Beyond the pitch: The founder’s journey

The path to launching and growing a startup can be full of twists and turns. For a budding entrepreneur, gaining perspective from those who have already experienced the journey can be incredibly valuable, and highly inspirational. 

“There are so many amazing entrepreneurial stories among our alumni. We want to bring those stories to our students and our community and build networks with our incredible alumni founders,” says John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering (MechE). “Through the Founder’s Journey class and other new programs, we want to cultivate interest in entrepreneurship among our students and expand opportunities to bring MechE-born technologies to the world.” 

According to a 2015 report on MIT’s global entrepreneurial impact, there are more than 30,000 active companies founded by MIT alumni worldwide, employing some 4.6 million people. Marina Hatsopoulos SM ’93, founding CEO of Z Corp., an early market leader in 3D printing, said one of the aims of the course was to show students they don’t need to reinvent everything. “So much of this has been done before. I want them to understand that this is a well-trod path.” 

Class 2.S977/2.S979 (Founder’s Journey: Launching and Scaling Hardware Startups) explores real-life challenges of startups focused on building and scaling hardware technologies. First held in spring 2025, the inaugural class invited students to “find and activate their entrepreneurial energy” through the lens of challenges faced by founders and their teams at various stages in development of new hardware-focused companies — ranging from fundraising to supply chain development, and much more. 

Each week of the class was structured around a key challenge faced during the development and growth of a hardware startup, presented by the instructors and guest speaker. The speakers were founders of companies in robotics, energy, 3D printing, consumer products, and other frontier technologies. Students engaged through preparing questions for the speakers and participating in follow-on discussions and reflective exercises throughout the semester. 

Ken Zolot, senior lecturer at MIT, and Hatsopoulous co-led the class and developed it along with Hart. Hart, who was among the alumni speakers in the course’s first iteration, also spoke to the class about his experience as a co-founder of VulcanForms, which began through collaboration with fellow co-founder Martin Feldmann MEng ’14. 

The other alumni speakers included Mick Mountz (Kiva/Amazon); Jon Hirschtick (Solidworks/Onshape); Max Lobovsky (Formlabs); Elise Strobach (Aeroshield); Greg Mark (Markforged); Seemantini Nadkarni (Coalesenz); Eran Egozy (Harmonix); Renuka Babu (DOTS Technology); Davide Marini (Inkbit); Loewen Cavill (Amira); and Colin Angle (iRobot).

Colin Angle ’89, SM ’91, co-founder of iRobot

Colin Angle ’89, SM ’91, co-founder and former CEO of iRobot, now CEO and co-founder of Familiar Machines and Magic, identified a passion for building things early on. 

“This idea that you can create something from nothing, that you can have an idea and not just draw it, but build it and make it real, is something I’ve always loved,” he says. “MIT had such a strong, hands-on ethos, and that really, powerfully resonated.”

While living in the Alpha Delta Phi Fraternity house at MIT, Angle watched several companies get their start (by his count, five multimillion-dollar companies were started by his fraternity brothers during his time in the house). Seeing others do it helped to demystify the process. 

He started iRobot in his living room, beginning at first not with a product concept, but a grand vision. “We’re supposed to have robots. So, if not us, who? And if not now, when? It was a magical day.” 

iRobot may be best known for the Roomba, an autonomous robotic vacuum cleaner, but through the years the company also sent robots to Afghanistan (saving thousands of lives with the Pack Bot tactical mobile robot) and explored the Great Pyramid in Giza live on National Geographic. 

“The joy I have taken from my entrepreneurial journey has been the ability to build bigger things, from building teams to building a company capable of building something far beyond what I could have ever imagined doing myself … we created inventions that no one thought possible, simply because we believed we could.”

Elise Strobach SM ’17, PhD ’20, CEO and co-founder of AeroShield

Elise Strobach SM ’17, PhD ’20 is CEO and co-founder of AeroShield Materials. The company, co-founded with Kyle Wilke PhD ’19 and Aaron Baskerville-Bridges SM ’20, MBA ’20, develops super-insulating transparent window inserts with technology based on transparent silica aerogels developed by Strobach while she was completing her PhD in Professor Evelyn Wang’s lab.

“I wasn’t thinking of myself as an entrepreneur at that time, but looking back, that’s definitely where that seed was planted,” says Strobach. As entrepreneurs, she says, “We have the … freedom to find the best problem to solve and to continue to seek the best way to solve that problem.”

Aerogels, which were first invented almost 100 years ago and were first commercialized by NASA to insulate equipment in space, had a hazy blue tint that limited their use in certain applications. The aerogel material created by Strobach and her team is completely see-through, creating a variety of new everyday applications. The company recently achieved another milestone, with their work on display at the Smithsonian National Air and Space Museum in Washington.

“You don’t have to know everything to start. You just have to know that this is what you want to do and just get started.”

Maxim Lobovsky SM ’11, CEO and co-founder of FormLabs

Maxim Lobovsky SM ’11 was already working on 3D printers when he came to MIT to study at the MIT Media Lab. As he was finishing his master’s degree, he saw an opportunity to build something new.  

Lobovsky, with fellow Media Lab graduates David Cranor SM ’11 and Natan Linder SM ’11, founded Formlabs, a developer and manufacturer of 3D printing technology. The trio set out to build a professional-level 3D printer, but a significant cost reduction and one that would be easier to use than what was then available on the market. At a time when 3D printers could cost $100,000 or more, Formlabs’ product started around $3,000.

“We definitely built Formlabs in a classic, disruptive innovation path,” Lobovsky says. They achieved the cost reduction through several different ways, including replacing technology developed in the 1980s with modern consumer electronics components like the laser diodes that were developed for Blu-ray Disc players, and with “just a lot of clever engineering.” 

It was a long grind to raise the first round of funding, he says. The team participated in MIT’s 100K competition and pitched their idea to many potential investors (with limited success, initially). Their big break came in the form of an overheard conversation. 

“As someone who is naturally introverted, shy engineer … a really important lesson [was] that, sometimes, you can get lucky,” he says. “Sometimes talking loudly at a restaurant is actually a good way to get things going.” 

Lobovsky and one of his co-founders were having dinner with a potential investor at Legal Seafoods in Harvard Square. The pitch to the initial investor didn’t go well, but Mitch Kapor, the founder of Lotus Software and an early pioneer in the PC industry overheard the conversation, and he ended up leading Formlabs’ first round of funding. 

Today, Formlabs is the largest supplier of professional stereolithography and selective laser sintering 3D printers in the world. 

Jon Hirschtick ’83, SM ’83, co-founder of SolidWorks and Onshape

Jon Hirschtick ’83, SM ’83, co-founder of SolidWorks and Onshape, says the first time he can remember thinking about starting a company was when he was an undergraduate. 

“I had heard about startups, and it sounded like a lot of things that I was drawn to … a sense of being able to realize your vision, express yourself; a sense of excitement, of making money, and even the idea of a chaotic environment,” he says. 

Hirschtick has spent over four decades building computer-aided design (CAD) software, starting as an intern at MIT in 1981 and continuing that work today. “I thought, ‘hey, the world could use this software.’ It’ll be a better place with the software that I envisioned.”

He refers to CAD as a meta product design. “We’re designing a product that other people use to design products, and that’s just really cool to me.” 

“I think startups just fit me,” he says. “The excitement, the idea of trying to solve a lot of problems at the same time. MIT is a place of problem-solving ... and a startup is a place where there’s lots of problems to solve.” He adds that a lot of big companies are doing new things, but “startups are always doing things.”

He says most anything today that is a manufactured product is modeled in CAD first. “If you’re interested and excited by product development, then building a CAD system lets you get involved in the world’s product development.”

“Nobody knows for sure when they start a company whether it’s going to be successful or not. If it were, if there was a way of knowing for sure, then there wouldn’t be all these classes in entrepreneurship. They’d just tell you the secret. There’s always risk. Visions and hallucinations, they look and feel the same. You only find out which is which once you really try to realize them.”

A version of this story appears in the 2026 issue of MechE Connects, the Department of Mechanical Engineering’s magazine. 

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Tiny robot boats build floating structures

Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, Lego-like construction site.

Their new system, called “FloatForm,” is a swarm of small square robotic boats that assemble themselves into larger structures on the water, break apart, and reassemble into something new, all with minimal human direction. 

Each robot, about the size of a dinner plate at 21 centimeters square, is a self-contained vessel with its own thrusters, sensors, and magnetic latches. Together, they hint at a future in which floating infrastructure could become more adaptive: a temporary platform after an emergency, a market on a canal, or a stage that appears for a festival and dissolves when the crowd goes home.

“Our FloatForm projects envisions a future where the waterfront becomes a programmable extension of the city, where autonomous boats can self-organize into bridges, platforms, and other useful structures on demand,” says Daniela Rus, the Panasonic Professor of Electrical Engineering and Computer Science at MIT and director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “This kind of distributed robotics opens new possibilities for mobility, emergency response, public space, and infrastructure on water.”

“With FloatForm, we are essentially turning static water surfaces into dynamic, programmable spaces,” says Wei Wang, lead author of a new paper on the project and a former MIT research scientist who now leads the Marine Robotics Lab at the University of Wisconsin at Madison. “Imagine an urban environment where public space isn’t fixed, but can autonomously expand, contract, or reconfigure on demand.” 

“We see it as forming infrastructure on the water, using a modular system to create one larger system,” says Alejandro Gonzalez-Garcia, a former researcher with MIT CSAIL and the Senseable City Lab. “If there’s an emergency, you could form a new bridge to alleviate traffic in the city. Or you could create floating markets and floating stages. If you want a more livable city, you want to use the water, too.”

The open-access work, published today in Nature Communications, comes from the labs of Rus and Carlo Ratti, professor of practice of urban technologies and planning at MIT and director of the Senseable City Lab, and grows out of Roboat, their joint project with the Amsterdam Institute for Advanced Metropolitan Solutions that put full-size autonomous vessels on Amsterdam’s canals. Those canals once carried the city’s goods; today, they mostly carry tourists. 

“We explored whether the canals could be used for waste collection, or for transport, to offload some of the stress on the roads back onto the water,” says Niklas Hagemann, an MIT graduate student in architecture, CSAIL affiliate, and former Senseable City Lab researcher who has worked on the project since its early stages. “Urban areas are getting denser, so could you expand public space onto water that’s currently underutilized?”

FloatForm shrinks that vision down to tabletop scale to answer a harder question: How do you get dozens, and eventually thousands, of floating robots to organize themselves?

Lessons from the ant raft

The team found its answer in biology. Fire ants famously survive floods by linking their bodies into living rafts, with no leader choreographing the assembly. Each ant follows simple local rules, and a resilient structure emerges.

“Each ant is an independent agent,” says Gonzalez-Garcia. “We wanted each robot to have its own capabilities, the same way ant colonies form a raft.”

Most existing self-assembling robot systems, on water and elsewhere, rely on a central computer dictating every move. That approach is vulnerable to single points of failure and scales poorly: The planning math balloons as robots are added, and the swarm must assemble sequentially, with most robots idling while they wait their turn. FloatForm flips the balance. A lightweight central planner steps in only sparingly, assigning each robot a final position to perfect the lattice, a level of geometric precision that purely distributed methods struggle to guarantee. Everything else, including navigating toward the target shape, avoiding collisions, and adapting to disturbances, runs on the robots themselves, which coordinate by exchanging positions with their immediate neighbors. The whole swarm moves at once.

That parallelism is what sets the work apart. The planning complexity of FloatForms approach depends only on a robot’s local neighbors, not the total size of the swarm. “What we’re trying to do is to have minimal central intervention, and have them all move together at the same time,” says Gonzalez-Garcia.

In experiments at MIT, a fleet of eight robots repeatedly gathered from random positions into a target shape, latched into a rigid structure, broke apart on command, reassembled into a new configuration, and then drove across the pool as a single vessel, with each run taking four to eight minutes. In that final mode, called collective transport, a planner charts a trajectory for the whole structure and each robot computes its own contribution. “Every robot becomes an actuator,” Gonzalez-Garcia explains. Simulations showed the framework scaling smoothly to swarms of 64.

“The beauty of this largely decentralized approach is that the computation doesn’t get bogged down as the swarm grows,” says Wang. “Whether you are working with eight boats or 80, the entire fleet coordinates and moves simultaneously. Because the overall assembly time doesn’t significantly increase in principle, the system remains highly scalable.” 

There's a physical payoff to sticking together, too. “Our boats become more stable by joining together, like the ant raft, if you have waves or currents,” Hagemann says.

An origami handshake

The robots connect through a latching mechanism hidden entirely inside each hull. A single servo motor at the center drives an origami-inspired auxetic structure, a geometry that contracts uniformly in all directions at once, pulling permanent magnets on all four sides inward to release, or pushing them outward to grab a neighbor across gaps of 10 to 15 centimeters. The magnets are arranged with alternating polarities, so the boats reliably click into clean square lattices.

The elegant part is what the mechanism doesn’t do: consume (much) power. A 3D-printed gearbox holds the latch in either state with the motor switched off. “It uses energy to latch and de-latch, but in between those states, it doesn’t use any energy,” says Hagemann. For infrastructure that might hold a configuration for hours, that matters. “Because the robots are so small, you can only have a battery so big,” adds Gonzalez-Garcia. “If they use less energy on latching, they can use more on computation, or on actually moving.”

Getting there took some humbling engineering. Four miniature thrusters arranged in an “X” give each robot omnidirectional motion, including turning in place, but they pack large forces relative to the robots’ tiny inertia, which made early prototypes twitchy and prone to aggressive spins at low speeds. The team added stabilizing fins to increase hydrodynamic drag and tuned the controllers to stay robust across robots that, at this scale, are never quite identical. The magnets posed their own problem: They held on so well that de-latching sometimes required the robots to twist themselves free.

From the tank to the canal

Across 10 trials, the system completed its missions without human intervention 90 percent of the time with four robots and 70 percent with eight. When things did go wrong, the architecture showed its resilience: A robot that briefly lost its bearings could rejoin the structure on its own, without bringing the whole swarm to a halt, and robots stuck in formation deadlocks learned to shake themselves free and retry.

Moving from a controlled indoor tank to a real canal or harbor will take more than confidence. “There’s always a relationship between the size of a boat and the magnitude of the disturbance it can handle,” says Gonzalez-Garcia. “These boats are very small, so in very disturbed water, they cannot work.” Scaling up will mean reinforcing the latches, potentially with mechanical interlocking like the full-size Roboat used, and trading the lab’s ultrasonic indoor positioning for GPS or vision-based sensing. Helpfully, the coordination algorithm was designed to be sensor-agnostic: swap the sensors, keep the logic.

The team envisions applications well beyond city canals, from forming temporary platforms for offshore inspection and maintenance to adaptive sensor networks for studying migratory species to reconfigurable docking stations for emergency response in hard-to-reach areas. There is also potential for offshore and remote operations, from temporary construction platforms to environmental monitoring and scientific expeditions.

And the geography is wide open. “Venice, the Netherlands, Belgium, the fjords and lakes of Norway, really any city with a river can take advantage of this,” says Gonzalez-Garcia. “The project uses spaces where water is already important, but it also raises the question: Where else can water be used for something more?” 

“This is an exciting step forward in realizing distributed collective behaviors on water,” says University of Michigan Assistant Professor Steven Ceron, who wasn’t involved in the research. “Assembly, self-reconfiguration, and collective motion are difficult enough in dry environments, but achieving these behaviors in a predominantly distributed fashion on water represents a serious additional challenge, and this team has credibly overcome it. By shifting the computational burden onto the robots themselves, they have built a more resilient system that in the near future could enable robot collectives like this to be deployed in open-water environments for search operations, environmental monitoring, and reconfigurable marine infrastructure.”

Gonzalez-Garcia, Hagemann, and Wang wrote the paper with senior authors Ratti, who is also a professor at Politecnico di Milano, and Rus. Gonzalez-Garcia is additionally affiliated with the MECO Research Team at KU Leuven. The research was supported by a grant from the Amsterdam Institute for Advanced Metropolitan Solutions, with additional support from the University of Wisconsin at Madison. The team thanks MIT Sea Grant and Professor Michael Triantafyllou for providing the test tank.

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Bringing the data to every sideline

With Boston serving as a host city for the FIFA World Cup, the whole Bay State has soccer fever, including Henry Wang. As a child growing up in Dallas, sports were everything to him. Today, Wang is working on research that could impact some of the biggest sporting events in the world, including future World Cups.

The first such event that made a big impression on Wang involved a different form of football.

“The first ever sports game I remember watching was Super Bowl XLII in 2008,” he says. “I was really drawn to the competition, and the way it was presented. It’s this whole big spectacle.”

Wang, a fourth-year PhD candidate in social and engineering systems within MIT’s Institute for Data, Systems, and Society, studies how data and technology can improve the way sports are played, analyzed, and refereed. Working in the MIT Sports Lab in collaboration with FIFA, he develops systems with the goals of helping referees make faster, more accurate decisions and expanding access to performance analytics across the globe.

Now in the final stretch of his doctoral program and preparing to defend his thesis at the end of this year, Wang has spent nearly a decade at MIT. After earning his undergraduate degree in 2023 with a double major in computer science, economics, and data science and business analytics, he transitioned directly into graduate school. Sports have been a constant throughout that journey.

A competitive swimmer since age 7, Wang says athletics shaped both his identity and his community.

“Athletic competition was always a really big part of my life,” he says. “It’s kind of how I made a lot of friends, around the pool, and now at school, or in the lab and office.”

Ironically, swimming entered his life not because of a burning passion for sports, but because of a doctor’s recommendation.

“I don’t really come from a huge sports family,” Wang says. When he was diagnosed with asthma as a child, his pediatrician suggested swimming to strengthen his lungs. 

His parents, both scientific researchers in radiology and medical physics, supported his growing passion. That support eventually led Wang to MIT, where he served as captain of the men’s swimming and diving team. In tandem, he continued pursuing research opportunities that merged his technical interests with his love of sports.

His first sports analytics project began with a cold email.

As a first-year student, Wang reached out to MIT Sloan School of Management Senior Lecturer Ben Shields to see if he could assist Shields with his research on sports strategy and analytics. Shields later connected Wang with a coach he knew who was interested in analyzing the two-point conversion strategy for MIT’s football team.

The project revealed that MIT could benefit from attempting two-point conversions much more frequently. The experience opened the door to the MIT Sports Lab, where Wang found mentors including Lecturer Christina Chase, Professor Anette “Peko” Hosoi, and former research scientist Ferran Vidal-Codina.

His research now focuses on two central questions: How can technology democratize access to sports data, and how can it help officials make better decisions?

Wang works with FIFA Innovation, the group within soccer’s global governing body that leads the development and testing of match technology used on the field. His research explores automatic event detection and officiating technologies designed to assist referees without disrupting the fan experience.

In one recent project, Wang helped develop a semi-automated system that uses players’ skeletal data and ball tracking to determine which player last touched the ball before it goes out of bounds. The research prototype aims to assist goal kick and corner kick decisions while minimizing interruptions to the game.

For Wang, success means that referees find the tools helpful, and fans barely notice it at all.

“A ball goes out of bounds, and we can immediately tell the referee it’s a corner kick,” he says. “The fans don’t even notice it.”

Alongside his doctoral research, Wang has gained experience across professional sports, spending two years with the Boston Red Sox’s baseball sciences team before accepting a role as a senior data scientist in basketball research and development with the Philadelphia 76ers, where he will continue working after graduation.

Despite his demanding schedule, he says the work rarely feels like work.

“I enjoy it so much,” he says. “I really don’t know what else I would be doing.”

Outside the lab, sports continue to anchor his life. Swimming at MIT provided structure and community during challenging moments.

“MIT can be pretty hard,” Wang says. “Having a consistent 5-to-7 o’clock swim practice every day definitely helped a lot.”

For Wang, sports have always been more than competition. They have shaped his friendships, inspired his research, and guided his career trajectory.

Now, as he works to build technologies that could change how billions of people experience the world’s most popular games, he is still driven by the same sense of love he felt watching sports as a child.

“I want every kid who plays sports to have the best experience possible, because I know how meaningful that can be toward someone’s life journey,” Wang says.

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Separating logic and language

Some people find it useful to talk through their problems — but language isn’t necessary for logical reasoning, cognitive neuroscientists at MIT’s McGovern Institute for Brain Research say. 

In research published this week in the journal PNAS, researchers led by MIT associate professor of brain and cognitive sciences Evelina Fedorenko have shown that people can perform well on tasks that require logical reasoning even if their language abilities are severely impaired. What’s more, brain imaging shows that language-processing parts of the brain are not called on for logical reasoning.

Philosophers, linguists, and cognitive scientists have debated the relationship between language and thought for thousands of years, with many arguing that we use language to think. There are good reasons to suspect a close relationship between logic and language, acknowledges Hope Kean, a postdoc and former K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center graduate fellow in Fedorenko’s lab. “Abstract thinking has properties that look a lot like language,” Kean says, pointing to structural similarities. “You can decompose a thought into subcomponents, like little atoms of logical propositions, and you can combine them in a hierarchical manner to make more complex structured rules, very akin to language.”

But she and Fedorenko, who is also a McGovern Institute investigator, suspected that while we largely depend on language to communicate about logical reasoning — from presenting a problem to explaining how we have arrived at conclusions — the brain might use a separate system for the reasoning itself. 

“There are aspects of thinking that seem to go beyond some of the limitations of language,” Kean explains. Logical reasoning demands precision that language often lacks. And language is linear, progressing one word at a time, whereas evaluating available information to reach logical conclusions can require thinking in less linear ways.

Logical reasoning

These observations left Kean curious about how the brain handles logical reasoning. It’s a particularly difficult question to answer scientifically, because it’s hard to take language out of the equation when working with human study participants. But Fedorenko’s team did just that by collaborating with Rosemary Varley, a neuroscientist at University College London who studies acquired language disorders, and her team.

Together, the scientists worked with two patients who had experienced stroke that damaged language-processing parts of their brains, leaving them with severe impairments in both understanding and producing language. They designed language-free logic games in which participants were asked to infer relationships between sets of numbers. Given two lists, they had to figure out the hidden rule that turned one list into the other, such as reversing the digits or removing numbers above a certain value. Once they thought they’d discovered the rule, they had to apply it to new examples. In a second game, participants were presented a set of geometric patterns and asked to identify another pattern to complete the matrix.

As participants solved increasingly difficult puzzles, it became clear that people don’t need language for this kind of reasoning. Patients with language impairments solved the problems as well as a control group, and were even able to communicate the rules they inferred using gestures, or with a sketch. “It really upends a theory that says that symbolic rule induction is not possible without linguistic capacities,” says Kean.

Alongside this part of the study, Kean and colleagues also used functional brain imaging to study what happens in the brains of healthy adults when they are engaged in logical reasoning. Participants in this part of the study visited MIT for a series of MRI scans, which captured images of their brain activity during an array of tasks. In addition to completing different kinds of logic games inside the scanner, participants were asked to engage in tasks designed to map the language-processing parts of their brain. Another set of tasks was used to map each person’s so-called “multiple demand network” — a distributed brain system that supports complex problem-solving.

These neurotypical participants completed logic games similar to those used with the language-impaired patients. They were also presented with problems that required syllogistic reasoning, using “if-then” statements such as “if the ball is red, then it is big. The ball is red. Is the ball big?” The team varied the difficulty of the logic puzzles so they could see which brain areas became more active when the need for logical reasoning intensified. Likewise, they looked for changes in brain activity when participants had to infer a hidden rule, versus simply applying a rule they’d been given.

Here, too, a separation between language and logic was clear: The MRI scans showed the brain’s language system is not engaged for either inductive reasoning (when participants identified hidden rules) or deductive reasoning (when they assessed the validity of syllogistic conclusions). Surprisingly, the multiple demand network, which many scientists had suspected was important for logical reasoning, was engaged during inductive reasoning, but didn’t seem to get involved in deductive reasoning — a finding Kean is building on in her ongoing work.

For Fedorenko and Kean, the findings are strong support for a separation of logic and language in the brain. They add to previous findings from Fedorenko’s lab showing that other types of thinking, such as object categorization and social reasoning, also do not rely on language.

Acquired language impairments and AI

The researchers say these findings have important implications for how we think about acquired language impairments, or aphasia. Specialists who work with people with aphasia have long recognized that loss of language does not mean loss of intelligence. People with aphasia can continue to enjoy playing chess, solving sudoku puzzles, or being in charge of the family’s finances. But it is common for others to confuse their communicative difficulties with thinking difficulties.

“This research adds to a growing body of work establishing that even severely aphasic individuals can preserve their ability for abstract logical thought — a defining feature of our species,” Fedorenko says. “We should continue to educate the public that linguistic difficulties — in aphasia, but also in those with developmental language conditions, such as stuttering, or those who do not speak English natively — are not indicative of how smart or capable someone is.”

There could be implications for artificial intelligence, too. Large language models like ChatGPT and Claude are trained entirely on text and use text as their output — yet they convincingly simulate some kinds of human reasoning. Exploring the differences between these models and the human brain, where language and abstract logical thought are distinct, might offer useful insights to inform future models, Kean says.

When it comes to understanding how the human brain reasons, Kean calls this a new frontier in the geography of thought — and she says it’s one she is eager to explore.

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MIT-designed educational factory embraces modern manufacturing

From the basement of MIT’s Building 35 to Monterrey, Mexico, and now beyond. That is the journey of FrED, a low-cost desktop fiber (Fr) extrusion (E) device (D), designed and assembled by students in an educational factory at MIT.

That factory is transforming how manufacturing is taught — replacing textbook learning with hands-on experience in a space where tinkering is encouraged and information flows continuously. Through a collaboration between MIT and Tecnológico de Monterrey (Tec) managed by MIT.nano, FrED has been refined across dozens of graduate theses and undergraduate research stays. It is used to study manufacturing systems in academic and professional courses, and at FrED factories, first established at MIT and now at Tec’s campuses in Monterrey and Mexico City.

“What does it mean to bring the factory to the learner?” asked Brian W. Anthony, MIT.nano associate director and principal research scientist in the MIT Department of Mechanical Engineering (MechE) at the second annual FrED summit in Mexico City. “We have FrED as a process that manufactures a fiber, and we also have the FrED factory that’s an education and practice factory where we are manufacturing a real product. It’s not just a learning factory where we tear apart the product when we’re done. We really ship FrEDs to our online learners, to educators at MIT and Tec, and soon, to new partners around the world.”

Designed from the start for multi-node community scaling, FrED and the FrED factory have created a thriving, collaborative ecosystem for current and future manufacturing engineers. The next step is to expand that ecosystem globally. Announced at the FrED summit by Tec professor Pedro Ponce Cruz, a new FrED factory at Tec’s Saltillo campus will be opening in the next academic year. After that, the team plans to expand to other campuses across the United States and Mexico.

“Together, we are helping build a global engineering talent pipeline,” says Adriana Vargas Martinez, executive director of research strategy at Tec. “Through the FrED and FrED factory initiative, nearly 500 students have already been trained in advanced manufacturing automation, moving from Tec classrooms into research laboratories and collaborative projects with MIT.” 

Discussing FrED and FrED factory’s research impact, she notes 25 publications and seven papers in development. “International mobility has also been an important dimension of this partnership,” she says.

A shift toward modern manufacturing deep-tech themes

FrED’s expansion comes at a time when manufacturing at MIT and across industry is shifting toward smart manufacturing, or Industry 4.0, integrating automation, machine learning, and artificial intelligence. One of MIT’s strategic priorities, the MIT Initiative for New Manufacturing (INM), is working to support new manufacturing research, development of new courses and workforce training, and building of shared facilities to pilot production lines and immersive manufacturing experiences. FrED and the FrED factory are already designed to support these efforts, and at an international scale.

“FrED and the FrED factory is really, I think, solving at least one problem: how we give real, physically meaningful physical context and production-level data, production-level problems in an academic environment that is directly transferable to the knowledge that you need on the factory floor,” says Anthony. It’s difficult to get data out of a real factory, he adds; what FrED offers is physical context crossed with data science, providing an open platform and open data for learning and experimenting.

FrED naturally generates the multi-modal data required for digital twins, analytics, and AI-driven process improvement, turning abstract AI/manufacturing integration into hands-on practice. The next set of research objectives in the FrED factory will focus on developing a realistic and interactive digital twin of the factory, immersive technology for collaborative learning, integrating agentic controllers. They will include new downstream manufacturing processes and machines that take as input the fiber from FrED — all to enhance smart manufacturing education.

These goals will be worked on by MIT and Tecnológico de Monterrey students as part of a FrED factory research stay. This program brings Tec undergraduates to MIT to work side-by-side with MIT students — not observing, but fully integrated into the research team. The students then take what they’ve learned back to Mexico, to enhance FrED factories at their home institution. 

“Beyond the technical side, FrED gave me memories, friendships, and a lot more confidence in myself than I knew I had,” says Naomi Najera, a Tec undergraduate student who completed a research stay at MIT in 2025. “It also gave me a space where I could make mistakes and learn from them. And also to realize how much I can achieve with my team. That human side of this project really changed my whole experience.”

A recent result from this exchange, announced June 23 by the American Society for Engineering Education (ASEE), a paper entitled “Hands-On Predictive Maintenance Kit for Manufacturing Education: An Accessible Experiential Learning Approach,” written by Tec and MIT students, received the 2026 ASEE Manufacturing Division Best Paper Award.

Shifting classroom learning to factory operations

At MIT’s campus in Cambridge, Massachusetts, passersby can look down into the Building 35 basement windows to see a constant flow of activity, materials, and knowledge in the MIT FrED factory. In Mexico, seven cohorts of students over four years each designed a custom version of FrED and built and operated an automated FrED factory production line. Indeed, FrED has restructured how Tec teaches mechatronics and manufacturing systems. “This collaboration integrates research directly into education,” says Vargas Martinez, “combining learning factories and our manufacturing environments with student-centered research.”

The Tec students’ enthusiasm has led to the launch of an Undergraduate Research Opportunities Program-like curriculum (FRAME: Factory-based Research for All in Mechatronics Education) in Mexico, where first-year undergraduates are working alongside graduate level students in the FrED factory. 

“Joining FrED as a first-semester university student has been an amazing opportunity for me to get hands-on experience in real-world projects in areas such as coding, manufacturing, and robotics,” says Katherine Lucia McLean. “It’s helped me grow a lot as an engineering student.”

The FrED factory model forces real leadership behaviors: coordinating multi-station systems, managing bottlenecks, building maintenance logic into the student experience, enforcing quality measurement, and iterating system design year after year. As each class graduates and a new one begins, knowledge is transferred, some of it lost, most of it built upon. In this way, FrED never becomes outdated, as each cohort is reimagining manufacturing technologies and systems for a smarter, more productive factory.

FrED and the FrED factory have momentum. Anthony taught the global capstone course at the Monterrey campus last year, and will expand to teach at all five international Tec campuses in 2027. The FrED Factory Conference will take place at MIT in 2027.

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MIT researcher proposes a way to detect nuclear weapons in space

In 2024, a U.S. government official warned that Russia could be developing a new satellite designed to carry nuclear weapons into space. The statement followed the launch of a suspicious Russian satellite into low-Earth orbit in 2022, just a few weeks before the country’s full-scale invasion of Ukraine.

A nuclear detonation in low-Earth orbit — the region about 100 miles to 1,200 miles above Earth’s surface — would release trillions of highly energetic electrons that would destroy many of the satellites in space, disrupting telecommunications networks, GPS, space-based internet, and more.

The 1967 Outer Space Treaty bans the placement of nuclear weapons in space, but there’s currently no way to verify satellites don’t contain nuclear weapons. In fact, no verification methods have even been proposed in unclassified, peer-reviewed literature.

Now, MIT Professor Areg Danagoulian is proposing a way to determine if a satellite orbiting Earth contains a nuclear weapon. In a new paper published in Nature, Danagoulian describes his idea for a satellite-based sensor system that could orbit close by a suspect satellite and detect neutrons generated by high-energy protons colliding with radioactive material.

In the paper, Danagoulian calculates that a sensor system the size of a large encyclopedia could detect a nuclear weapon with 99 percent accuracy if it orbited within 4,000 meters of the suspect satellite for about a week. He also estimates that the detection time could be cut to a matter of hours if multiple satellite sensors were used or the sensor satellite was able to get within 1,000 meters of the suspect satellite.

“If we eventually have some verification mechanisms for the Outer Space Treaty, that will put pressure on countries to respect the treaty or disclose what they are doing, because they know if they try to violate it, we will find out,” Danagoulian says. “I very much hope this will turn into a real system, or proof-of-concept system, but the goal right now is to get national labs to use this work for their own research, and to get policymakers to seriously consider this technology as a potential part of national technical means.”

Protecting space

In 1962, the U.S. detonated a 1.4-megaton thermonuclear warhead in space, which unintentionally destroyed many of the early satellites of the era. The blast released enormous volumes of highly energized electrons, and many became trapped in Earth’s magnetic field, where they damage any electronics in their path.

“When you have a nuclear detonation in outer space, basically the whole body of the bomb becomes ionized, and nearly every single electron in the weapon’s mass becomes free,” Danagoulian explains. “It gets injected into what’s called the inner Van Allen radiation belt. Once there, the electrons start hitting everything flying through those belts, causing ionization, radiation damage, and more. As you go further out into space, you create these thick belts around Earth populated by highly energetic protons and electrons.”

The 1967 Outer Space Treaty declared space the “province of all mankind” and banned nuclear weapons in space, among other safeguards. It has since been signed by 118 countries including the U.S., China, and Russia.

Monitoring compliance with the treaty has taken on increased urgency since Russia’s 2022 launch of a suspicious satellite, Cosmos2553, which Russia claims is used for surveillance and sensing. However, U.S. authorities believe it may carry components of a nuclear device undergoing testing, with the possible future goal of fielding an actual nuclear anti-satellite weapon. The detonation of a nuclear weapon at that orbit could destroy many of the U.S. reconnaissance satellites, international communication satellite platforms, as well as the Starlink satellites.

“The Russians launched this satellite in a very strange and unusual orbit because it goes through the most hostile environment possible around the planet,” Danagoulian explains. “No one puts satellites there because it’s highly radioactive. Why would you put a satellite in that orbit? Well, that location is likely the best point for trapping electrons if you were to detonate a thermonuclear weapon.”

Danagoulian notes most research on nuclear detection is highly classified, making it hard to know how much progress has been made in national labs. But he wanted to show that scientifically proving the presence of a nuclear weapon in space is possible.

Particle bombardment

The approach Danagoulian developed centers on a reaction known as spallation, caused by highly energetic protons in radioactive environments.

“When an energetic proton slams into elements with a high atomic number, like uranium and plutonium, each proton may knock out something like 40 neutrons,” he explains. “That’s a ridiculously large number. We’re talking about millions of protons per second per square centimeter, with many of them generating 40 neutrons. The question is can you detect some of those neutrons?”

Normal satellites wouldn’t emit nearly as many neutrons, but there are still naturally occurring protons, neutrons, and electrons in the atmosphere, especially in low-Earth orbit. Danagoulian’s concept uses two panels made up of pixels of neutron sensors known as scintillators that interact with radiation and emit light. The panels are sandwiched between synthetic crystal diamond detectors that allow the system to distinguish between neutrons coming from radioactive materials and natural protons and electrons. The two-panel construction then can be used to estimate the direction of the neutron, allowing it to differentiate between natural atmospheric neutrons and those coming from a suspected satellite. 

“Most neutron detectors are very sensitive to protons, so you have to come up with some smart ways to reject protons but keep neutrons,” Danagoulian says. “You also have to tell the difference between naturally occurring neutrons and neutron spallation from the satellite.”

He believes the system, placed inside of an inspector satellite, would be strong enough to survive the harsh environment of low-Earth orbit while also being fast enough to process the protons, electrons, and neutrons that bombard it.

Danagoulian’s calculations on how long the detector satellite would have to be near the suspect satellite give him confidence in the feasibility of the system. If a detector satellite were able to get within 1,000 meters of the suspect satellite, it could accurately detect nuclear weapons in about one hour. That would amount to a single flyby.

Danagoulian calls the paper a feasibility study of the concept.

“I say in the paper this isn’t a completely proven system,” he says. “The purpose of the paper is to show the scientific community that it’s scientifically possible to do this. But there are many more practical considerations to be made to actually build these detectors.”

Danagoulian hopes the study will stimulate further research and development. He is also working with researchers in MIT’s Center for Nuclear Security and Policy (CNSP) to understand the policy landscape around this issue.

If a version of his system is eventually developed, Danagoulian believes it could encourage the nonproliferation that has helped preserve satellites so far. He notes that while adversarial countries are naturally suspicious of each other’s claims, scientific evidence would strengthen trust.

“You can fake intelligence,” he says, “but you can’t fake physics.”

The work was supported, in part, by the National Nuclear Security Administration, the Carnegie Foundation, and Longview Philanthropy.

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How novice coders can develop AI programs for military applications

In today's world, artificial intelligence chatbots such as ChatGPT and Claude can perform many functions, such as composing work emails and planning travel itineraries. These chatbots are systems built around large vision-language models (VLMs): AI trained on a massive dataset that includes books, websites, code, and images. 

The AI algorithms are then refined on massive amounts of human-generated feedback to follow instructions and avoid harmful or unwanted output, and use that "knowledge" to produce text or images based on input from a user. Although chatbots have clear limitations, they can be very helpful for a wide range of tasks, including in some areas that traditionally require specialized skills, like computer programming.

As part of a project for the U.S. Department of the Air Force–MIT AI Accelerator's Phantom Program, U.S. Air Force cadet Joshua Lynch — with the help of his mentor, Laura Niss, a technical staff member in the Embedded and AI Systems Group at MIT Lincoln Laboratory — wanted to determine if, as a complete novice to coding, he could develop a fully functional program. He used a process called "vibe-coding," in which a user relies entirely on prompts to guide a generative AI chatbot to write and refine code. 

His motivation was to empower anyone familiar with the military problem space, regardless of their technical background, to advance their ideas for useful software applications, essentially bypassing the time and cost constraints of the traditional military software development pipeline. Lynch aimed to build his own application while Niss monitored his experience with the technology.

"The Phantom student wanted to see if he could create a useful application through self-identified vibe-coding, without any previous experience," Niss says. "Within this project, I wanted to understand how his perception of AI changed over time with use. We both wanted to understand better where and how AI could be used by nontechnical users in the military."

Lynch set out to see if, starting with no coding skills and using chatbots, he could create an application specific to his type of tactical team to help reduce collateral damage while enhancing survivability in the broader mission. This application would offer capabilities including AI-assisted target recognition; modular intelligence, surveillance, and reconnaissance; autonomous striking; and communication management on the battlefield. 

During the project, Lynch completed several professional development courses in AI and familiarized himself with both military and nonmilitary uses of the technology. For the basis for his code generation, he used the paid models of three AI chatbots: Anthropic's Claude, OpenAI's ChatGPT, and Google's Gemini. Most of this work was done only through the chatbots' main chat function on a web browser, not as an integrated system within a development environment, as is standard now. The final application was produced using Google AI Studio App, which can create applications that interface with the Gemini application programming interface and has AI integrated in the development environment. 

Over three months, Lynch worked with these models to build his application, called the Remote Operating Modular Augmentation Device (ROMAD-AI). During this time, he learned several methods to improve the code output. For example, he often encountered difficulties with the AI chatbots lacking hierarchical focus and modifying unrelated code sections. He discovered it was important to break problems into small parts, frame questions clearly, and steer conversations back on topic when they stray too far from the objective. 

Learning to recognize the chatbots' limitations and effectively work around them took up most of the project timeline. As Lynch gained more experience with the chatbots, limitations in the AI capabilities and time for development caused him to re-scope the project, moving it from an application that could assist on the battlefield to one that could perform basic document processing, such as analyzing tactical maps of battlefields and generating mission-planning documents through an interface with a VLM-powered chatbot. While the resulting prototype did not perform all capabilities Lynch originally set out to include (and in its current iteration was not secure for the desired use case), it proved the capability and usefulness of such an application for service members.

"I was quite impressed with this final product, and it showed me how powerful these systems can be at prototyping designs from nonexperts," Niss says. "I'm now of the opinion that these can be powerful tools for nontechnical experts to convey problems and possible solutions to technical experts, and aid in communicating desired outcomes."

Niss observed the change in Lynch's perspective of AI language models during his experience. After starting with an impressive goal, Lynch gained understanding of the capabilities of current technology and significantly scoped down his expectations by the end of the project period. Measures of his perceptions of the different AI systems over time and across system updates were particularly interesting to Lynch and Niss, with Claude showing more stability than ChatGPT across traits such as likeability, anthropomorphism, and perceived intelligence. Lynch found AI to be a helpful tutor, but noted its inaccuracies on topics he knew well.

The project showed that AI chatbots can empower nontechnical service members to produce viable software applications for their unique problems, although it works better as a prototyping assistant than as a full production tool when handling sensitive information and for critical applications. Improper vetting of code may lead to security risks, as demonstrated by an instance where Lynch didn't realize that the final application was sending the input documents to a Gemini AI model to analyze, rather than parsing the documents locally on his computer. Although AI can generate significant amounts of functional code, code review remains a bottleneck in this space.

"For me, this project reinforced the expanse between experts in different fields," Niss says. "No matter how good AI gets, I think we'll always need to collaborate to get to the best solutions for the most important problems."

Research was sponsored by the Department of the Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000.

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