Fueling a return journey from Mars

When Lanie McKinney was 3 years old, her parents stopped at a massive meteor crater during a road trip through the U.S. Southwest. As they prepared to leave, McKinney began to protest.

“I want to wait here for the next one,” she told them.

She didn’t yet understand that another meteor wasn’t likely to land in exactly the same spot. But the story, which her parents still tell, captures a fascination that has remained with McKinney throughout her life.

“I just always remember being captivated by space and what is out there,” she says.

Today, McKinney is entering her fifth year as a PhD candidate at MIT, where she works in the Aerospace Plasma Group with Esther and Harold E. Edgerton Associate Professor Carmen Guerra-Garcia. McKinney’s research focuses on developing technologies that could help humans explore Mars.

One of the challenges of sending humans to the Red Planet is figuring out how to supply them once they arrive — including how to enable their journey back home. Rather than transporting everything from Earth, McKinney is interested in using the resources already available on the planet, a concept known as in-situ resource utilization, or ISRU.

“If we don’t build gas stations on Mars, it will be very difficult to get humans back to Earth,” she says. “We’re going to need some way to produce the propellant on site.”

McKinney’s research uses cold plasma to convert carbon dioxide, which is abundant in the martian atmosphere, into oxygen and carbon monoxide, a technology that could eventually be used to produce life support and propellant on Mars. 

An Oklahoma native, McKinney earned her bachelor’s at the University of Tulsa, where she studied physics and applied mathematics. She had initially expected to pursue astrophysics, but a summer research internship at the University of Colorado at Boulder introduced her to plasma physics through a project involving dusty plasmas in the lunar environment. 

“I thought it was an incredibly interesting problem,” she says. 

At MIT, McKinney has developed a small reactor that can convert carbon dioxide into oxygen and other products. The challenge now is separating out the oxygen before it recombines.

“We can actually perform the conversion step really well,” she says. “But what happens in a plasma is we convert it, and then we get a mixture that needs to be separated.”

Her current work pairs the plasma reactor with an oxygen-selective membrane designed to extract oxygen rapidly. The integration process isn’t well-understood, leaving McKinney and her colleagues with questions about how the reactive plasma environment will affect the membrane.

“We are not entirely sure what we will see,” she says.

For McKinney, the possibility of connecting laboratory experiments to future human missions is what makes the work particularly rewarding.

“I get to work in a really cool lab and develop exciting experiments,” she says. “I get ownership over an entire experimental system, and then I get to connect that to performance requirements for a future Mars system. That’s just the dream.” 

That same philosophy has shaped McKinney’s work beyond her thesis. Through MIT’s Space Resources Workshop, she has participated in NASA competitions focused on sustaining humans in space. Her first competition involved designing a self-sustaining Mars mission for 10 years.

“I had no clue what was going on,” she says. “I  didn’t know anything about space systems. So, my mentality was, let me jump in and learn.”

She later co-led MIT’s CERBERUZ team for NASA’s LunaRecycle Challenge, which asked teams to develop ways to recycle waste on missions to the moon and deep space. The MIT team recently won first prize in Phase 2, receiving $775,000 in awards for a system that grinds mixed trash into powder that can be reused via injection molding to make spare parts and 3D-printing filament. 

Another project McKinney enjoyed brought together engineers and architects through MAS.S66/4.154/16.89 (Space Architecture) to tackle a different problem: how to protect lunar habitats from radiation using only resources available on the moon. The students’ solution was to produce cast bricks from lunar regolith that could be stacked without mortar or another binder. For McKinney, the project demonstrated the value of bringing together people with different expertise.

“The kinds of innovative solutions that can be discovered when you work on a team that brings together different expertise and experiences was one of the project’s major takeaways,” she says.

The experience reflects a broader lesson McKinney has taken from MIT: Research may involve focused individual work, but solving the problems of human space exploration will require collaborations across disciplines.

“I feel like I have learned so much from being a part of these different teams,” she says. 

McKinney sees that collaboration as essential to the future she hopes to help build. Reaching the Moon and Mars is only the first step: “What comes next is building up a permanent presence so that we can do amazing science and be really effective at exploration,” she says.

McKinney’s fascination with exploration extends beyond her research. She is an avid hiker and mountaineer, having grown up hiking with her family in the Rockies. She recently completed a mountaineering course in Alaska and summited Mount Baker in the Cascade Range. She sees a connection between those adventures and the curiosity that first drew her to space.

“I love to explore and go on adventures,” she says. “And space is the ultimate thing you could explore.”

That curiosity has also shaped how McKinney approaches her work. When she arrived at MIT from the University of Tulsa, she initially felt intimidated.

“I thought that it was a fluke that I’d gotten in,” she says. “I was very nervous that I was not going to measure up to the environment.”

Over time, she learned to approach unfamiliar problems by asking questions and committing fully to whatever interested her.

“If something interests you, try it and go all in,” she says.

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Understanding the world, from the Cold War to the age of AI

At a moment when global alliances are shifting, technological change is accelerating, and the boundaries between science and geopolitics are dissolving, understanding the world demands new ways of thinking. 

For 75 years, the MIT Center for International Studies (CIS) has helped meet that challenge — bringing together engineers, social scientists, and policy practitioners to confront the most pressing global challenges of their time. From developing the foundations of modern international security to redefining how the United States engages with the world, CIS has not only studied global affairs, it has helped shape them. 

What distinguishes CIS is not just the scope of its work, but the way it approaches it. 

At MIT, international studies does not sit apart from science and technology, it is embedded within it. This proximity has enabled generations of scholars to tackle geopolitical problems with tools and perspectives rarely found in traditional academic and policy environments. 

“Being situated within the world’s leading technical institution enables a lot of exciting possibilities,” says Evan Lieberman, the director of CIS and the Total Professor of Political Science and Contemporary Africa. “We focus on critical problems in international development and security — always with an eye towards the challenges and opportunities presented by technological change. Beyond that, a big part of our mission is to provide global perspectives and engagement avenues relevant to scientists and engineers.” 

Established during the dawn of the Cold War, CIS pioneered a new understanding of global power: that science, technology, and geopolitics were becoming deeply intertwined. From the beginning, it convened faculty across disciplines — economics, political science, engineering, and beyond — setting a template that has since become a model for institutions around the world. Over the decades, this approach has produced an outsized impact. 

In 1961, a memorandum to President John F. Kennedy from MIT economist Max Millikan — the inaugural director of CIS — helped inspire the creation of the Peace Corps, fundamentally reshaping how the United States engages in global development. 

CIS scholars such as Lincoln Bloomfield and William “Bill” Kaufman played a central role in establishing security studies as a rigorous academic field in the late 1950s. Less than two decades later, Jack Ruina and George Rathjens founded the center’s Arms Control and Defense Policy Program (now known as the MIT Security Studies Program), which has influenced generations of policymakers and trained generations of scholars.

The study of modernization and political development has also long been central to the work of the center, with notable luminaries such as Lucian Pye and Myron Weiner helping to lead the way. 

A legacy of global exchange 

At the same time, CIS has reshaped how knowledge flows across borders. The MIT International Science and Technology Initiatives (MISTI), launched in 1983 by Institute Professor Suzanne Berger, has sent thousands of MIT students abroad to work, study, and conduct research alongside international partners — experiences that extend far beyond traditional study abroad. In doing so, it helped change longstanding assumptions about the United States’ role in the world, demonstrating that learning is most powerful when it is reciprocal. 

That ethos of mutual exchange continues to define CIS today. Through initiatives such as the Global Seed Funds, MIT faculty, researchers, and their students collaborate with academic partners around the world to advance shared research agendas. 

The connection between these initiatives can be traced to Richard Samuels, Ford International Professor of Political Science and director of CIS from 2000 until 2023. His creation of the MIT-Japan Program in 1981 served as the model for MISTI. He was also the visionary behind the launch of the Global Seed Funds in 2008. 

Together, these programs reflect a consistent vision: that the strongest ideas emerge through sustained engagement with partners around the world. 

Expertise in action 

Drawing on deep regional expertise, CIS also serves as a platform for global engagement across MIT, mobilizing cross-disciplinary knowledge to respond to unfolding international crises and inform both scholarly and policy debates. 

Its MIT-MENA Program, led by Richard Nielsen, associate professor of political science, recently convened experts to assess the energy and security implications of disruptions in the Strait of Hormuz; the MIT-Ukraine Program, under the direction of Elizabeth Wood, Ford International Professor of History, brings together scientific, technical, and academic expertise to design sustainable solutions for a nation at war; and the MIT-China Program, directed by Yasheng Huang, professor of global economics and management at the MIT Sloan School of Management, is creating a hub for scholars and policy experts focused on balancing the Institute’s engagement with China. 

Scholarship that shapes security 

For decades, the MIT Security Studies Program, directed since 2019 by Taylor Fravel, the Arthur and Ruth Sloan Professor of Political Science, has been a leading incubator of ideas that have shaped debates on grand strategy, nuclear policy, civil conflict and Asian security. Its affiliated scholars, fellows, and graduate students have produced policy relevant research that continues to inform policymakers grappling with an increasingly complex international security challenges. 

Building on that legacy, SSP recently established the Center for Nuclear Security Policy (CNSP) — made possible by a $45 million gift from the Stanton Foundation. Directed by Vipin Narang, the Frank Stanton Professor of Nuclear Security and Political Science, the CNSP aims to expand MIT’s leadership in addressing one of the most urgent challenges of our time: managing the risks posed by nuclear weapons in a rapidly evolving and uncertain geopolitical environment. 

Another cornerstone of CIS’s security work is Seminar XXI, currently led by Kelly Greenhill, who holds faculty appointments at MIT and Tufts University. The annual, nine-month program brings together rising leaders from across the U.S. government, military, and national security community. In three decades, more than 2,500 participants have engaged deeply with issues such as nationalism, technological disruption, and global conflict — developing new frameworks for decision-making in high-stakes environments. 

Advancing research, expanding dialogue beyond its anchor programs, CIS continues to invest in the next generation of scholars and practitioners. Undergraduate research initiatives, postdoctoral fellowships, and visiting scholar programs — including the Robert E Wilhelm Fellowship — create space for emerging and established leaders to explore critical questions, from governance and corruption to political reform and social change. 

It also prioritizes policy-relevant research by supporting conferences, workshops, labs, and research initiatives on key problems in international affairs. 

Finally, CIS plays a vital role in connecting MIT to the broader world. Through public events like the Starr Forum, the center brings leading global voices to campus, fostering dialogue on issues that shape international politics and policy. 

The next 75 years 

As CIS looks to the future, its mission is evolving to meet a dramatically changing global landscape. 

“The moment we’re in now is so different from the Cold War era,” says Lieberman. “We’re seeing a much more complex global system, with new actors and new kinds of challenges.” 

In what Lieberman describes as CIS 2.0, the center is sharpening its focus on the forces that will define the coming decades. This includes the geopolitical implications of artificial intelligence, the future of global cooperation in an era of climate crisis, and the evolving role of the United States within an increasingly contested international order. 

Addressing these challenges will require exactly the kind of interdisciplinary, globally engaged approach that has defined CIS for the past 75 years. It will also require a renewed commitment to collaboration — across fields, across institutions, and across countries. 

“A key source of our value added is to convene complementary sources of expertise,” Lieberman says. “It’s about bringing people together who might not otherwise be in the same room, and asking how we can have the greatest possible impact.” 

Seventy-five years after its founding, CIS remains guided by a simple but powerful idea: that understanding the world — and improving it — demands more than any single discipline, perspective, or nation can offer alone.

The CIS’s 75th anniversary symposium, taking place Oct. 15-16, will explore the defining challenges of today with leading thinkers.

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Lincoln Laboratory summer research interns take on national security challenges

Nearly 170 interns recently dispersed from MIT Lincoln Laboratory to return to their undergraduate or advanced degree programs. For Anna Raymaker and Vivek Jagadeesh, however, the work is just getting started. They are among more than two dozen interns staying on as student technical assistants, continuing to support the laboratory's national security research during the 2026-27 academic year.

"Our summer research program is a key pathway for developing talent to support defense-critical programs," says Robert Loynd, executive officer in the Director's Office. "Interns are embedded in R&D teams across nearly all mission areas, from missile defense and cyber operations to advanced communications and quantum technologies."

In 2026, the laboratory's intern program was named to Yello and WayUp's Top 100 Internship Program list and received the organizations' Public Service Award. This award recognizes programs that demonstrate exceptional commitment to meaningful intern engagement that benefits the public good. 

Anna Raymaker: Securing maritime infrastructure

Anna Raymaker found her bearings when she began researching maritime security. Four years ago, the PhD student at Georgia Tech had just started her cybersecurity studies, but hadn't yet settled on a focus area. When her advisor offered a project building a boat test bed, the Florida native was hooked.

As she began presenting her test bed research at academic cybersecurity conferences, she noticed a gap: "No one was really looking at shipping security," she says. That realization led her to speak directly with mariners to learn about the cybersecurity issues they faced.

One issue mariners repeatedly raised was the security of the Automatic Identification System (AIS), a device that helps ships avoid collisions by broadcasting their location, speed, and course. International regulations require all ships over 300 gross tons — such as cargo, tanker, and cruise ships — to transmit their identity via AIS at all times. 

"Mariners told me that AIS is their source of truth, so it was very scary when they experienced it being manipulated in the wild," Raymaker says. For example, so-called "ghost fleets" could use AIS to disguise themselves as other vessel types to evade sanctions. Such deception is possible because AIS does not require identity verification.

This summer, Raymaker examined AIS security firsthand at Lincoln Laboratory. Her goal was to analyze the trust assumptions built into the system and identify where those assumptions could be exploited. Her research revealed several methods of interfering with AIS, including radio-based "spoofing," in which false messages can appear to come from a legitimate device. Spoofed messages could, for instance, instruct ships to switch transmission channels or report a fake vessel position, potentially causing ships to change course. Working with her Lincoln Laboratory advisor, Hamed Okhravi, she then explored defenses against these false signals.

"Recent events have demonstrated that AIS security is not merely a theoretical concern, as manipulation or spoofing of maritime positioning data can directly affect navigation, safety, and global shipping. Anna's work directly contributes to understanding and mitigating these emerging risks," Okhravi says. "She built a new experimental test bed from scratch, conducted detailed experiments, analyzed the results, and helped turn the work into a publication, demonstrating excellent hands-on technical and research skills."

Raymaker says she has been both surprised and encouraged by the laboratory's collaborative culture. Mentioning her AIS project in a hallway conversation would prompt staff to offer help or connect her with relevant experts. "The opportunity to network with all these experts and see what other groups do is extremely unique. Any student would benefit from that kind of exposure," she says. 

As a student technical assistant, Raymaker will research other dimensions of maritime security. She's particularly interested in preventing the malicious cutting of undersea cables, which has become a major geopolitical security concern. "Ships are big and slow. If we have data on where they're moving, maybe we could use it to predict when a ship is going to do something bad," she says.

After graduation in the spring, she hopes to keep working through the problems she heard from mariners: "I want to go one by one down that list to create solutions that might help. Their job at sea is hard, and they deserve to be protected."

Vivek Jagadeesh: Readying cyber technology for industry adoption

Vivek Jagadeesh is a master's student at Worcester Polytechnic Institute. His path to Lincoln Laboratory came together naturally. After interviewing for a summer position, he learned that his advisor had a connection with staff in the Secure Resilient Systems and Technology Group. That connection gave him the confidence that the laboratory was the right fit for his interests. As it turned out, the group's work aligned closely with the problems Jagadeesh was tackling in his research: securing operating systems.

Specifically, Lincoln Laboratory researchers have been developing Hardware-Assisted Kernel Compartmentalization (HAKC). The core software of an operating system, a kernel typically has the highest level of access to a computer's hardware. Because of that access, a single bug in kernel code can lead to catastrophic security failures. HAKC mitigates this risk by dividing kernel code into smaller components, each separated by access-control checks. The team anticipates that the technology can resolve vulnerabilities in Linux kernels, which power most of the world's devices.

Jagadeesh's focus has been on supporting HAKC's transition to industry. "The idea is to make the technology less proprietary, so that any of the big distributors of Linux, like Red Hat, or Canonical, can use it," he says. Those distributors, however, need clear insight into how HAKC modifies the kernel code. To enable this insight, Jagadeesh developed a tool called a source-to-source compiler, or transpiler. 

A compiler converts C source code into binary for machines to execute. Different compilers process code differently, and the compiler HAKC uses differs from the compiler used frequently by the greater Linux community. Modifications to code are usually done at an intermediate stage — a translated version of the code that compilers use before generating binary — but interfacing with the code at this stage varies by compiler, making modifications hard to transfer between systems. To avoid this problem, Jagadeesh's transpiler inserts HAKC code directly into the original C source file, while preserving the source file's original information and making additions easily identifiable. As a result, any developer can audit the changes HAKC implements, and HAKC can cleanly integrate into the complicated build systems used by kernel developers and distributors.  

"Creating a transpiler is a non-trivial task, but that is nevertheless what Vivek achieved. His transpiler is capable of transforming the entire Linux kernel, a key milestone we need to bring HAKC to industry," says his Lincoln Laboratory advisor, Derrick McKee, who began developing HAKC as a student researcher himself five years ago.

According to McKee, the transpiler will serve as the foundation for the next iteration of HAKC. That new version is planned for release under the Open Resilient Compartmentalization Alliance, a Linux Foundation initiative dedicated to bringing compartmentalization technology to Linux systems.

Jagadeesh says he felt strongly supported throughout the internship, meeting with the project's two principal investigators at least twice a week. "It felt like we were working on this together in a big way — and I got a lot of support from everyone responsible for it," he says. He looks forward to working on other aspects of system security in the group this fall. 

For students considering a laboratory internship, Jagadeesh offers this perspective: "You get to work on real things that have an actual impact. It's work that, after you go back to school, you'll apply to more research going forward."

More information on Lincoln Laboratory's summer research program and other student opportunities can be found here. 

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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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