Robotic lab sets up and runs optics experiments on demand

Every new generation of phone display, television screen, and solar panel is a result of precision optics experiments, which use lasers and other light sources to measure the optical properties of candidate materials. These experiments can take months to run, requiring scientists to meticulously angle and adjust delicate light sources, mirrors, cameras, and other components, in a careful and constant tuning that can be physically tedious and time-consuming. 

But MIT scientists say the whole process of building and running an optics experiment could one day be fully automated. Taking a step toward such a future, they have developed a reconfigurable, robotic optics laboratory. 

The new robotic lab autonomously assembles standard optical components into desired configurations. It can then tune the angle and position of mirrors and lenses with micron-scale precision to produce beams of light with specific properties. The system can also safely dismantle an experiment and reassemble the parts into an entirely new setup. 

The team showed that the robotic system could autonomously build and fine-tune a tabletop laser cavity — a key element of most optics experiments. The system could also precisely manipulate components to perform several optical tasks, such as centering a laser beam, aligning multiple beams, and automatically stabilizing the beams in response to physical disturbances.

“We start with randomly placed components,” says Sachin Vaidya, a postdoc in MIT’s Research Laboratory of Electronics. “At the end, we have a fully functioning laser that the robot has built.”

The researchers are expanding the robotic lab, in a physical and virtual sense. In addition to improving the system’s physical sensing, maneuvering, and overall space, they are developing a cloud-based application that gives users virtual access to the physical robot. They envision that one day, scientists from anywhere will be able to remotely access robotic optics labs and virtually submit experimental protocols or queries that the labs would then set up and run autonomously. 

“There are many things this could enable,” says Marin Soljacic, the Cecil and Ida Green Professor of Physics at MIT. “A robot isn’t going to get bored. It can work 365 days, 24 hours a day, on very boring things. That will free up so much creativity and time for scientists to then push theories and see what we can do. Science could progress much faster.”

The MIT team will present the details of the new system at the Intelligent Robots and Systems (IROS) conference later this month. Along with Soljacic and Vaidya, project team members include co-lead Seou Choi, Caio Silva, and Shrish Choudhury from MIT, Shiekh Uddin of Nokia Bell Labs, and Sajib Shuvo of Arizona State University.

A city of light

A tabletop optics experiment can resemble a miniature city of densely packed mirrors, lenses, and light sources. Scientists manually arrange and align the various components in precise configurations, then shine light into the experiment. The lenses and mirrors bounce and focus the beam into a desired wavelength, frequency, or intensity that can then be used to probe or manipulate a given material. 

“Sometimes this manual setup takes days or months depending on the complexity of the experiment,” Soljacic says. “It’s meticulous work that has to be done again and again for each experiment.”

Most labs do incorporate some level of automation in an optics setup, such as motorized tuners that mechanically turn knobs to precisely angle a mirror. 

“These components can automate the most tedious parts of an experiment,” Vaidya notes. “But no one has built a full system that goes from no setup to a completely aligned setup in one tool. That was our goal, to show complete automation through all the steps that go into an optics experiment.”

Auto-tuned optics

The team’s robotic lab centers around a robotic arm with seven moveable joints that is attached to a metallic tabletop. The robot picks and places lenses, mirrors, and other optical components, each of which the researchers installed in its own 3D-printed plastic housing. 

The housings are designed such that the robot can easily and safely grip and move each component. The researchers etched the top of each housing with a QR code containing information about the component within the housing (such as whether it is a lens versus a mirror, and its exact dimensions and capabilities). Each housing has a magnetic base that helps stabilize a component once the arm places it down on the metallic tabletop. 

The researchers designed a Wi-Fi-enabled “fine-adjustment tool” that clips onto the mount of standard optical components. The motorized tool can be wirelessly controlled to turn a component’s knobs, for instance to angle a mirror. 

“The way humans do this tuning is by feel, and based on a lot of intuition,” Vaidya says. “This tool is at least as precise as a human, but in reality it is much more precise.”

The team also installed a pair of cameras over the entire setup that provides a birds-eye view of the tabletop experiment. Finally, they developed a “software stack,” or a set of programs that enables the robot to navigate through every step of setting up and continuously tuning an experiment. These steps include recognizing a specific component, knowing how to safely approach and pick it up, where to move it, and how to avoid collisions with other parts of the experiment along the way. 

Finally, they designed a simple virtual user interface to allow an experimenter to remotely direct the robot. For instance, when a user drags the icon for a mirror from one spot to another, and clicks a button to confirm, the robot responds by picking up the actual mirror and placing it down at the corresponding location on the table. 

As a demonstration, they directed the robot to assemble various components into a laser cavity. A laser cavity consists of two mirrors arranged on either side of a crystal. When a beam of light is shone into the setup, it pings back and forth between the two mirrors. With each pass, the light also passes through the crystal, which amplifies the light’s intensity, to a point that whatever light escapes, is intense enough to form a laser. 

“We wanted to pick a demonstration in optics that’s reasonably challenging,” says co-lead author Seou Choi, a graduate student in electrical engineering and computer science. “This is not something a new trainee could do in an afternoon. It requires a lot of alignment and component experience.”

In the end, the robot successfully built a functional laser cavity by autonomously carrying out 50 maneuvers, all within 30 minutes. When the researchers introduced physical disturbances to the setup, such as randomly moving a component on the table, the system automatically readjusted components to maintain the laser’s intensity. 

“Even tiny vibrations or temperature changes can degrade an optics experiment,” Vaidya says. “An autonomous lab could continuously monitor its own performance and repair the alignment before valuable data is lost.”

The researchers envision that robotic labs like theirs could be paired with a nearby library of physical components that another robot could fetch and deliver to a tabletop robot to arrange into an experiment. Such a system could work to build and run experiments, then break them down and set up new ones on demand, or continuously run an experiment that requires active 24/7 monitoring.

“A system like this could help industry test prototypes faster, for everything from cameras and displays to solar cells and AR/VR goggles,” Vaidya says. 

For their part, the researchers are applying the new robot lab to test promising carbon-capture materials. By shining light with specific properties at these materials, they can get information about how a material absorbs carbon dioxide. 

“Experimental optics is the backbone of many important fields,” Vaidya says. “Our work takes the first step toward optical labs that can operate faster, more reliably, and without manual intervention in a domain that demands extreme precision and diversity of experimental setups.”

This research was supported, in part, by the Korea Foundation for Advanced Studies Overseas PhD Scholarship, the U.S. National Science Foundation, the U.S. Army DEVCOM ARL Army Research Office, Parviz Tayebati, the MIT Undergraduate Research Opportunities Program (UROP), the MIT Generative AI Impact Consortium (MGAIC), and Shell International Exploration and Production Inc.

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Nanoscale mechanics could enable brain-inspired computing

MIT researchers have created a new computing platform that could be used to develop intelligent and adaptive next-generation electronics that can simultaneously perform multiple functions, like computing and memory, all within one extremely compact, energy-efficient device.

Such a platform opens opportunities for low-power edge computing applications, interactive medical and environmental monitoring systems, and smart robots.

The researchers accomplished this by leveraging the unique mechanical response of soft polymers at the nanoscale. A mechanical response is how a structure changes when a force is applied to it. 

They harnessed this response to create tiny mechanical devices that use reconfigurable motion to remember and process information in a way that mimics how neurons behave in the brain.

Because key computing functions are built into the intrinsic properties of the soft polymer material, the number of components needed to perform the functions are minimized, enabling a compact and versatile platform for information processing. 

“Complex and coupled nanoscale phenomena can provide tremendous opportunities for new approaches to information processing and integrating multiple functionalities, such as computing, sensing, and actuation. This could enable levels of energy efficiency, autonomy, and reconfigurability in nanoscale devices and systems that are challenging to achieve with conventional computing platforms,” says Farnaz Niroui, an associate professor of electrical engineering and computer science (EECS), a member of the Research Laboratory of Electronics (RLE), and senior author of a paper on this device. “Here, we harness the intrinsic mechanical properties of materials to engineer device-level dynamics, such that the material building blocks play a much more active role in defining device functionality than conventionally considered.”

She is joined on the paper by co-lead authors Peter Satterthwaite and Sarah Spector, EECS graduate students; as well as Jeremiah Johnson, the A. Thomas Guertin Professor of Chemistry at MIT; Maxwell Conte, a graduate student in the Department of Materials Science and Engineering; Teddy Hsieh, an EECS graduate student; postdoc Eduard Bobylev; and Srinidhi Venkatesh ’25. The research appears today in Science Advances. 

Bioinspired computation

Biological systems can leverage physical changes, like motion or deformation, to process information efficiently and without needing access to a central controller. 

For instance, an octopus has a highly distributed nervous systems, with about two-thirds of its neurons spread throughout its arms. This allows the octopus to sense and process information about its environment locally and generate responses without requiring access to the central brain.

As an example, an octopus can mechanically change the color cells in its skin, enabling it to go through a rapid and context-specific camouflage process.

“You can think of an octopus as continuous computing matter, with computing, memory, sensing, and actuation distributed throughout its body,” Niroui adds.

Inspired by such performance, the researchers sought to develop a platform that can compute using mechanical transformations at the nanoscale. In mechanical computing, calculations are performed through physical transformations like movement and compression. 

While bioinspired mechanical computing platforms have been developed at the micro and macro scales, the MIT researchers shrunk their device to the nanoscale. At this scale, even minute mechanical transformations can lead to drastic changes in a material’s properties. This can enable complex computing in an energy-efficient platform.

But achieving the reversible nanomechanical transformations needed for such computing is a fundamental challenge. When two surfaces come very close, they experience strong adhesive forces that pull the surfaces together, making them impossible to unstick. 

To overcome this fundamental challenge, the researchers built a device with a super-thin film of the soft polymer polydimethylsiloxane (PDMS) sandwiched between two metal electrodes. This soft spacer balances the adhesive forces between the two metal surfaces, keeping the electrodes from crashing together in an irreversible way.

“The soft material in serves as a ‘nano-spring,’ to help balance the forces to achieve nanoscale mechanical reconfiguration in a controlled and reversible manner,” Niroui explains.

When the researchers apply a voltage to the device, the two metal plates attract to one another, compressing the soft material and altering the electrical current flowing through the device. 

“PDMS is viscoelastic, which means that after being compressed, it takes time to return to its original state. This allows the devices to dynamically remember the history of forces and voltages applied to them, and convert that history into an electrical response,” says Satterthwaite.

They researchers used this performance to demonstrate an artificial neuron.

Brain-inspired information processing

In the brain, each neuron accumulates an electrical charge a little bit at a time until it reaches a threshold and fires, passing information to other neurons in the network. 

The researchers’ device mirrors this behavior. As voltage is applied over time, it accumulates stimulus as the electrodes gradually compress the PDMS. After crossing a threshold, it “fires” like a neuron before relaxing back to its original state.

“We have this complex functionality, which is the basis of biological computing, all contained in one nanoscale device,” Satterthwaite says.

Since computing and memory are incorporated within a single device with no need for external components, like capacitors or complex circuitry, this platform can achieve high energy efficiency with a small footprint. 

“The performance highly relies on the memory introduced using the soft polymer. We can intentionally engineer this over a large design space to meet the requirements of the desired applications,” Spector says.

The device can also be compatible with biological systems, Spector adds. For instance, it could be useful in applications like smart prosthetics that can rapidly process tactile data or low-power wearable patches that collect and analyze health indicators in real-time.

In the future, the researchers want to expand this work to further integrate sensing with computing and memory to realize nanomechanical computing matter with applications in intelligent and adaptive systems. 

This work was funded, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), an MIT EECS MathWorks Fellowship, and the Netherlands Organization for Scientific Research. Device fabrication was carried out, in part, using MIT.nano facilities.

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New AI technique could make minimally invasive surgeries safer and more precise

Researchers created a new technique that accurately and rapidly matches X-rays captured during surgery with a patient’s preoperative 3D medical scan. This method could make it easier for clinicians to precisely pilot minimally invasive surgical tools, leading to faster and safer procedures.

Clinicians perform many minimally invasive surgeries using real-time X-rays to help them steer devices like catheters and endoscopes through tiny incisions. But since X-rays are flat images, it can be challenging to determine exactly where surgical tools are located and oriented within the patient’s body, increasing the risk of complications.

To help localize surgical devices, clinicians may manually align X-rays with preoperative 3D medical images, such as CT scans or MRIs. Artificial intelligence tools designed to streamline this process struggle to align images robustly for all patients, making them infeasible in practice.

This new system, developed by scientists and clinicians at MIT and collaborating institutions, uses an AI model that adapts to each patient in only about five minutes. The model automatically matches one patient’s X-rays with 3D scans in a matter of seconds, and with sub-millimeter precision.

Named xvr (which stands for X-ray volume registration), it outperformed existing AI methods by an order of magnitude across a wide range of patients, body parts, and medical procedures.

“A majority of Americans live more than an hour away from a center that can perform noninvasive procedures, like emergency stroke interventions. An hour in stroke time is incredibly substantial. Making these procedures easier by combining 2D and 3D information enables these types of highly specialized life-saving procedures to be more accessible to much broader parts of the population,” says Vivek Gopalakrishnan, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); a recent graduate of the Harvard-MIT Program in Health Sciences and Technology; and lead author of a paper on xvr, which appears today in Nature.

He is joined on the paper by his advisor Polina Golland, the Sunlin and Priscilla Chou Professor of Electrical Engineering and Computer Science (EECS), a principal investigator in CSAIL, the leader of the Medical Vision Group, and co-senior author of the paper; and Neel Dey, a former postdoc in the Medical Vision Group who is now an investigator at Harvard Medical School and Massachusetts General Hospital as well as co-senior author on the paper. Additional co-authors include David-Dimitris Chlorogiannis, a researcher and clinician at Harvard Medical School; Andrew Abumoussa, a neurosurgeon at St. Luke’s Marion Bloch Neuroscience Institute; Anna M. Larson, a pediatric clinician at Shriners Children’s Hospital; Nazim Haouchine, an assistant professor of radiology at Harvard and Brigham and Women’s Hospital; Darren B. Orbach, a physician and scientist at Boston Children’s Hospital; and Sarah Frisken, an associate professor of radiology at Harvard.

Making X-rays more informative

In many minimally invasive surgical procedures, like angioplasty to open blocked arteries, clinicians insert instruments through a tiny incision and use a high-speed mobile X-ray scanner to generate images that allow them to visualize the procedure from any angle. 

But to guide surgical tools without accidentally damaging other tissue, clinicians must align real-time X-rays with the patient’s preoperative MRI or CT scan. This process, called registration, helps them determine where the tool is in relation to anatomical structures. 

“It takes decades of training for a clinician to become skilled enough to see grainy, 2D images and understand how everything is oriented. We want to make these 2D X-rays more informative, so it becomes safer and easier to do these life-saving procedures,” Gopalakrishnan says.

Manual registration methods are slow and burdensome, requiring the clinician to guess the position of a surgical instrument by punching numbers into a computer or clicking anatomical landmarks on a screen. 

To streamline the process, researchers are developing AI models that can predict 2D/3D registration. But people have such diverse anatomy that a model which works well for some patients may fail for others. 

A lack of high-quality annotated medical image data makes it difficult to train a deep-learning model robust enough to adapt to many patients, Gopalakrishnan says.

Rather than trying to make a machine-learning model that can be applied to all patients, the researchers built a model designed to adapt extremely well for the specific patient.

“We tailor this one specific model for this one specific patient, and it doesn’t matter if it works on other people because there will be different models for those people,” Gopalakrishnan adds.

Patient-specific machine learning

Xvr takes one patient’s preoperative 3D scan, like an MRI or CT, and uses it to generate thousands of synthetic X-rays from many angles, producing about 1,000 images each second. It uses a physics-based simulation of the X-ray process to ensure these synthetic images are realistic.

“Instead of generating data from nothing, like some types of generative AI, this physics simulation is entirely based on the CT scan or MRI from this patient. Because xvr creates patient-specific data in a purely physics-based manner, there is no room for hallucinations,” Gopalakrishnan says.

The xvr framework uses these simulated data to train an AI model that can accurately align this patient’s 2D X-rays with their 3D image scan in a matter of seconds.

But while such a registration model is highly accurate, it would take about 12 hours to train from scratch for each patient, making it impossible to deploy in an emergency. To make the process faster, the researchers used xvr to pretrain a more versatile AI system, called a foundation model, that can quickly adjust to each new patient. 

They collected whole-body 3D medical scans from more than 2,000 patients covering a wide range of ages, image modalities, and regions. Xvr used these diverse data to generate synthetic X-rays and train a foundation model to perform 2D/3D registration.

This pretrained model can adapt to a new patient in about five minutes, and performs registration with the same accuracy as if it had been trained from scratch. 

“So now you can get patient-specific accuracy but also in a very rapid time frame,” Gopalakrishnan says.

The team tested the model on the largest available dataset of real 2D/3D registrations, incorporating data from five hospitals that covered dozens of bones and organ systems in adult and pediatric patients. 

Xvr significantly outperformed other AI-based methods in accuracy and robustness, while operating fast enough for emergency surgeries. The model could also be used to improve the performance of robotic surgery technologies. 

In the future, the researchers hope to focus on making xvr faster for real-time deployment, conducting further studies to verify its reliability in additional situations, and extending the system to handle more complex scenarios, like moving body parts. 

“For the past two years, we’ve been carefully developing this algorithm and validating it. Now, we are collaborating closely with surgical robotics companies and clinical groups to turn this research into useful tools for navigation or deployment,” Gopalakrishnan says.

This work was funded, in part, but the National Institutes of Health (NIH), the MIT CSAIL-Wistron Program, the MIT-IBM Computing Research Lab, the MIT Jameel Clinic, the MIT Health and Life Sciences Collaborative, and the Chou Family Transformative Research Fund.

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Measure by measure, studying society accurately

Let’s agree at the outset the world is a complicated place, and social scientists have exacting jobs when it comes to measuring civic phenomena with precision. 

After all, even careful studies raise follow-up questions: How much do their findings apply in other settings? Do conclusions about politics in one country apply to other countries? If you’re studying voters in a lopsided election, will your findings apply to voters in a close election? Those questions are all a natural part of the research process.

That’s where Naoki Egami comes in. Egami is an MIT political scientist whose specialty is the methodology of research. He carefully scrutinizes, for one thing, what social scientists call “external validity,” whether the results of particular studies apply more generally.

“I always say political methodology is the field where you ask questions as a political scientist, but then you solve them like an applied statistician or an applied computer scientist,” Egami says. “You find out the underlying mathematical problems behind the empirical challenges people face, and solve them optimally.”

As it happens, Egami’s interests range widely. Years ago, before the current artificial intelligence craze, he started studying what happens when AI tools are introduced into studies. How accurate are they? How can researchers account for AI tendencies? Focusing on these and other questions has helped Egami build a broad portfolio of research, win awards, and flourish in his career. All the while, he retains interest in basic questions about politics, as well as measuring things correctly. 

“You need both perspectives,” Egami says. “If you only think about technical statistical theories, you might not work on interesting empirical problems sometimes. But if you only think about problems, you won’t really solve them optimally; you’ll solve them in an ad-hoc way. So, you really want to have both lenses.”

Egami joined MIT’s Department of Political Science as an associate professor with tenure in 2025. He is also a faculty affiliate of the Statistics and Data Science Center at the Institute for Data, Systems, and Society (IDSS).

Workshopping his career

Almost anyone who likes their job has experienced some good fortune in finding it. Egami’s case calls to mind those adages about luck being a mixture of preparation and opportunity. 

Egami grew up in Tokyo and attended the University of Tokyo. He was good at math and physics, but he also liked political philosophy and was unsure how to combine his interests. One day, Egami attended a workshop about U.S. graduate school, which he thought was about MBA programs. Actually, it was about PhD programs, and included a political scientist talking about using math in the field, so Egami asked her a question. 

“The miracle is: That workshop had 200 people in it, and after it was done, I was packing my stuff to go home, and the panelist, who was a PhD student, came down from the stage and found me,” Egami recalls. “She asked, ‘Are you the one who said you’re interested in political science in the U.S., and likes math?’” 

She invited Egami to what he thought would be another career workshop, the following week. Once again, he was mistaken.

“I showed up, and it was an academic seminar,” Egami continues. “There were only 20 people there. It was 19 professors, and me, a first-year undergrad.” Then a professor named Kosuke Imai, now at Harvard University, gave a talk about his own research on using statistics in the social sciences. 

“I was super-excited and felt if I could do even 20 percent of that, it would be a dream,” Egami says. “I talked to Kosuke and said, ‘I want to do what you’re doing.’ He probably thought I was just a random person.”

Egami, thus bolstered, started pursuing the goal of becoming a political scientist. He received his BA after spending a year as an exchange student at the University of Michigan, and applied to graduate schools in the U.S., landing at Princeton University — where Imai eventually became one of his advisors. Working with Imai, Rafaela Dancygier, Brandon Stewart, and others, Egami generated papers on methodological topics like external validity — and found substantial interest when he presented them. 

“That was a case where the audience or market told me what I should really work on,” Egami says. After earning his PhD from Princeton in 2020, he joined the faculty at Columbia University, moving to MIT five years later. 

Enjoying the spirit of MIT

One of the hallmarks of Egami’s work is very close scrutiny of the factors that can influence the results found in empirical studies. 

“In statistics, you talk about whether the people in the data are similar, meaning the population data,” Egami says. “But in political science, there are a lot of differences in context.” 

Consider the question of how much political campaigns sway the minds of voters. Political scientists have sometimes received permission to conduct field experiments in active political campaigns. That’s a significant step toward generating robust results. And yet, not all campaign settings are the same. Politicians may let researchers in when they expect to triumph, and the dynamics in those races might differ from close races. 

“It’s great to do field experiments, and that’s usually where people are allowed to do research,” Egami says. “It’s where politicians know they can win. But most of the time, we’re interested in the battlefield races, the politically competitive districts. And the logic and voter behaviors can be different in those cases.” 

Egami’s job, on one level, is to spot such differences and make other researchers aware of them.

Meanwhile, he has also developed a strong interest in scrutinizing the tools of machine learning, as applied to the social sciences. This predates the elevated interested in AI generated by ChatGPT, starting in late 2022. Some of Egami’s work explores how to systematically identify errors introduced by AI tools and then account for this issue when using AI in research.

“In the past, social science data is something we carefully collect and take a long time to really validate before we analyze it,” Egami says. “But if the generation of data is changing. If people use AI to generate data at scale, it can have errors. So I was already thinking: You want to have statistical methods that take into account these errors, otherwise many of the analyses will not be able to be replicated. That’s how I started to work on a lot of things about AI.” 

All of this has brought Egami recognition and honors in the field. Last year, he received the Emerging Scholar Award from the Society for Political Methodology. He has also been the recipient of best paper awards from the American Political Science Association’s sections for political methodology (in 2019 and 2025), experimental research (in 2024), and political networks (in 2022). Earning awards in three subfields of the discipline speaks to Egami’s scholarly versatility.

In his view, though, the work he does in different areas is ultimately aligned. 

“All these things are in parallel,” Egami says. “I’m trying to start a new research agenda every three to four years. That helps me learn new topics and be motivated.”

Further motivation, he says, comes from being at MIT and liking the experience.

“I already knew MIT was an amazing place I would enjoy,” Egami says. Even so, in his time at MIT, he says, he has gained even more appreciation for the “spirit of engineering,” in the sense of working systematically on solutions to ongoing problems, among other things. In any case, Egami has found the Institute to be a stimulating and congenial place to do his work. 

“People are really nice at MIT,” says Egami, who has been teaching both undergraduate and graduate classes.

He adds: “The Department of Political Science is really high-functioning, people are intensive in terms of their work, but it’s just genuinely nice people.” 

And, yes, that’s one claim about the world Egami does not have to double-check. 

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How MIT student communities help develop lifelong skills and connections

At the beginning of their first year, many MIT undergraduates choose to join one of the Institute’s 44 fraternities, sororities, or independent living groups (FSILGs), some of which are housed across Cambridge, Boston, and Brookline, Massachusetts.

There are 30 fraternities, nine sororities, and five independent living groups for students to choose from. Nearly 37 percent of undergrads join an FSILG, and these communities offer students more than a place to live, eat, and socialize; they are places where students create friendships, mentor younger students, work with both alumni and MIT administrators, raise funds for local charities, and learn valuable leadership skills.

While each organization has its own set of values, traditions, and membership process, they all share a common goal: creating communities where students can grow both personally and professionally inside and outside of the classroom, while navigating the rigors of an MIT education.

Anya Kattef ’98, director of FSILG Alumni Programs, says, “I can't imagine my MIT experience — or the decades that followed — without the extraordinary community I found in Alpha Phi. Surrounded by smart, compassionate, and driven women, I gained the confidence not only to survive MIT's demanding academic environment, but also to grow as a leader, progressing through the officer roles of athletic chair, house manager, and ultimately president. Beyond the leadership opportunities, the mentorship I received from upperclassmen helped me secure my first summer internship, navigate course selection, and pursue opportunities I might otherwise have overlooked. And perhaps most meaningfully, the friendships I formed through Alpha Phi while at MIT have grown into lifelong bonds that continue to shape and enrich my life.”

Service is a common bond

Liz Jason, associate dean and director of FSILGs at MIT, says “although every organization is unique and has its own personality, service remains a common thread throughout every fraternity and sorority. Many national organizations partner with causes ranging from heart health research and children's hospitals to literacy initiatives. Local chapters then build additional partnerships with organizations throughout Greater Boston, supporting causes such as Rosie's Place, the Boston Area Rape Crisis Center, animal welfare organizations, and other community nonprofits.”

FSILGs often host signature fundraising events tied to philanthropy, while others organize volunteer opportunities throughout the year, such as cleaning up Back Bay alleys, so that it’s woven into the members' experience.

Jason also notes: “Our culturally based fraternities and sororities place a particularly strong emphasis on community service, with some requiring prospective members to demonstrate volunteer work before joining. In addition, some of our national organizations require students to complete at least one semester of college before joining to ensure they have established academic success first.”

Leadership and responsibility

Presidents and leaders of an FSILG take on a large amount of responsibility that goes beyond the scope of organizing social events or fundraisers. They’re managing organizations that function much like a small business.

“Leaders learn soft skills overseeing budgets, coordinating recruitment, mentoring new members, organizing educational programming, and often spend 10 or more hours each week fulfilling leadership responsibilities,” says Jason. “Leaders also learn conflict resolution while navigating disagreements among members or neighboring residents. They practice delegation, budgeting, prioritization, and time management. They gain experience running meetings, communicating with alumni volunteers, and working with senior Institute leaders. They have a seat at the decision-making table. As a leader, if you expect your peers to do something, you need to model and espouse that behavior, too.”

For students living in chapter houses, the responsibilities can extend even further. Leaders learn to manage multimillion-dollar properties. Student leaders coordinate building maintenance, communicate with vendors, oversee safety inspections, organize chores, and help maintain properties that, in some cases, have housed MIT students for more than a century. The student house manager manages the facility, attends training four times a year, where FSILG leadership goes over seasonal items they need to know, such as removing snow from sidewalks and steps, liability insurance, and safety inspections.

As the chapter president of Pi Beta Phi, senior Tea Picconatto says, “My role as president has strengthened my communication, leadership, and conflict-resolution skills. It has also connected me to the broader national organization and provided opportunities to build relationships with members and alumnae across the country. From a professional perspective, the experience has been valuable in demonstrating leadership and responsibility to future employers. I’m certain I was hired for two of my internship roles because of my sorority leadership experience.”

Picconatto adds, “Greek life offers a unique sense of identity, community, and connection to a nationwide network of members and alumnae that continues well after graduation in a way that is not replicated elsewhere on campus. My sorority sisters have always been there to offer emotional support, academic guidance, and encouragement whenever I have needed it.”

At MIT, Alpha Delta Phi Society is a gender-inclusive member of the Institute’s Interfraternity Council. As president, Gabriel Tian, who came to MIT from Toronto, Ontario, sought a community with which to experience MIT. During the first week of school, he was studying at the ADPhi house library late at night and said it felt very natural and productive. He says he thought “this is where I belong,” and pledged shortly after. Tian quickly became involved as academic chair and vice president, and even helped update the chapter's website.

“I have learned so much since Rush — how to be a leader, how to make difficult decisions, how to have hard and personal conversations, how to run a living community with an executive board, how to socialize more effectively to connect with each and every member. Being the president, or any other leadership position, is tough, but so incredibly valuable, and gives me confidence in myself and my ability to care of my community,” says Tian.

“In just two years since joining, I have made lifelong friends. In fact, some of the closest friendships in my life are right here in the siblinghood. The web of connections my chapter offers really enables connections between people who otherwise would not have the opportunity to have even met at MIT. To me, ADP makes MIT all the more brilliant and special.”

After graduating

Long after graduation, many alumni continue volunteering with their chapters, serving on house corporations, mentoring students, and helping preserve traditions for future generations. For many, the relationships formed during college continue throughout their professional and personal lives.

Some families even span multiple generations of FSILG life, with parents and children joining the same FSILG years apart. Others have found lifelong friendships — or even spouses — through their chapter experience.

Cecilia Warpinski Stuopis ’90, the chief health officer at MIT Health, found that when she joined the Alpha Chi Omega sorority while a student, she had an “instant group of peers.”

“I was trying out for the volleyball team, and my teammate invited me to Rush to see what it was about. We both were invited to join. There were about 20 of us in our pledge class, and perhaps 40 women total in the sorority at the time, and I’m still friends with many of my Alpha Chi sisters to this day. We’re a very tight-knit group. Sororities are a very supportive network of people who care about each other.”

Stuopis, whose husband also graduated from MIT, adds, “I became reengaged with the community at MIT as an alum, as a volunteer, and then an advisory board member for Alpha Chi Omega. My daughter came to MIT and pledged Alpha Chi, too, and this allowed me to attend her initiation. I’ve been on the Board of the Association of Independent Living Groups at MIT for the last nine years and recently signed up for another three. At MIT, fraternities or sororities are not like they are portrayed in the movies. They are guided by friendships, developing bonds, and are there to support all aspects of their member’s success — both during their time as students and well into the future.”

Students interested in joining an FSILG can find more information on the website.

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3 Questions: Putting nuclear waste into perspective

For decades, one of the major complaints about nuclear power in the United States has been the argument that, after all this time, we still have not come up with a dependable strategy for sequestering high-level radioactive waste, including spent fuel from plant operation. This issue is of such importance that Haruko Wainwright has put it at the center of her research agenda as an Atlantic Richfield Career Development Professor in Energy Studies at MIT and an associate professor in the departments of Nuclear Science and Engineering and Civil and Environmental Engineering. 

In an essay called “The best-managed industrial waste in history,” which appeared in the Aug. 27 issue of the journal Nature, Wainwright made a bold statement, maintaining that an expansion of the nuclear power sector in the United States will benefit the environment, despite the fact that a solution to the permanent disposal of nuclear wastes has yet to be demonstrated in this country. 

In this interview, Wainwright describes risks associated with different forms of waste, ways to improve waste-handling procedures, and what lessons other countries can teach the U.S. in this realm.

Q: Why do you think chemical contaminants pose a greater public health risk than radioactive wastes?

A: I’ve always appreciated the fact that the dangers of radiation were recognized relatively early in the 20th century, prior to the widespread use of nuclear technologies. By the time an industry emerged, radiation protection standards were reasonably well established, including waste management. While nuclear power plants inevitably produce highly radioactive spent fuel, it is both solid and compact, making it relatively easy to contain and isolate from the environment. It took time to develop a disposal solution because people were pursuing a perfect one. Now, several countries are demonstrating that effective isolation over geological timescales is feasible. Finland, in fact, is about to open the world’s first deep geological repository for spent fuel.

Chemical contaminants present a different story. For many substances, like hexavalent chromium and PFAS (“forever chemicals”), the risks were identified long after they’d been released, having spread widely through the environment, food chains, and human bodies. PFAS, for example, has been used in industry and consumer products since the 1940s, yet the first federal drinking water standards were not adopted until 2024. Chemical hazardous wastes — including substances that degrade very slowly or not at all — are disposed of in the shallow subsurface without the requirement of long-term predictive assessments.

This is not to suggest that radioactive wastes are without risk. However, public perception is often disproportionately focused on — often hypothetical — nuclear hazards, while underestimating the dangers posed by chemical wastes. This misalignment actually has an adverse effect on the environment and public health. It leads to the misallocation of resources, diverting funding — including taxpayer dollars — away from worrisome contaminants whose environmental and public health consequences are already occurring. 

Q: How can we improve our procedures for storing spent fuel as more nuclear power plants come into operation around the world?

A: The nuclear industry is becoming increasingly proactive about waste management. Some companies, for example, now incorporate spent fuel storage capacity directly into their power plant designs, formulating plans that cover the entire operating period. Research on waste streams from advanced reactors — and even fusion reactors — is also growing. This approach of thinking about wastes before any are produced — what I call “design from the wastes up” — is critical for long-term sustainability.

Although further technical advances are surely needed, communication remains another area with significant room for improvement. Transparent monitoring programs and effective communications have been shown to build public confidence and provide assurance. Additionally, I believe we should place a greater focus on the inherent properties of radionuclides, including their risk pathways and mobility. Long-lived radionuclides are weakly radioactive and emit little or no penetrating radiation; their health risks are associated with ingestion or inhalation, analogous to chemical carcinogens. Most radionuclides, including plutonium, have low solubility and a high affinity for soil particles, limiting their mobility in the environment.

Current research on spent fuel storage has been devoted mainly to the integrity of the metal canisters used to contain spent fuel. Attention should also be directed toward developing predictive understanding of radionuclide transport and about geochemical barriers to the spread of radioactivity in the unlikely event of a containment breach. These approaches would exploit the natural immobility of radionuclides to afford additional layers of protection — in keeping with the nuclear industry’s recent embrace of passive safety features.

Q: How can the United States move toward the permanent disposal of nuclear wastes, and what can we learn from the European and Canadian examples? 

A: Many people tend to dwell on political and social issues, while the underlying science is frequently left out of the conversation. Fundamental questions — regarding the true dangers of radioactive materials and the feasibility of safe geological disposal — often go unanswered, leaving nuclear waste a vague, almost mythological threat, rather than a technical and engineering problem.

In fact, many people in geoscience believe that the failure of Yucca Mountain — the proposed geological repository for high-level radioactive wastes in the U.S. — stemmed from the fact that the site was chosen for political rather than scientific reasons. In 1987, Congress amended the Nuclear Waste Policy Act to confine site characterization to a single location, abandoning the original plan to screen multiple candidates. This top-down decision, widely dubbed the "Screw Nevada Bill," generated vehement local opposition. In addition, Yucca Mountain is the only proposed repository in the world situated above the groundwater table and within a zone of fractured igneous rock, where radionuclides are relatively mobile. Demonstrating its long-term safety is, consequently, much more difficult than for other proposed repositories.

Europe's approach to waste disposal offers a stark contrast. Switzerland, for example, identified a preferred site after a transparent, scientific evaluation of multiple candidates based on technical criteria, earning community acceptance as a result. Sweden and Finland built trust through decades of patient consultations with the public. And in Canada, more than 10 communities voluntarily expressed interest in hosting a repository before one favored site was ultimately selected.

Another underappreciated difference relates to how public concerns are handled. In the U.S., worries about radiation and radioactive waste have often been brushed aside by experts. In Europe, communication professionals and experts are trained to address every concern sincerely, offering understandable, science-based explanations. Discussing those concerns, moreover, can provide valuable opportunities to identify knowledge gaps and improve safety.

I believe that selecting a geologically sound site and communicating the science clearly — in terms that anyone can grasp — are the essential first steps toward achieving the permanent and safe disposal of nuclear waste.

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Marine bacteria team up to break down one of the ocean's toughest carbon-storing molecules

Deep in the ocean, brown algae and diatoms produce a complex carbohydrate molecule called fucoidan, which helps form the algae's protective outer layer. The fucoidan molecule is very difficult for microbes to break down because its chemical structure may include dozens of different linkages and branching patterns that vary from one algae species to another. This resistance to decay is one reason why fucoidan matters; when microbes struggle to break it down, fucoidan can sink deep into the ocean, carrying carbon with it and potentially storing it for long periods. This could make fucoidan an important player in the ocean’s carbon cycle.

For many years, scientists knew of individual bacteria that could break down pieces of fucoidan. But one fundamental question remained unanswered: Could a microbial community break it down completely, and if so, how?

A new open-access study published in Nature, led by Andreas Sichert, a former MIT postdoc now at ETH Zurich, and Otto X. Cordero, associate professor of civil and environmental engineering at MIT, provides an answer.

"No single bacterium can finish the job," says Cordero. "Instead, fucoidan is degraded through teamwork. Different bacterial strains specialize in different parts of the molecule, and together, their combined efforts get the job done far more efficiently than any one organism could manage alone."

A puzzle with 453 pieces

In order to understand how fucoidan breaks down in nature, the research team enriched a fucoidan-degrading bacterial community from coastal seawater samples. What they found was staggering: more than 453 different genes, each responsible for making an enzyme that can act on fucoidan, spread across eight bacterial strains the researchers isolated. On their own, none of these strains could fully break down the molecule.

But when the researchers used a new, rapid mass-spectrometry method, they were able to observe how bacteria consumed individual sugar building blocks — and a clear pattern emerged. All of that genetic complexity could be reduced to two roles. Some bacterial strains specialized in degrading fucoidan's fucose-rich "backbone," while others specialized in removing its side branches, which contain less-common sugars such as xylose and galactose.

When strains playing both roles were combined, something noteworthy happened: degradation didn't simply add up. Instead, it became synergistic and exceeded what the bacteria's individual activities could predict. The more complementary the strains' preference for sugar were, the stronger the effect became. In some cases, the paired communities came close to completely degrading the complex polysaccharide.

"The breakdown of one of the ocean's most abundant carbon pools rests on a division of labor," says Cordero, "not between particular strains, but between functional roles."

Turning complexity into predictability

The most surprising result was that this division of labor made the system much more predictable than its underlying complexity indicated.

The researchers developed a simple model that sorted bacterial activity into two broad categories: fucose, and the rarer sugars found in fucoidan's side chains. They trained the model using data from small communities containing just one to three bacterial strains.

The simplified model was able to predict degradation in communities containing up to seven strains, and its predictions also generalized to nine structurally different fucoidans from other kinds of algae.

"A predictive understanding of a complex system need not come from characterizing each of its parts," adds Cordero, "but from finding the right simplification." The finding suggests that scientists may be able to predict how efficiently other complex, carbon-rich biological materials are broken down in nature, even when their exact chemistry and the enzymes involved are only partly understood.

The researchers also found that bacteria with complementary capabilities often occurred together in samples taken from the natural ocean, suggesting that the division of labor observed in the laboratory may also play a role in the ocean.

The consequences extend well beyond the field of microbiology. 

The researchers propose a concept they call "diversity-limited degradation," in which the absence of the right combination of complementary bacterial specialists allows fucoidan to persist for longer instead of being broken down. This concept may help explain why some algal carbon stays in the ocean for extended periods, contributing to long-term carbon storage.

For biotechnology, the takeaway is more straightforward. Instead of engineering a single "superbug" that can digest tough and complex biomass, a more promising approach may be to bring together teams of microbes that already specialize in complementary tasks. These teams could potentially be used to process brown algal biomass and other complex polysaccharides on a larger scale.

Looking ahead

The broader promise, though, may lie in the approach, rather than the molecule. If hundreds of uncharacterized enzymes can be reduced to two measurable traits, the same strategy might work for other biopolymers whose chemistry has so far resisted description — and, more generally, for predicting what microbial communities do. 

"Here was a system with hundreds of enzymes acting on a molecule we still can't fully describe, and it turned out to be far more tractable than anyone expected," says Cordero. "What we found is that there's a level of organization above the individual enzyme, corresponding to traits we can measure and plug into simple models that predict function from (genomic) composition. When biology looks intractable, it may be that we haven't found the right level of description yet."

One question the work leaves open is a fundamental one. Fucoidan is abundant, and has been for a very long time, so why has no bacterium evolved to eat it whole? The researchers suggest answers on two levels: constraints within sugar metabolism itself, and evolutionary dynamics in which complementary specialists are continually regenerated rather than merged into one.

"Really, this is a question about how life on Earth is organized," says Cordero. "Why are the biochemical functions that drive the planet's elemental cycles distributed across many organisms instead of concentrated in a few? Explaining that is, I think, one of the frontiers of the life sciences."

In addition to Cordero and Sichert, the research team included co-authors from ETH Zurich, the University of Vienna, and the Tata Institute of Fundamental Research.

The work was supported by Simons Foundation through the Principles of Microbial Ecosystems (PRIME) collaboration.

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