environment, change how we shop, design complex structures, and make physical things.

One of his latest innovations is Curator AI, for which he and his MIT graduate student partners took first prize — $26,000 in OpenAI products and cash — at the MIT AI Conference’s AI Build: Generative Voice AI Solutions, a weeklong hackathon at MIT with final presentations held last fall in New York City. Working with Kyaw were Richa Gupta (architecture) and Bradley Bunch, Nidhish Sagar, and Michael Won — all from the MIT Department of Electrical Engineering and Computer Science (EECS).

Curator AI is designed to streamline online furniture shopping by providing context-aware product recommendations using AI and AR. The platform uses AR to take the dimensions of a room with locations of windows, doors, and existing furniture. Users can then speak to the software to describe what new furnishings they want, and the system will use a vision-language AI model to search for and display various options that match both the user’s prompts and the room’s visual characteristics.

“Shoppers can choose from the suggested options, visualize products in AR, and use natural language to ask for modifications to the search, making the furniture selection process more intuitive, efficient, and personalized,” Kyaw says. “The problem we’re trying to solve is that most people don’t know where to start when furnishing a room, so we developed Curator AI to provide smart, contextual recommendations based on what your room looks like.” Although Curator AI was developed for furniture shopping, it could be expanded for use in other markets.

Another example of Kyaw’s work is Estimate, a product that he and three other graduate students created during the MIT Sloan Product Tech Conference’s hackathon in March 2024. The focus of that competition was to help small businesses; Kyaw and team decided to base their work on a painting company in Cambridge that employs 10 people. Estimate uses AR and an object-recognition AI technology to take the exact measurements of a room and generate a detailed cost estimate for a renovation and/or paint job. It also leverages generative AI to display images of the room or rooms as they might look like after painting or renovating, and generates an invoice once the project is complete.

The team won that hackathon and $5,000 in cash. Kyaw’s teammates were Guillaume Allegre, May Khine, and Anna Mathy, all of whom graduated from MIT in 2024 with master’s degrees in business analytics.

In April, Kyaw will give a TedX talk at his alma mater, Cornell University, in which he’ll describe Curator AI, Estimate, and other projects that use AI, AR, and robotics to design and build things.

One of these projects is Unlog, for which Kyaw connected AR with gesture recognition to build a software that takes input from the touch of a fingertip on the surface of a material, or even in the air, to map the dimensions of building components. That’s how Unlog — a towering art sculpture made from ash logs that stands on the Cornell campus — came about.

Unlog represents the possibility that structures can be built directly from a whole log, rather than having the log travel to a lumber mill to be turned into planks or two-by-fours, then shipped to a wholesaler or retailer. It’s a good representation of Kyaw’s desire to use building materials in a more sustainable way. A paper on this work, “Gestural Recognition for Feedback-Based Mixed Reality Fabrication a Case Study of the UnLog Tower,” was published by Kyaw, Leslie Lok, Lawson Spencer, and Sasa Zivkovic in the Proceedings of the 5th International Conference on Computational Design and Robotic Fabrication, January 2024.

Another system Kyaw developed integrates physics simulation, gesture recognition, and AR to design active bending structures built with bamboo poles. Gesture recognition allows users to manipulate digital bamboo modules in AR, and the physics simulation is integrated to visualize how the bamboo bends and where to attach the bamboo poles in ways that create a stable structure. This work appeared in the Proceedings of the 41st Education and Research in Computer Aided Architectural Design in Europe, August 2023, as “Active Bending in Physics-Based Mixed Reality: The Design and Fabrication of a Reconfigurable Modular Bamboo System.”

Kyaw pitched a similar idea using bamboo modules to create deployable structures last year to MITdesignX, an MIT MAD program that selects promising startups and provides coaching and funding to launch them. Kyaw has since founded BendShelters to build the prefabricated, modular bamboo shelters and community spaces for refugees and displaced persons in Myanmar, his home country.

“Where I grew up, in Myanmar, I’ve seen a lot of day-to-day effects of climate change and extreme poverty,” Kyaw says. “There’s a huge refugee crisis in the country, and I want to think about how I can contribute back to my community.”

His work with BendShelters has been recognized by MIT Sandbox, PKG Social Innovation Challenge, and the Amazon Robotics’ Prize for Social Good.

At MIT, Kyaw is collaborating with Professor Neil Gershenfeld, director of the Center for Bits and Atoms, and PhD student Miana Smith to use speech recognition, 3D generative AI, and robotic arms to create a workflow that can build objects in an accessible, on-demand, and sustainable way. Kyaw holds bachelor’s degrees in architecture and computer science from Cornell. Last year, he was awarded an SJA Fellowship from the Steve Jobs Archive, which provides funding for projects at the intersection of technology and the arts. 

“I enjoy exploring different kinds of technologies to design and make things,” Kyaw says. “Being part of MAD has made me think about how all my work connects, and helped clarify my intentions. My research vision is to design and develop systems and products that enable natural interactions between humans, machines, and the world around us.” 

As demand grows for more powerful and efficient microelectronics systems, industry is turning to 3D integration — stacking chips on top of each other. This vertically layered architecture could allow high-performance processors, like those used for artificial intelligence, to be packaged closely with other highly specialized chips for communication or imaging. But technologists everywhere face a major challenge: how to prevent these stacks from overheating.

Now, MIT Lincoln Laboratory has developed a specialized chip to test and validate cooling solutions for packaged chip stacks. The chip dissipates extremely high power, mimicking high-performance logic chips, to generate heat through the silicon layer and in localized hot spots. Then, as cooling technologies are applied to the packaged stack, the chip measures temperature changes. When sandwiched in a stack, the chip will allow researchers to study how heat moves through stack layers and benchmark progress in keeping them cool. 

"If you have just a single chip, you can cool it from above or below. But if you start stacking several chips on top of each other, the heat has nowhere to escape. No cooling methods exist today that allow industry to stack multiples of these really high-performance chips," says Chenson Chen, who led the development of the chip with Ryan Keech, both of the laboratory’s Advanced Materials and Microsystems Group.

The benchmarking chip is now being used at HRL Laboratories, a research and development company co-owned by Boeing and General Motors, as they develop cooling systems for 3D heterogenous integrated (3DHI) systems. Heterogenous integration refers to the stacking of silicon chips with non-silicon chips, such as III-V semiconductors used in radio-frequency (RF) systems.   

"RF components can get very hot and run at very high powers — it adds an extra layer of complexity to 3D integration, which is why having this testing capability is so needed," Keech says.

The Defense Advanced Research Projects Agency (DARPA) funded the laboratory's development of the benchmarking chip to support the HRL program. All of this research stems from DARPA's Miniature Integrated Thermal Management Systems for 3D Heterogeneous Integration (Minitherms3D) program.

For the Department of Defense, 3DHI opens new opportunities for critical systems. For example, 3DHI could increase the range of radar and communication systems, enable the integration of advanced sensors on small platforms such as uncrewed aerial vehicles, or allow artificial intelligence data to be processed directly in fielded systems instead of remote data centers.

The test chip was developed through collaboration between circuit designers, electrical testing experts, and technicians in the laboratory's Microelectronics Laboratory. 

The chip serves two functions: generating heat and sensing temperature. To generate heat, the team designed circuits that could operate at very high power densities, in the kilowatts-per-square-centimeter range, comparable to the projected power demands of high-performance chips today and into the future. They also replicated the layout of circuits in those chips, allowing the test chip to serve as a realistic stand-in. 

"We adapted our existing silicon technology to essentially design chip-scale heaters," says Chen, who brings years of complex integration and chip design experience to the program. In the 2000s, he helped the laboratory pioneer the fabrication of two- and three-tier integrated circuits, leading early development of 3D integration.

The chip's heaters emulate both the background levels of heat within a stack and localized hot spots. Hot spots often occur in the most buried and inaccessible areas of a chip stack, making it difficult for 3D-chip developers to assess whether cooling schemes, such as microchannels delivering cold liquid, are reaching those spots and are effective enough.

That's where temperature-sensing elements come in. The chip is distributed with what Chen likens to "tiny thermometers" that read out the temperature in multiple locations across the chip as coolants are applied.

These thermometers are actually diodes, or switches that allow current to flow through a circuit as voltage is applied. As the diodes heat up, the current-to-voltage ratio changes. "We're able to check a diode's performance and know that it's 200 degrees C, or 100 degrees C, or 50 degrees C, for example," Keech says. "We thought creatively about how devices could fail from overheating, and then used those same properties to design useful measurement tools."

Chen and Keech — along with other design, fabrication, and electrical test experts across the laboratory — are now collaborating with HRL Laboratories researchers as they couple the chip with novel cooling technologies, and integrate those technologies into a 3DHI stack that could boost RF signal power. "We need to cool the heat equivalent of more than 190 laptop CPUs [central processing units], but in the size of a single CPU package," Christopher Roper, co-principal investigator at HRL, said in a recent press release announcing their program.

According to Keech, the rapid timeline for delivering the chip was a challenge overcome by teamwork through all phases of the chip's design, fabrication, test, and 3D heterogenous integration.

"Stacked architectures are considered the next frontier for microelectronics," he says. "We want to help the U.S. government get ahead in finding ways to integrate them effectively and enable the highest performance possible for these chips."

The laboratory team presented this work at the annual Government Microcircuit Applications and Critical Technology Conference (GOMACTech), held March 17-20.

To understand what drives disease progression in tissues, scientists need more than just a snapshot of cells in isolation — they need to see where the cells are, how they interact, and how that spatial organization shifts across disease states. A new computational method called MESA (Multiomics and Ecological Spatial Analysis), detailed in a study published in Nature Genetics, is helping researchers study diseased tissues in more meaningful ways.

The work details the results of a collaboration between researchers from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, and Harvard, and the Broad Institute of MIT and Harvard, and was led by the Stanford team.

MESA brings an ecology-inspired lens to tissue analysis. It offers a pipeline to interpret spatial omics data — the product of cutting-edge technology that captures molecular information along with the location of cells in tissue samples. These data provide a high-resolution map of tissue “neighborhoods,” and MESA helps make sense of the structure of that map.

“By integrating approaches from traditionally distinct disciplines, MESA enables researchers to better appreciate how tissues are locally organized and how that organization changes in different disease contexts, powering new diagnostics and the identification of new targets for preventions and cures,” says Alex K. Shalek, the director of the Institute for Medical Engineering and Science (IMES), the J. W. Kieckhefer Professor in IMES and the Department of Chemistry, and an extramural member of the Koch Institute for Integrative Cancer Research at MIT, as well as an institute member of the Broad Institute and a member of the Ragon Institute.

“In ecology, people study biodiversity across regions — how animal species are distributed and interact,” explains Bokai Zhu, MIT postdoc and author on the study. “We realized we could apply those same ideas to cells in tissues. Instead of rabbits and snakes, we analyze T cells and B cells.”

By treating cell types like ecological species, MESA quantifies “biodiversity” within tissues and tracks how that diversity changes in disease. For example, in liver cancer samples, the method revealed zones where tumor cells consistently co-occurred with macrophages, suggesting these regions may drive unique disease outcomes.

“Our method reads tissues like ecosystems, uncovering cellular ‘hotspots’ that mark early signs of disease or treatment response,” Zhu adds. “This opens new possibilities for precision diagnostics and therapy design.”

MESA also offers another major advantage: It can computationally enrich tissue data without the need for more experiments. Using publicly available single-cell datasets, the tool transfers additional information — such as gene expression profiles — onto existing tissue samples. This approach deepens understanding of how spatial domains function, especially when comparing healthy and diseased tissue.

In tests across multiple datasets and tissue types, MESA uncovered spatial structures and key cell populations that were previously overlooked. It integrates different types of omics data, such as transcriptomics and proteomics, and builds a multilayered view of tissue architecture.

Currently available as a Python package, MESA is designed for academic and translational research. Although spatial omics is still too resource-intensive for routine in-hospital clinical use, the technology is gaining traction among pharmaceutical companies, particularly for drug trials where understanding tissue responses is critical.

“This is just the beginning,” says Zhu. “MESA opens the door to using ecological theory to unravel the spatial complexity of disease — and ultimately, to better predict and treat it.”

Many diseases are caused by a missing or defective copy of a single gene. For decades, scientists have been working on gene therapy treatments that could cure such diseases by delivering a new copy of the missing genes to the affected cells.

Despite those efforts, very few gene therapy treatments have been approved by the FDA. One of the challenges to developing these treatments has been achieving control over how much the new gene is expressed in cells — too little and it won’t succeed, too much and it could cause serious side effects.

To help achieve more precise control of gene therapy, MIT engineers have tuned and applied a control circuit that can keep expression levels within a target range. In human cells, they showed that they could use this method to deliver genes that could help treat diseases including fragile X syndrome, a disorder that leads to intellectual disability and other developmental problems.

“In theory, gene supplementation can solve monogenic disorders that are very diverse but have a relatively straightforward gene therapy fix if you could control the therapy well enough,” says Katie Galloway, the W. M. Keck Career Development Professor in Biomedical Engineering and Chemical Engineering and the senior author of the new study.

MIT graduate student Kasey Love is the lead author of the paper, which appears today in Cell Systems. Other authors of the paper include MIT graduate students Christopher Johnstone, Emma Peterman, and Stephanie Gaglione, and Michael Birnbaum, an associate professor of biological engineering at MIT.

Delivering genes

While gene therapy holds promise for treating a variety of diseases, including hemophilia and sickle cell anemia, only a handful of treatments have been approved so far, for an inherited retinal disease and certain blood cancers.

Most gene therapy approaches use a virus to deliver a new copy of a gene, which is then integrated into the DNA of host cells. Some cells may take up many copies of the gene, while others don’t receive any.

“Simple overexpression of that payload can result in a really wide range of expression levels in the target genes as they take up different numbers of copies of those genes or just have different expression levels,” Love says. “If it's not expressing enough, that defeats the purpose of the therapy. But on the other hand, expressing at too high levels is also a problem, as that payload can be toxic.”

To try to overcome this, scientists have experimented with different types of control circuits that constrain expression of the therapeutic gene. In this study, the MIT team decided to use a type of circuit called an incoherent feedforward loop (IFFL).

In an IFFL circuit, activation of the target gene simultaneously activates production of a molecule that suppresses gene expression. One type of molecule that can be used to achieve that suppression is microRNA — a short RNA sequence that binds to messenger RNA, preventing it from being translated into protein.

In this study, the MIT team designed an IFFL circuit, called “ComMAND” (Compact microRNA-mediated attenuator of noise and dosage), so that a microRNA strand that represses mRNA translation is encoded within the therapeutic gene. The microRNA is located within a short segment called an intron, which gets spliced out of the gene when it is transcribed into mRNA. This means that whenever the gene is turned on, both the mRNA and the microRNA that represses it are produced in roughly equal amounts.

This approach allows the researchers to control the entire ComMAND circuit with just one promoter — the DNA site where gene transcription is turned on. By swapping in promoters of different strengths, the researchers can tailor how much of the therapeutic gene will be produced.

In addition to offering tighter control, the circuit’s compact design allows it to be carried on a single delivery vehicle, such as a lentivirus or adeno-associated virus, which could improve the manufacturability of these therapies. Both of those viruses are frequently used to deliver therapeutic cargoes.

“Other people have developed microRNA based incoherent feed forward loops, but what Kasey has done is put it all on a single transcript, and she showed that this gives the best possible control when you have variable delivery to cells,” Galloway says.

Precise control

To demonstrate this system, the researchers designed ComMAND circuits that could deliver the gene FXN, which is mutated in Friedreich’s ataxia — a disorder that affects the heart and nervous system. They also delivered the gene Fmr1, whose dysfunction causes fragile X syndrome. In tests in human cells, they showed that they could tune gene expression levels to about eight times the levels normally seen in healthy cells.

Without ComMAND, gene expression was more than 50 times the normal level, which could pose safety risks. Further tests in animal models would be needed to determine the optimal levels, the researchers say.

The researchers also performed tests in rat neurons, mouse fibroblasts, and human T-cells. For those cells, they delivered a gene that encodes a fluorescent protein, so they could easily measure the gene expression levels. In those cells, too, the researchers found that they could control gene expression levels more precisely than without the circuit.

The researchers now plan to study whether they could use this approach to deliver genes at a level that would restore normal function and reverse signs of disease, either in cultured cells or animal models.

“There's probably some tuning that would need to be done to the expression levels, but we understand some of those design principles, so if we needed to tune the levels up or down, I think we'd know potentially how to go about that,” Love says.

Other diseases that this approach could be applied to include Rett syndrome, muscular dystrophy and spinal muscular atrophy, the researchers say.

“The challenge with a lot of those is they're also rare diseases, so you don't have large patient populations,” Galloway says. “We're trying to build out these tools that are robust so people can figure out how to do the tuning, because the patient populations are so small and there isn't a lot of funding for solving some of these disorders.”

The research was funded by the National Institute of General Medical Sciences, the National Science Foundation, the Institute for Collaborative Biotechnologies, and the Air Force Research Laboratory. 

Researchers from the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, in collaboration with MIT, A*STAR Skin Research Labs, and the National University of Singapore, have developed a novel method that can quickly and automatically detect and monitor microbial contamination in cell therapy products (CTPs) early on during the manufacturing process. By measuring ultraviolet light absorbance of cell culture fluids and using machine learning to recognize light absorption patterns associated with microbial contamination, this preliminary testing method aims to reduce the overall time taken for sterility testing and, subsequently, the time patients need to wait for CTP doses. This is especially crucial where timely administration of treatments can be life-saving for terminally ill patients. Cell therapy represents a promising new frontier in medicine, especially in treating diseases such as cancers, inflammatory diseases, and chronic degenerative disorders by manipulating or replacing cells to restore function or fight disease. However, a major challenge in CTP manufacturing is quickly and effectively ensuring that cells are free from contamination before being administered to patients. Existing sterility testing methods, based on microbiological methods,  are labor-intensive and require up to 14 days to detect contamination, which could adversely affect critically ill patients who need immediate treatment. While advanced techniques such as rapid microbiological methods (RMMs) can reduce the testing period to seven days, they still require complex processes such as cell extraction and growth enrichment mediums, and they are highly dependent on skilled workers for procedures such as sample extraction, measurement, and analysis. This creates an urgent need for new methods that offer quicker outcomes without compromising the quality of CTPs, meet the patient-use timeline, and use a simple workflow that does not require additional preparation. In a paper titled “Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products,” published in the journal Scientific Reports, SMART CAMP researchers described how they combined UV absorbance spectroscopy to develop a machine learning-aided method for label-free, noninvasive, and real-time detection of cell contamination during the early stages of manufacturing. This method offers significant advantages over both traditional sterility tests and RMMs, as it eliminates the need for staining of cells to identify labelled organisms, avoids the invasive process of cell extraction, and delivers results in under half-an-hour. It provides an intuitive, rapid “yes/no” contamination assessment, facilitating automation of cell culture sampling with a simple workflow. Furthermore, the developed method does not require specialized equipment, resulting in lower costs. “This rapid, label-free method is designed to be a preliminary step in the CTP manufacturing process as a form of continuous safety testing, which allows users to detect contamination early and implement timely corrective actions, including the use of RMMs only when possible contamination is detected. This approach saves costs, optimizes resource allocation, and ultimately accelerates the overall manufacturing timeline,” says Shruthi Pandi Chelvam, senior research engineer at SMART CAMP and first author of the paper. “Traditionally, cell therapy manufacturing is labor-intensive and subject to operator variability. By introducing automation and machine learning, we hope to streamline cell therapy manufacturing and reduce the risk of contamination. Specifically, our method supports automated cell culture sampling at designated intervals to check for contamination, which reduces manual tasks such as sample extraction, measurement, and analysis. This enables cell cultures to be monitored continuously and contamination to be detected at early stages,” says Rajeev Ram, the Clarence J. LeBel Professor in Electrical Engineering and Computer Science at MIT, a principal investigator at SMART CAMP, and the corresponding author of the paper. Moving forward, future research will focus on broadening the application of the method to encompass a wider range of microbial contaminants, specifically those representative of current good manufacturing practices environments and previously identified CTP contaminants. Additionally, the model’s robustness can be tested across more cell types apart from MSCs. Beyond cell therapy manufacturing, this method can also be applied to the food and beverage industry as part of microbial quality control testing to ensure food products meet safety standards.

Senior Madison Wang, a double major in creative writing and chemistry, developed her passion for writing in middle school. Her interest in chemistry fit nicely alongside her commitment to producing engaging narratives. 

Wang believes that world-building in stories supported by science and research can make for a more immersive reader experience.

“In science and in writing, you have to tell an effective story,” she says. “People respond well to stories.”  

A native of Buffalo, New York, Wang applied early action for admission to MIT and learned quickly that the Institute was where she wanted to be. “It was a really good fit,” she says. “There was positive energy and vibes, and I had a great feeling overall.”

The power of science and good storytelling

“Chemistry is practical, complex, and interesting,” says Wang. “It’s about quantifying natural laws and understanding how reality works.”

Chemistry and writing both help us “see the world’s irregularity,” she continues. Together, they can erase the artificial and arbitrary line separating one from the other and work in concert to tell a more complete story about the world, the ways in which we participate in building it, and how people and objects exist in and move through it. 

“Understanding magnetism, material properties, and believing in the power of magic in a good story … these are why we’re drawn to explore,” she says. “Chemistry describes why things are the way they are, and I use it for world-building in my creative writing.”

Wang lauds MIT’s creative writing program and cites a course she took with Comparative Media Studies/Writing Professor and Pulitzer Prize winner Junot Díaz as an affirmation of her choice. Seeing and understanding the world through the eyes of a scientist — its building blocks, the ways the pieces fit and function together — help explain her passion for chemistry, especially inorganic and physical chemistry.

Wang cites the work of authors like Sam Kean and Knight Science Journalism Program Director Deborah Blum as part of her inspiration to study science. The books “The Disappearing Spoon” by Kean and “The Poisoner’s Handbook” by Blum “both present historical perspectives, opting for a story style to discuss the events and people involved,” she says. “They each put a lot of work into bridging the gap between what can sometimes be sterile science and an effective narrative that gets people to care about why the science matters.”

Genres like fantasy and science fiction are complementary, according to Wang. “Constructing an effective world means ensuring readers understand characters’ motivations — the ‘why’ — and ensuring it makes sense,” she says. “It’s also important to show how actions and their consequences influence and motivate characters.” 

As she explores the world’s building blocks inside and outside the classroom, Wang works to navigate multiple genres in her writing, as with her studies in chemistry. “I like romance and horror, too,” she says. “I have gripes with committing to a single genre, so I just take whatever I like from each and put them in my stories.”

In chemistry, Wang favors an environment in which scientists can regularly test their ideas. “It’s important to ground chemistry in the real world to create connections for students,” she argues. Advancements in the field have occurred, she notes, because scientists could exit the realm of theory and apply ideas practically.

“Fritz Haber’s work on ammonia synthesis revolutionized approaches to food supply chains,” she says, referring to the German chemist and Nobel laureate. “Converting nitrogen and hydrogen gas to ammonia for fertilizer marked a dramatic shift in how farming could work.” This kind of work could only result from the consistent, controlled, practical application of the theories scientists consider in laboratory environments.

A future built on collaboration and cooperation

Watching the world change dramatically and seeing humanity struggle to grapple with the implications of phenomena like climate change, political unrest, and shifting alliances, Wang emphasizes the importance of deconstructing silos in academia and the workplace. Technology can be a tool for harm, she notes, so inviting more people inside previously segregated spaces helps everyone.

Criticism in both chemistry and writing, Wang believes, are valuable tools for continuous improvement. Effective communication, explaining complex concepts, and partnering to develop long-term solutions are invaluable when working at the intersection of history, art, and science. In writing, Wang says, criticism can help define areas to improve writers’ stories and shape interesting ideas.

“We’ve seen the positive results that can occur with effective science writing, which requires rigor and fact-checking,” she says. “MIT’s cross-disciplinary approach to our studies, alongside feedback from teachers and peers, is a great set of tools to carry with us regardless of where we are.”

Wang explores connections between science and stories in her leisure time, too. “I’m a member of MIT’s Anime Club and I enjoy participating in MIT’s Sport Taekwondo Club,” she says. The competitive aspect in tae kwon do allows for her to feed her competitive drive and gets her out of her head. Her participation in DAAMIT (Digital Art and Animation at MIT) creates connections with different groups of people and gives her ideas she can use to tell better stories. “It’s fascinating exploring others’ minds,” she says.

Wang argues that there’s a false divide between science and the humanities and wants the work she does after graduation to bridge that divide. “Writing and learning about science can help,” she asserts. “Fields like conservation and history allow for continued exploration of that intersection.”

Ultimately, Wang believes it’s important to examine narratives carefully and to question notions of science’s inherent superiority over humanities fields. “The humanities and science have equal value,” she says.

Every day, hundreds of chat messages flow between pilots, crew, and controllers of the Air Mobility Command's 618th Air Operations Center (AOC). These controllers direct a thousand-wide fleet of aircraft, juggling variables to determine which routes to fly, how much time fueling or loading supplies will take, or who can fly those missions. Their mission planning allows the U.S. Air Force to quickly respond to national security needs around the globe.

"It takes a lot of work to get a missile defense system across the world, for example, and this coordination used to be done through phone and email. Now, we are using chat, which creates opportunities for artificial intelligence to enhance our workflows," says Colonel Joseph Monaco, the director of strategy at the 618th AOC, which is the Department of Defense's largest air operations center.

The 618th AOC is sponsoring Lincoln Laboratory to develop these artificial intelligence tools, through a project called Conversational AI Technology for Transition (CAITT).

During a visit to Lincoln Laboratory from the 618th AOC's headquarters at Scott Air Force Base in Illinois, Colonel Monaco, Lieutenant Colonel Tim Heaton, and Captain Laura Quitiquit met with laboratory researchers to discuss CAITT. CAITT is a part of a broader effort to transition AI technology into a major Air Force modernization initiative, called the Next Generation Information Technology for Mobility Readiness Enhancement (NITMRE).

The type of AI being used in this project is natural language processing (NLP), which allows models to read and process human language. "We are utilizing NLP to map major trends in chat conversations, retrieve and cite specific information, and identify and contextualize critical decision points," says Courtland VanDam, a researcher in Lincoln Laboratory's AI Technology and Systems Group, which is leading the project. CAITT encompasses a suite of tools leveraging NLP.

One of the most mature tools, topic summarization, extracts trending topics from chat messages and formats those topics in a user-friendly display highlighting critical conversations and emerging issues. For example, a trending topic might read, "Crew members missing Congo visas, potential for delay." The entry shows the number of chats related to the topic and summarizes in bullet points the main points of conversations, linking back to specific chat exchanges.

"Our missions are very time-dependent, so we have to synthesize a lot of information quickly. This feature can really cue us as to where our efforts should be focused," says Monaco.

Another tool in production is semantic search. This tool improves upon the chat service's search engine, which currently returns empty results if chat messages do not contain every word in the query. Using the new tool, users can ask questions in a natural language format, such as why a specific aircraft is delayed, and receive intelligent results. "It incorporates a search model based on neural networks that can understand the user intent of the query and go beyond term matching," says VanDam.

Other tools under development aim to automatically add users to chat conversations deemed relevant to their expertise, predict the amount of ground time needed to unload specific types of cargo from aircraft, and summarize key processes from regulatory documents as a guide to operators as they develop mission plans.

The CAITT project grew out of the DAF–MIT AI Accelerator, a three-pronged effort between MIT, Lincoln Laboratory, and the Department of the Air Force (DAF) to develop and transition AI algorithms and systems to advance both the DAF and society. "Through our involvement in the AI Accelerator via the NITMRE project, we realized we could do something innovative with all of the unstructured chat information in the 618th AOC," says Heaton.

As laboratory researchers advance their prototypes of CAITT tools, they have begun to transition them to the 402nd Software Engineering Group, a software provider for the Department of Defense. That group will implement the tools into the operational software environment in use by the 618th AOC. 

Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.

They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.

The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).

“We designed a new language to talk about these new systems,” Zardini says. This new diagram-based “language” is heavily based on something called category theory, he explains.

It all has to do with designing the underlying architecture of computer algorithms — the programs that will actually end up sensing and controlling the various different parts of the system that’s being optimized. “The components are different pieces of an algorithm, and they have to talk to each other, exchange information, but also account for energy usage, memory consumption, and so on.” Such optimizations are notoriously difficult because each change in one part of the system can in turn cause changes in other parts, which can further affect other parts, and so on.

The researchers decided to focus on the particular class of deep-learning algorithms, which are currently a hot topic of research. Deep learning is the basis of the large artificial intelligence models, including large language models such as ChatGPT and image-generation models such as Midjourney. These models manipulate data by a “deep” series of matrix multiplications interspersed with other operations. The numbers within matrices are parameters, and are updated during long training runs, allowing for complex patterns to be found. Models consist of billions of parameters, making computation expensive, and hence improved resource usage and optimization invaluable.

Diagrams can represent details of the parallelized operations that deep-learning models consist of, revealing the relationships between algorithms and the parallelized graphics processing unit (GPU) hardware they run on, supplied by companies such as NVIDIA. “I’m very excited about this,” says Zardini, because “we seem to have found a language that very nicely describes deep learning algorithms, explicitly representing all the important things, which is the operators you use,” for example the energy consumption, the memory allocation, and any other parameter that you’re trying to optimize for.

Much of the progress within deep learning has stemmed from resource efficiency optimizations. The latest DeepSeek model showed that a small team can compete with top models from OpenAI and other major labs by focusing on resource efficiency and the relationship between software and hardware. Typically, in deriving these optimizations, he says, “people need a lot of trial and error to discover new architectures.” For example, a widely used optimization program called FlashAttention took more than four years to develop, he says. But with the new framework they developed, “we can really approach this problem in a more formal way.” And all of this is represented visually in a precisely defined graphical language.

But the methods that have been used to find these improvements “are very limited,” he says. “I think this shows that there’s a major gap, in that we don’t have a formal systematic method of relating an algorithm to either its optimal execution, or even really understanding how many resources it will take to run.” But now, with the new diagram-based method they devised, such a system exists.

Category theory, which underlies this approach, is a way of mathematically describing the different components of a system and how they interact in a generalized, abstract manner. Different perspectives can be related. For example, mathematical formulas can be related to algorithms that implement them and use resources, or descriptions of systems can be related to robust “monoidal string diagrams.” These visualizations allow you to directly play around and experiment with how the different parts connect and interact. What they developed, he says, amounts to “string diagrams on steroids,” which incorporates many more graphical conventions and many more properties.

“Category theory can be thought of as the mathematics of abstraction and composition,” Abbott says. “Any compositional system can be described using category theory, and the relationship between compositional systems can then also be studied.” Algebraic rules that are typically associated with functions can also be represented as diagrams, he says. “Then, a lot of the visual tricks we can do with diagrams, we can relate to algebraic tricks and functions. So, it creates this correspondence between these different systems.”

As a result, he says, “this solves a very important problem, which is that we have these deep-learning algorithms, but they’re not clearly understood as mathematical models.” But by representing them as diagrams, it becomes possible to approach them formally and systematically, he says.

One thing this enables is a clear visual understanding of the way parallel real-world processes can be represented by parallel processing in multicore computer GPUs. “In this way,” Abbott says, “diagrams can both represent a function, and then reveal how to optimally execute it on a GPU.”

The “attention” algorithm is used by deep-learning algorithms that require general, contextual information, and is a key phase of the serialized blocks that constitute large language models such as ChatGPT. FlashAttention is an optimization that took years to develop, but resulted in a sixfold improvement in the speed of attention algorithms.

Applying their method to the well-established FlashAttention algorithm, Zardini says that “here we are able to derive it, literally, on a napkin.” He then adds, “OK, maybe it’s a large napkin.” But to drive home the point about how much their new approach can simplify dealing with these complex algorithms, they titled their formal research paper on the work “FlashAttention on a Napkin.”

This method, Abbott says, “allows for optimization to be really quickly derived, in contrast to prevailing methods.” While they initially applied this approach to the already existing FlashAttention algorithm, thus verifying its effectiveness, “we hope to now use this language to automate the detection of improvements,” says Zardini, who in addition to being a principal investigator in LIDS, is the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems, and Society.

The plan is that ultimately, he says, they will develop the software to the point that “the researcher uploads their code, and with the new algorithm you automatically detect what can be improved, what can be optimized, and you return an optimized version of the algorithm to the user.”

In addition to automating algorithm optimization, Zardini notes that a robust analysis of how deep-learning algorithms relate to hardware resource usage allows for systematic co-design of hardware and software. This line of work integrates with Zardini’s focus on categorical co-design, which uses the tools of category theory to simultaneously optimize various components of engineered systems.

Abbott says that “this whole field of optimized deep learning models, I believe, is quite critically unaddressed, and that’s why these diagrams are so exciting. They open the doors to a systematic approach to this problem.”

“I’m very impressed by the quality of this research. ... The new approach to diagramming deep-learning algorithms used by this paper could be a very significant step,” says Jeremy Howard, founder and CEO of Answers.ai, who was not associated with this work. “This paper is the first time I’ve seen such a notation used to deeply analyze the performance of a deep-learning algorithm on real-world hardware. ... The next step will be to see whether real-world performance gains can be achieved.”

“This is a beautifully executed piece of theoretical research, which also aims for high accessibility to uninitiated readers — a trait rarely seen in papers of this kind,” says Petar Velickovic, a senior research scientist at Google DeepMind and a lecturer at Cambridge University, who was not associated with this work. These researchers, he says, “are clearly excellent communicators, and I cannot wait to see what they come up with next!”

The new diagram-based language, having been posted online, has already attracted great attention and interest from software developers. A reviewer from Abbott’s prior paper introducing the diagrams noted that “The proposed neural circuit diagrams look great from an artistic standpoint (as far as I am able to judge this).” “It’s technical research, but it’s also flashy!” Zardini says.