MIT Farm is growing

“This tomato is blowing my mind,” says Nermine Hassanin. The MIT graduate student in architecture is marveling at the fruit in her salad. 

She and several MIT School of Architecture and Planning classmates had spent their morning working at Hannan Healthy Foods Farm in Lincoln, Massachusetts, and then harvested the contents of the salad they were sharing at a picnic table: lettuce, kale, cucumbers, green onions, and tomatoes. 

“It doesn’t get much fresher unless you’re eating this in the field,” responds Zachary Rapaport, a classmate and co-founder of MIT Farm, a community-driven program at MIT that is radically changing what it means to eat farm-fresh food on campus and gain practical experience in urban farming. 

In fact, the program resonated so quickly with so many that it was awarded the Karl Taylor Compton Prize for a group this spring — the highest award presented by the Institute to students in recognition of excellent achievements in citizenship and devotion to the welfare of MIT. 

Seated with Hassanin and Rapaport were classmates Aisha Cheema and MIT Farm co-founder Aleks Banaś, undergraduate architecture major Amy Kim, Sanne Wright MCP ’24, and Mohammed Hannan, the owner of Hannan Healthy Foods Farm. The story of how they came to enjoy this meal on a brilliant summer Sunday is equal parts vision, hard work, and serendipity. 

Community in Cambridge

In their first year at MIT, Rapaport and Banaś were classmates in a design workshop 4.184 (ClimateCorps@MIT) that focused on climate action within communities. Bonding over their interest in food systems, the two shared a desire to continue the community service work that took them outside of the classroom.

“We started ideating on what it would mean to create relationships with students, faculty, and staff on campus with partners who were already growing food in the community,” says Banaś. “We saw many organizations in the community already doing inspiring work, so instead of starting from scratch we wanted to plug into this system.”

Their concept for MIT Farm explored a holistic view of community, food security, and supporting local farming in a time of climate resilience. It also capitalized on the desires of two budding architects to develop practices they see as essential for sustainable communities.

“After working in an architecture practice, I came to grad school to find a way to create an architecture practice grounded within real communities,” says Banaś. “For me, architecture is not just about creating a building, but creating an environment for other interactions. I think we have a certain level of responsibility to the people around us; we need to learn how to work together with them.”

“What I’ve learned through developing MIT Farm is that farming requires you to being accountable to seasonal change, to people, and to the environment in which we’re working,” adds Rapaport. “There are things you only begin to notice when you spend time with a place and really engage with it. In many ways, architecture is the same. We need to be very attentive to the world around us if we’re going to make good design.”

In 2025, Rapaport and Banaś actively sought out like-minded organizations on campus — including MIT Professor Miho Mazereeuw’s Urban Risk Lab, the MIT Office of Sustainability, and the Open Space Committee — to build relationships and germinate ideas. They found enthusiastic supporters for their ideas which kept them both engaged and excited. That spring, they entered their concept for MIT Farm into the iCampus competition, which recognizes innovative and creative applications of technology that improve living and learning at MIT; they were awarded one of the three cash prizes. 

Between supporters on campus and burgeoning relationships with like-minded organizations off campus, Rapaport and Banaś began to execute their vision.

One idea — a farm cart — kick-started what Rapaport terms “the growing side” of MIT Farm. He, Banaś, and classmate Courage Dzidula Kpodo MArch ’25 designed and built the two-tiered cart. It is constructed of pine with polycarbonate panels and divided into sections that support active vegetable gardens. Repurposed pipette tip containers sourced from campus labs serve as seedling planters. Supported on four wheels, the cart can be pushed or pulled anywhere.

“By introducing mobility, we began growing food without having it tethered to a specific plot of land,” says Rapaport. 

They discovered it was more than just a “moveable farm.” The cart became a DJ booth at school happy hours; was wheeled to nearby public schools as a learning tool; showcased at the “Day of Climate” event at the MIT Museum; and transformed into a tiny winter greenhouse at the East Campus dorms.

Field work

Rapaport and Banaś also located farm communities in Boston and beyond to develop volunteering and mentoring opportunities. Now, through MIT Farm, students can volunteer to work the two rooftop gardens at Boston Medical Center (BMC), a compact farm that generates 6,000 pounds of produce to serve patients and the BMC community. 

A second option for students to work at a city farm is with Eastie Farm in East Boston. A nonprofit with a focus on food security and climate justice, the organization supports local farms that struggle to compete with industrial agriculture.

Last summer, Rapaport and Banaś connected with Mohammed Hannan, the Lincoln farm owner and a laboratory manager at MIT. MIT students had already found their way to his farm, about 15 miles from Cambridge. Four years earlier, Heather Paxson, an anthropology professor and associate dean for faculty at MIT School of Humanities and Social Sciences, brought her anthropology class to visit Hannan Farm to learn about small-scale certified organic operations. 

Hannan was open to the idea of the MIT community working on the farm. He says it is “increasingly challenging” to find people who want to farm. He stresses the broader importance of community support for local agriculture, noting that food security and sustainable farming require collective effort from society — not just from farmers. 

“Many farmers around me are leaving farming to make their living elsewhere,” says Hannan. “It is a silent pandemic.” 

When classes resumed last fall, Rapaport, Banaś, and classmates began volunteering on weekends at the farm. Working with the MIT Office of Sustainability and the Anthropology Section, the group started a pilot program selling Hannan Farm’s produce for a reduced price to students on campus on Mondays. The Fresh Food Bag program offered curated bags of produce that had been harvested a day earlier. The program was met with delight among the MIT community, with nary a vegetable left behind each week. Rapaport and Banaś say they will expand the program this fall to include offering bags of fresh produce in MIT dorms.

The work on the farm changes constantly, based on the season and business needs. One day students may be in the field replanting plants that didn’t establish properly; on another, they may be maintaining tools or working on infrastructure projects.

“There isn't really an ‘off season’ since the farm operates year-round, and we've worked to bring MIT students out in every season and semester,” says Rapaport. “During the winter, we repaired greenhouses, prepared seedling trays, and started getting ready for the next growing season.” 

Hannan describes his collaboration with MIT students as “game changing,” highlighting how students gain practical, problem-solving experience while advising him on how to improve the farm’s infrastructure.

“It’s hard to visualize real-world problems when you are working in a lab [on campus],” says Hannan. “But the students are seeing the day-to-day challenges farmers are facing here. They are coming up with solutions that can alleviate these problems — and not just on my farm. We need more people from different backgrounds to see how we can locally grow more efficiently using the tools and technologies that we have. Sometimes we farmers don’t know where the technologies are — such as using solar irrigation. We’re not always tech-savvy, or we don’t have the expertise. Farming needs all of it.”

To everything, there is a season

In January, during MIT’s Independent Activities Period, Rapaport and Banaś conducted a weeklong workshop to design a root cellar to support food storage and preservation for the farm. The group also developed a curriculum for K-12 students to teach how to design a community garden with the support of a Day of Climate Grant they were awarded.

Kahlen Wheaton, a Department of Urban Studies and Planning (DUSP) undergrad, was among the students who worked on the curriculum and helped organize volunteers at the farm last spring.

“I really love the club,” says Wheaton. “It’s a great opportunity for MIT students to get out of their bubble and do some service work. It’s also great just to bring fresh produce to campus because, as an undergrad, I’m not always eating the most healthy or local foods.”

Wheaton had originally planned to study environmental engineering at MIT, but found urban planning and the opportunity to impact policy — especially as it influences designing cities for healthy lifestyles — exciting. She has planned her thesis on urban farming and will use the connections she is making in local farming to inform her work.

Pastures of plenty

This past spring saw dozens of volunteers spending weekends at Hannan Farm. Among them was Rania Ghosn, an MIT associate professor of architecture and urbanism, who brought her family. She says she enjoyed working shoulder-to-shoulder with colleagues in the field, and that MIT Farm provides a “tangible public good while strengthening a sense of belonging and joy.

“Working in the fields with my husband and our 10-year-old son became a kind of Sunday ritual: purposeful, physical, joyful, and full of meaning. For my son, the farm was a lesson no classroom could quite reproduce.”

On a recent Sunday, five architecture students tended to rows and rows of vegetables. Weeds had grown high with the recent rain, but after some tugging and digging, they released their hold in the dirt and lay in piles. The hot sun, the smell of fresh herbs, and the sounds of birds and insects made Cambridge and MIT feel very far away.

Nermine Hassanin has spent several Sundays here. She says she enjoys meeting new people on the farm each time.

“It’s a good sense of community here,” she says. “And being out of the city is very therapeutic.”

Growing up, Aisha Cheema worked with her dad on their family garden. She says working on the farm is “a nice way to stay connected” to those experiences.

“I’m interested in how this environment can be pulled into architecture,” says Cheema. “So many spaces seem siloed from the outdoors. I’d like to connect the outdoors to indoor spaces. I think some of the structures here — like the greenhouse — are great inspiration for me.”

Rapaport and Banaś spent their summer designing additional interventions for the Lincoln farm: an improved washing station and an upgraded farm stand that allows for cold storage to keep produce fresh longer. The design has been approved by the Lincoln Conservation Commission and permits are pending. 

Also, this summer, Amy Kim oversaw the design of a dormant garden plot on campus near Tang Hall. The undergraduate performed the work as part of MIT’s Undergraduate Research Opportunity Program. Kim says she designed the project because it allowed her “to get hands-on experience building a space that would foster community and collaboration, with the added environmental benefit of repurposing material such as scrap brick and scrap wood. 

“The garden's primary goal is to be a pleasant space to sit and relax with friends while surrounded by aromatic herbs and flowers,” Kim adds.

With their theses underway in preparation for Commencement next year, Rapaport and Banaś will hand off their oversight of MIT Farm to Wheaton and others this fall. They could not have guessed how the popularity of MIT Farm would have connected with so many students, or how many friends they cultivated off campus — and they are overjoyed with the results. They believe the community they have developed and fostered will keep MIT Farm vibrant into the future. 

Ghosn agrees.

“MIT Farm feels deeply MIT: mind and hand, ethics and experimentation, systems thinking and dirt under the fingernails,” she says. “Aleks and Zach have created a student-led project with moral clarity, organizational intelligence, and genuine public purpose. It is modest in its means and expansive in its imagination and impact."

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Reprogrammed NK immune cells target cancer and autoimmune disorders

MIT engineers have developed a new way to program natural killer (NK) immune cells to target cancerous T cells or T cells that have become autoreactive, leading to diseases such as lymphoma and autoimmune hepatitis. 

Using their new technique, the researchers can reprogram NK cells to target diseased T cells without ever removing the NK cells from the patient’s body. Instead, they use lipid nanoparticles to deliver messenger RNA directly to NK cells. These mRNA molecules encode proteins that allow the NK cells to seek and destroy the dysfunctional T cells.

Known as CAR NK cells, these cells could be deployed against a variety of diseases. The MIT team’s new approach could bypass one of the major obstacles to using these cells, which is the time and expense of manufacturing them outside the body.

“The manufacturing challenges with CAR NK cells are significant. The idea here is you can deliver a nanoparticle that will modify your own NK cells with a CAR construct and teach them to attack tumor cells,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science.

Anderson is the senior author of the study, which appears today in Nature Biotechnology. The paper’s lead authors are MIT postdoc Yuang Chen and MIT graduate students Julia Monkovic and Haseeb Mughal. Robert Langer, the David H. Koch Institute Professor at MIT, is also an author.

Easier reprogramming

Over the past decade, the U.S. Food and Drug Administration has approved seven new cancer treatments based on engineered T cells called chimeric antigen receptor (CAR) T cells. Chimeric antigen receptors are proteins that can be embedded into T cells, allowing them to detect a target antigen, such as a cancer protein. When the target is detected, the T cell becomes activated and destroys the target cell.

This approach has had great success in treating blood cancers, primarily those that originate from B cells, including several types of leukemia, lymphoma, and multiple myeloma. 

To make CAR T cells, doctors take a blood sample from the patient. The sample is sent to a lab where the T cells are isolated and then engineered to express the CAR protein. When enough CAR T cells have been generated, they are shipped back to the hospital and infused into the patient. 

In recent years, scientists have begun using a similar strategy to create CAR NK cells. One potential advantage of CAR NK cells over CAR T cells is that they could be used to kill diseased T cells. Using CAR T cells to treat T cell diseases often doesn’t work well because T cells that are engineered to target T cell proteins may end up killing each other, in addition to the diseased T cells. That issue doesn’t arise when using NK cells to target T cells.

“NK cells can treat these T cell malignancies — not only T cell cancer, but also T cell autoimmune diseases,” Chen says.

However, manufacturing large quantities of CAR NK cells poses many of the same difficulties as generating CAR T cells. To simplify the process, the MIT team wanted to come up with a way to generate CAR NK cells “in vivo” — that is, in the patient’s own body, without having to remove any cells.

“If a patient can just get an infusion of nanoparticles, it would dramatically simplify the process and reduce costs,” Anderson says.

Currently, there are no FDA-approved CAR NK cell therapies, but many researchers are working on developing them to treat cancer or autoimmune disorders. 

To generate these cells, the MIT team designed lipid nanoparticles (LNPs) that can deliver an mRNA payload. These strands of mRNA encode a CAR that can be targeted to specific cell types. For example, they could deliver a CAR that targets CD19, a protein found on B cells, or CD3, a protein found on autoreactive T cells. 

The particles are coated with antibody fragments that bind to receptors found on the surface of NK cells, to ensure they reach the correct targets.

Targeting dysfunctional T cells

After these particles were infused into mice, the researchers found that they could successfully reprogram about 80 percent of the targeted NK cells. The researchers then tested the particles’ potential to treat cancer and autoimmune hepatitis, which is caused by autoreactive T cells. 

In a mouse model of an aggressive form of melanoma, three of the five mice that received LNPs that reprogrammed their NK cells to target the cancer cells went into complete remission.

The researchers also saw significant tumor destruction when they used the LNPs to treat T cell lymphoma in a humanized mouse model, created when mice receive transfusions of human blood cells, allowing them to transiently generate a human-like immune system.

In these mice, tumors were eradicated after five injections of the reprogramming LNPs. This result was similar to what the researchers saw when they treated mice with CAR NK cells that were generated using the conventional approach — removing NK cells, reprogramming them, and returning them to the mice.

Lastly, the researchers demonstrated that their LNPs could generate CAR NK cells that target autoreactive T cells. For this application, they studied mice with autoimmune hepatitis, in which T cells attack hepatocytes in the liver. They found that in the treated mice, the autoreactive T cells were destroyed, and liver damage healed. These effects were seen after just one injection of the LNPs.

“This study is an excellent example of the power of nano delivery systems and their ability to potentially treat cancer and other diseases,” Langer says. 

The researchers hope that these kinds of engineered NK cells could be used to treat a variety of autoimmune disorders caused by dysfunctional T cells, including multiple sclerosis and rheumatoid arthritis. 

The research was funded by Sanofi, the Marble Center for Cancer Nanomedicine, and a Koch Institute Support (core) Grant from the National Cancer Institute.

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Exploring how plants thrive in a changing environment

The Sierra Nevada mountain range in California is as stunning as it is diverse, with storms from the Pacific Ocean dumping rain and snow on the range’s western slopes while arid conditions mark much of its eastern face. David Des Marais, MIT’s Amgen Career Development Associate Professor, knows the mountains well. His childhood in San Francisco was filled with hiking and camping excursions throughout the range.

At some point during those trips, he started wondering about one of the few constants of the landscape: the presence of plants. How were plants able to thrive in all those conditions? He turned the question into a career.

Des Marais, who directs the Parsons Laboratory in the Department of Civil and Environmental Engineering, studies how plants interact with their environment. The research spans molecules and mountain ranges, exploring everything from how individual genes change a plant’s ability to grow to how climate change will impact populations of red oak trees across the eastern U.S. His work also considers how evolutionary forces act on plants to change populations over time.

Plants are central to his lab, but Des Marais sees them as a window into all living things. He’s even quick to credit the health of his houseplants and garden to his wife, an evolutionary biologist at Harvard University.

“Understanding why life is so diverse is fascinating to me,” Des Marais says. “Understanding how different organisms, from bacteria to dinosaurs, have figured out how to interact with their challenging environment and optimize their fitness is inherently interesting. Why plants in particular? As a geneticist and a physiologist, plants are compelling experimental systems. If I have a theory about gene X causing function Y, I can go into the lab and delete gene X from this species and put it into that species and test function directly. It’s a powerful approach.”

Des Marais does not spend all his days in a lab. In addition to directing the Parsons Laboratory, he teaches four classes at MIT and has two sons who are active in baseball and theater. He welcomes these responsibilities.

At MIT especially, he embraces service.

“The only reason MIT works is because there are people willing to sacrifice some of their research to keep the place running,” Des Marais says. “One of the things I like about academia is that there’s sort of an institutional memory, and you try to honor the legacy of where you’ve been while you shape the future.”

From California to MIT

Des Marais grew up near Stanford University as well as near facilities for NASA and the U.S. Geological Survey, which meant there were always plenty of scientists and engineers around. His mother was a teacher, and his father was a geochemist with a knack for pointing out that the landscape around them was the result of geological processes dating back millions of years — a habit Des Marais admits he didn’t like at the time but that, in hindsight, influenced his thinking.

“I’m pretty sure that got into my brain, especially the idea that what looks static often comes about because of processes, and we can study those processes,” he says.

Des Marais studied integrative biology as an undergraduate at the University of California at Berkeley. He completed his PhD at Duke University around plant genetics.

“There’s a whole series of outdoor events, from the Boy Scouts through college, that made me start to think about the challenges that plants face living in these stressful environments,” he says. “That drove me to plant science and environmental science.”

In a long postdoc at the University of Texas at Austin, Des Marais learned to conduct experiments teasing out the effect of specific genes as he studied how they changed a model plant’s relationship with water.

Des Marais came to Boston primarily for personal reasons: He and his wife, who was also a postdoc at UT Austin, entered the job market at the same time. After some awkward moments competing for the same jobs, his wife landed a tenure-track position at Harvard University. Shortly after, he joined Harvard as a staff scientist.

In 2017, Des Marais discovered MIT’s Department of Civil and Environmental Engineering was hiring a plant ecologist.

“I don’t even think of myself as a plant ecologist, but I applied on a whim and they were interested,” Des Marais says. “I do a lot around plant water relations, and I joke that there are all these hydrologists in the department who study how water moves through the soil and into the atmosphere through these magical pipes. The magical pipes are plants, which play an important role in the water cycle. I realized a ton of people at MIT were studying that phenomenon at regional or global scales, but no one really studied how or why plants move water in the first place.”

Today Des Marais’ lab work can be broken into two broad categories. The first is centered around how plants function, including their relationship with environmental factors like water, temperature, and nutrients. Des Marais says some of his most impactful studies link specific molecules to how a plant functions in a given ecosystem.

The second centers around fundamental questions in plant evolution, what Des Marais describes as how plants “make decisions” about when to grow, reproduce, germinate, and more.

One current research project looks at how climate change will impact red oak trees, an important species and carbon sink. He is also examining climate change’s impact on plants that are important for food systems.

“We know that climate change is impacting plant function, and it’s impacting plant nutrition, and that impacts human nutrition,” Des Marais says. “Climate change is going to affect where we can grow which plants efficiently and how we can get them where they need to be eaten. All that stuff is critically important for humans.”

Understanding plants for the planet

Des Marais says it’s been fascinating working with experts in environmental chemistry and fluid mechanics as part of the Parsons Laboratory. He sees his role as director as being primarily about fostering community.

“The students are great,” Des Marais says. “It’s a funky old building, but we all work well together. We try to make sure everyone feels comfortable and welcome in the space.”

Even if he can’t take credit for his houseplants, Des Marais appreciates the vital role plants play for the planet.

“Plants are the foundation of the food chain, and there’s no way to get around that,” Des Marais says. “The efficiency of plant photosynthesis directly impacts our nutrition, and it impacts what we can feed the animals that we eat. It affects carbon cycling and water cycling — put simply, plant biology matters.”

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Economists Robert E. Hall PhD ’67 and Michael Woodford PhD ’83 share Nobel prize

Robert Hall PhD ’67 and Michael Woodford PhD ’83, scholars who both earned their doctorates at MIT, have been awarded the 2026 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.

Hall, a professor at Stanford University, and Woodford, a professor at Columbia University, were recognized for work that helped revitalize ideas about how government policy can spur growth to help end economic downturns. The Nobel citation describes their research as contributing to “new macroeconomic models that have significantly improved our ability to implement effective stabilisation policy.”

Both scholars helped integrate microeconomic analysis into macroeconomic models. In Hall’s case, the Nobel committee particularly cited research he conducted in the late 1970s shedding new light on economic expectations and household consumption. The committee also spotlighted Woodford’s construction of a comprehensive and influential macroeconomic model that was built on microeconomic foundations and introduced in his 2003 book, “Interest and Prices: Foundations of a Theory of Monetary Policy.” 

The work of both scholars helped update models for intervention during economic crises, based on the ideas of John Maynard Keynes, which had worked many times in the 20th century but encountered newfound difficulties adapting to the conditions of the 1970s. So-called New Keynesian models used for economic stabilization today are a result of both economists’ research, the citation noted. 

Hall is the Robert and Carole McNeil Joint Senior Fellow at the Hoover Institution and Professor in the Department of Economics at Stanford University. He received his BA in economics from the University of California at Berkeley before earning his PhD from MIT.

Woodford is the the John Bates Clark Professor of  Political Economy at Columbia University. He received a BA from the University of Chicago and a JD from Yale University Law School prior to receiving his PhD from MIT. 

Remarkably, Hall becomes the fifth former doctoral student principally advised by MIT economist Robert M. Solow to earn the Nobel Prize in economics. The others are George Akerlof PhD ’66, Peter Diamond PhD ’63, William Nordhaus PhD ’67, and Joseph Stiglitz PhD ’67, all of whom had Solow as their principal advisor in MIT’s graduate program. Additionally, MIT professor of biology H. Robert Horvitz ’68, a Nobel laureate in 2002 in the category of physiology or medicine, wrote his undergraduate thesis in economics under the direction of Solow. In 1987, Solow received the Nobel Prize himself. 

Woodford did his doctoral work under the primary supervision of Timothy Kehoe, now of the University of Minnesota — although Woodford does thank Solow in the first paragraph of his doctoral thesis, for helping him “distinguish important problems from exercises of merely technical interest.”

There are now 17 MIT alumni who have won the Nobel Prize in economics. Previously, 10 people have won the award while serving on the MIT faculty, and eight former faculty have also been named recipients.

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Supporting systems of success

A new course in the Department of Electrical Science and Engineering (EECS) is not focused on machine learning or microprocessors, on cryptography or quantum computing. Instead, 6.C31 (Hack Yourself) draws student interest with an intriguing pitch: that by using computing tools to implement a personal toolkit of more than 60 positive habits, students can substantially improve their college experiences. 

A joint venture with the Experimental Study Group (ESG), developed by EECS Senior Lecturer Ana Bell, ESG Visiting Instructor Carter Jernigan, and ESG Senior Lecturer Paola Rebusco, Hack Yourself offers students the opportunity to upgrade their lives. 

“We wanted to build an MIT course that exposes students, in a structured way, to what we actually know about learning and thriving,” explain the course’s instructors. “That said, MIT students are skeptical unless they can see evidence. They want data. They want methods. So we built the class around research-backed ideas that students can explore themselves, and that’s where computational thinking comes in. The course treats well-being and learning almost like systems problems: How do you gather meaningful data, break a big question into manageable parts, test an intervention, and evaluate whether it worked?”

The course’s ambitious goals fall into two categories: personal interventions that students can deploy in their own lives, and analytical skills that they can use to test whether their new habits are working. “It’s an introduction to the data science pipeline and evidence-based thinking,” says Bell. “We start from the idea that good analysis begins with good data. Students spend time learning how to collect information thoughtfully: designing surveys, conducting interviews, thinking about bias and measurement. Those are human-centered skills that complement technical training and remain valuable no matter what technologies exist years from now.”

The data that students collect are as diverse as their goals. “A few patterns come up consistently — reducing smartphone use and getting more consistent sleep,” says Jernigan. “For juniors and seniors especially, a lot of interest centers on work habits, productivity, and the transition into the workplace, which naturally pulls them toward teamwork, leadership, and relationships. What’s interesting is that no single intervention emerged as universally ‘best.’” 

Many of the course’s assignments center on teamwork, using research-backed interventions for more-effective group collaboration. “Some are small interventions students can use anywhere — for instance, replacing default small talk with a prompt like, ‘Tell me something good going on in your life,’” explains Jernigan. “We also teach practices like the pre-mortem, where a group imagines a project has already failed, lists everything that could have gone wrong, and works backward to prevent those problems.” 

Even the class’s reading assignments are collaborative, using Perusall (a shared annotation app). “We polled students about which strategies were most personally impactful and which they’d recommend to others, and the answers varied a lot,” says Jernigan. “That’s part of the point. Different tools resonate with different people, so we help students build a broad toolkit.”

Part of that toolkit looks like a tiny photo album. The course’s “Intention cards” are professionally printed, playing-card-sized collectibles that the students accumulate as they move through the course. Each card distills a research-backed habit or strategy into a few words and a colorful AI-generated image as a tangible reminder of the skill. “Students earn a few each week and end up with more than 60 by the end of the semester,” says Jernigan.

As the students gain more cards, they store them in a booklet — but the instructors have noticed them carefully tending and tidying their collections. 

“It’s been interesting to watch the collection dynamic work in ways we didn’t fully design for,” Rebusco reports. “Students reorganize the cards, revisit old ones when they get new ones, and carry the booklets around. Without explicitly thinking about it, they end up engaging in spaced repetition and recall — the same learning principles we discuss in class.”

Junior Sarah Hopp has amassed a large collection of the cards. The course 6-3 Computer Science and Engineering major, from Stevens Point, Wisconsin, has found that the mementos work as both a memory tool and a sentimental souvenir. “With each card, I also tend to remember the class we talked about that intervention, and any positive memories I have from that class experience.” 

Hopp’s favorite class experience was an assignment, originally developed in 2001 by Laura King, in which the student is challenged to envision her “best possible future self” in close detail for 20 minutes, imagining completed life goals and successful outcomes, before writing that imagined future self a letter. “It gave me time to really think about what I want from my future and what I want it to look like,” says Hopp. “It also forced me to focus on the positives and the accomplishments I want to achieve, rather than thinking about the negatives and my fears for the future.”

“When I first signed up for Hack Yourself, I looked forward to having a psychologically insightful and reflective class environment that I figured would be a helpful break from my usual STEM-heavy course content,” says student Amitoj Singh, who is majoring in 6-3. “I wanted to build a habit of using strategies from class to improve my lifestyle in college.” One of the strategies Singh implemented stemmed from a class discussion on flow state (a state of full and joyous task absorption experienced when a person is immersed in challenging and creative work). After learning that even brief interruptions could significantly impact productivity and derail flow, Singh changed how he scheduled his time, creating longer and more coherent “blocks” for his desired goals. “I think one of the most powerful tools [the class uses] is the massive pool of research the lectures and content draw from,” says Singh, who is now working on “satisficing,” or learning when to deem an outcome satisfactory rather than chasing a perhaps-unattainable state of optimization.

The Hack Yourself curriculum is intentionally designed to steer students toward small experiments and goals that feel personal to them. “We’ve had students explore relationships, sports performance, music, motivation, and productivity,” says Jernigan. “They develop hypotheses, think about measurement and bias, design surveys or interviews, and create an analysis plan. They don’t run a large-scale study, but they learn how to think rigorously about their proposed questions and hypotheses, using the same kind of decomposition and iterative thinking we use in computing.” Part of that rigor focuses on the use of large language models — specifically, how to evaluate the quality of both the prompt and the response. “We ask students to engage critically with modern AI tools,” says Bell. “They can use generative AI to explore ideas or analyze data, but they’re required to reflect on the process and critique the quality of what comes back. The durable skill isn’t fluency with today’s AI — it’s knowing how to evaluate any tool that comes next.”

Bell, Jernigan, and Rebusco hope that their students will leave with the tools to ask better questions — a lifelong skill. As they note, “We intentionally avoided making this another machine learning course. Students already get plenty of exposure to advanced technical tools elsewhere at MIT. In this course, we focus on a fundamental element: the data science pipeline itself. How do you collect useful data? How do you ask good questions? How do you design a small experiment before trying to boil the ocean? That mindset scales well, even as technology evolves.”

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Shape-sensing sheet digitally tracks its movement as it bends and twists

MIT engineers have developed a flexible, shape-sensing sheet that digitally reconstructs its own form as it bends and twists. 

The sensor is similar in principle to some motion-capture garments, which track a person’s physical movements to mimic them in a virtual avatar. Those designs use rigid sensors that are stitched into a suit at strategic locations. 

In contrast, the team’s new design uses multiple soft optical fibers that zig-zag throughout a soft and stretchable sheet. When the sheet bends, the fibers bend in kind, changing the pattern of light that travels through them. The researchers developed an algorithm to interpret the bending light patterns on the fly, to create a digital image that moves the same way the sheet is moving. The team also showed that the design is resistant to damage: Even when a few fibers are cut or disconnected, the remaining fibers can still accurately reconstruct the sheet’s overall shape. 

“Our setup is targeting fully soft and stretchable sensing surfaces so it’s potentially safer for interacting with a human or a soft environment,” says Qifan Yu, a mechanical engineering graduate student at MIT. 

The team envisions that the new design could be fashioned into a soft and pliable garment that a user could comfortably wear to physically control a video game character or a tele-operated robot. The sheet could also be used in physical therapy settings, where it could wrap around a patient’s leg or arm to track and record their mobility and range of motion during each session. 

“In physical therapy, you often need to gather patient data on how they are moving and stretching their arm during rehabilitation, for example,” Yu says. “Surface shape sensing could be very useful in that way.” 

Yu and his colleagues present their design in a study appearing in the journal Advanced Intelligent Systems. The study’s co-authors at MIT are graduate student Nina Cao and assistant professor of mechanical engineering Kaitlyn Becker.

The shape of light

The team’s shape-sensing design is a tweak on the standard optical fiber. Also known as a waveguide, an optical fiber is designed to guide light efficiently through a fiber. Optical fibers consist of a core of transparent material, such as glass, wrapped in a dark, opaque cladding. When light is sent through one end of the fiber, the dark cladding traps the light within the glass core, which itself is extremely smooth, allowing the light to pass straight through the fiber with near-perfect efficiency. Waveguides are used in fiber optic cables for super fast, efficient data transmission and telecommunications. 

Scientists have also experimented with waveguides as simple shape sensors: When the fibers bend, they affect how much light can make it all the way through. The more a fiber bends, the less light comes out the other end. The amount of light that shines out, then, can be a measure of the degree the fiber bends. In this way, optical fibers have been used to sense simple curvatures. 

“People have used waveguides to sense the shape of a line, which they have applied to a robot arm to see how it curves,” Yu says. “But they haven’t been applied to surface shape sensing, to reconstruct the 3D shape of a surface. That’s what we’re trying to do here.”

Fiber tweaks

In their new design, the researchers fabricated their own optical fibers, with some twists. Like conventional waveguides, they made their fibers from a transparent core surrounded by dark cladding. Rather than hard glass, they used a clear, flexible rubber core and a cladding made from the same rubbery material, dyed black. 

And instead of keeping the core completely smooth, they intentionally roughed up one side. If one side of the fiber is rougher than the other, they reasoned, then when the fiber is bent one way, any light passing through would scatter off the rough surface and affect the total amount of light that makes it out the other end. This would be a different amount than would pass through if the fiber were bent at the same angle but toward the fiber’s smooth side. In this way, the half-roughened fiber should act as a bidirectional shape sensor. 

The team fabricated multiple bidirectional optical fibers and looked to embed them into a soft sheet of silicone, arranged so that the fibers would reconstruct the sheet’s shape as it bends and twists. To do so, the researchers carried out simulations of sheets embedded with different optical fiber patterns, from a straightforward checkerboard to criss-crossed, zig-zag arrangements. 

They simulated different ways to bend or twist the sheets, and measured the output of light from each sheet’s configuration of fibers. They converted these light measurements into estimates of how much each fiber must be bending, and combined these to construct an overall 3D shape of the sheet, which they compared to the original simulated sheet shape. From these simulations, they found that a particular spacing of zig-zagging fibers was closest to recreating the sheet’s original shape.

The researchers then fabricated a shape-sensing sheet with the same zig-zag pattern of optical fibers embedded into the sheet. They incorporated an LED at one end of each fiber and a light sensor at the other end, which they connected to an external circuit board to collect and amplify the light measurements. They also developed an algorithm to automatically convert the measurements from fibers into a reconstruction of the sheet’s 3D shape as a whole. 

In experiments, they showed that the algorithm smoothly created a virtual reconstruction of the sheet, almost in real time, as the researchers twisted it into different forms. For instance, when they folded the sheet diagonally, and then again in the opposite direction, the virtual twin mimicked the changing shapes, almost in real time. 

They also placed the sheet in different 3D-printed molds so they could precisely measure the difference between the digitally reconstructed sheet and the physical sheet laying over each mold. In these tests, they found the soft sensing sheet was more accurate than other designs.

“We use a metric that describes the distance between the actual surface and the reconstructed surface, and from that, we found our error was less than 0.4 centimeters,” Yu says. “Existing designs, which are based on rigid sensors, have errors of around 1 to 2 centimeters. So that’s respectable, and at least on par with existing technologies.”

The team will further optimize the sensing sheet, first by thinning it down. Currently, the optical fibers are 1 millimeter thick. Other fabrication processes could shave them down to tens of micrometers, which can be thinner than a strand of hair. Then, the researchers envision embedding many more fibers into a garment, to sense detailed changes in its shape and form, for instance to gauge a patient’s performance in physical therapy. 

“We hope to build tools that augment what a physical therapist can do and help them track and quantify their patients’ progress over time,” says Becker. “These sensing sheets could help a physical therapist to recall and compare more precisely how an evaluation went today versus two months and hundreds of appointments ago.”

This research was supported, in part, by MathWorks.

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Using AI to mitigate the growing environmental threat of data centers

The global building boom of power-hungry data centers is straining electrical grids, causing greater reliance on energy from polluting fossil fuels.

Christina Delimitrou, a newly tenured associate professor at MIT, is fighting this environmental threat by rethinking how the computer servers and networking equipment inside those data centers operate. 

She and her group apply machine learning to make large-scale data centers more efficient, secure, and reliable. They redesign outdated cloud computing systems, develop methods to manage shared hardware resources, and create streamlined server architectures. 

These advances allow data center operators to coax more computational power out of existing hardware.

“If data centers are not utilized to the best of their capabilities, then they will burn much more power than they need to meet growing user demand,” says Delimitrou, the KDD Career Development Associate Professor in Communications and Technology in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). “There is a lot of bloating, especially on the software side of these systems. If we can remove that bloating in a way that doesn’t compromise performance, then we won’t need to build as many new data centers.”

She also harnesses AI to help programmers find and fix problems in cloud-based applications, like music-streaming services or video conferencing systems. This eliminates application downtime that hampers performance and drains computational resources.

“By managing resources more effectively in the cloud, the end user gets more predictable performance from the application running on their smartphone,” she adds.

Mathematical beginnings

Delimitrou grew up in a midsized town within the vast plains of northern Greece. Her early interest in math and science was sparked, in part, by the ancient history of her homeland, where Euclid and Pythagoras studied mathematical problems more than 2,000 years ago. 

“In Greece, there is a long tradition of geometry,” she says.

She also drew scientific inspiration from her parents. Her mother worked as a chemical engineer and her father as a pharmacist — and both encouraged their daughter’s innate curiosity.

Her early affinity for math led Delimitrou to study computer engineering at the National Technical University of Athens, even though she didn’t know much about the field. She quickly gravitated toward courses that focused on the applied science of engineering.

For her diploma thesis — a project all students complete during their fifth and final year of study — she studied resource management in a computer when multiple applications are running at once. 

“A lot of the challenges I was looking at then would get much harder if, instead of a single system, you had 100,000 of these systems. That was a problem that piqued my interest,” she says.

Seeking to make a bigger impact as a researcher, Delimitrou pursued a graduate degree at Stanford University. She began tackling inefficiencies in cloud computing systems and large-scale data centers, which was a rapidly growing area of research. 

Through that work, Delimitrou and her research mentor, Christos Kozyrakis, the Leonard Bosack and Sandy K. Lerner Professor of Engineering, realized many large computing systems were underutilized.

“You would expect, with all the demand for these systems, that they should be running close to 100 percent capacity. But we found that most were running at only about 15 percent capacity,” she says. “This is not a resource-efficient or sustainable way of scaling these systems.”

Applying AI

To push that utilization closer to 100 percent, she began investigating machine-learning solutions to streamline cumbersome computational processes. Machine learning could automate resource management operations in the cloud, identifying solutions that developers might miss on their own.

“Applying machine learning to solve a large-scale system problem was a novel approach at the time. It was a bit risky because people had not yet shown that these techniques would work,” Delimitrou says. “But empirical approaches require a lot of expertise, and the scale of the system is so large that it is difficult for users to manage. This is why machine learning is often the best solution.”

After earning her PhD, Delimitrou continued this line of work as an assistant professor at Cornell University.

One tool her group developed, Seer, uses deep learning to anticipate and prevent problems in web applications before they happen. This averts widespread slowdowns that may occur if a developer tries to fix a problem manually.   

As she delved deeper into cloud computing, Delimitrou observed that cloud applications were changing. Developers were now splitting applications into smaller pieces to spread across multiple servers, which increases the speed of deployment.

“But the servers were not built for this new style of application design. So, I rethought some of my earlier work to build machine-learning systems for this new class of applications,” she says.

To tackle these new challenges, she found herself collaborating more often with faculty members who had different software and hardware expertise. Those collaborations opened exciting new research areas.

A few years later, she decided to join MIT because of the opportunity to collaborate with researchers at the top of their fields in hardware and software engineering. She became an assistant professor in EECS in 2022.

Creative approaches

At MIT, Delimitrou also enjoys the teaching aspect of her role. One of her favorite courses to teach is 6.191 (Computation Structure), a popular undergraduate class with about 350 students each semester. 

While it’s challenging to keep the course material fresh when the field constantly evolves, she strives to inspire creativity in her students.

“I want the students to learn how to think and learn on their own. Part of that involves shifting away from formulaic assignments and making classes more open-ended. I’d rather give the students something to make them think more deeply,” she says.

In the lab, a creative mindset helps Delimitrou and her team identify novel solutions to problems in cloud computing that others might overlook.

For instance, she extended prior work on debugging problems in cloud applications to encompass not just errors in the code, but also security issues that can make user data vulnerable to hackers.

She also uses AI to redesign software systems so they better fit the capabilities of existing hardware.

“One of the challenges when it comes to applying AI to these systems is that the AI is not interpretable,” she says. “A lot of the work we are doing now involves adding explainability into these AI tools so people can get useful feedback from the system.”

That not only helps developers ensure AI is giving the right answer, but also provides insights into how to design systems better in the future.

She finds that studying these large-scale cloud computing systems is becoming more challenging because tech companies that operate data centers use proprietary hardware, unlike the commodity equipment of early cloud computing days, as well as software systems that can’t be accessed by academic teams. A solution that works in the lab might not work in the real world.

To that end, Delimitrou and her group create clones of proprietary systems and applications. One tool they developed, called Ditto, mimics an application’s structure and performance characteristics, enabling a wide range of studies.

She expects her work to continue shifting as machine-learning models become more advanced, opening new possibilities to boost application performance and hardware efficiency.

“But you still have to use AI carefully. While it can greatly accelerate the application development side, we still need to audit it and be especially careful about how these models are applied so we don’t lose the ability to gain insights out of the solutions AI is giving,” she says.

Outside the lab, Delimitrou enjoys spending time with her husband and 1-year-old daughter.

While she doesn’t have as much time for hobbies these days, she also enjoys gardening and building an electrical toy train track for her daughter, as well as playing classical piano and painting nature scenes. She became interested in painting at Cornell, where she would often paint the many waterfalls near the campus. 

Whether she is working in the garden or painting, Delimitrou says she finds spending time outdoors to be a relaxing escape from the technical nature of her work, but also an important reminder of the role her research plays in sustainability. 

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