A number of MIT affiliates were recently honored for their research by the Breakthrough Prize Foundation.

Stuart H. Orkin ’67 shared a Breakthrough Prize in Life Sciences with Swee Lay Thein for their research transforming sickle cell disease and beta-thalassemia from incurable to treatable conditions through gene editing therapy. Their work identified the master switch controlling fetal hemoglobin, leading directly to the development of Casgevy – the first CRISPR-based medicine approved for any disease. Orkin, a graduate of the MIT Department of Biology, is currently a professor of pediatrics at Harvard Medical School.

Shu-Heng Shao, assistant professor of physics at MIT and a researcher in the MIT Center for Theoretical Physics — a Leinweber Institute, was recognized with a 2026 New Horizons in Physics Prize. Shao shared the honor with Clay Córdova from the University of Chicago, Thomas Dumitrescu from the University of California at Los Angeles, and Yifan Wang PhD ’16 from New York University. The four were recognized for “discover[ing] and develop[ing] the theory of ‘generalized symmetries’ in quantum field theory.” 

J. Colin Hill ’08 shared a New Horizons in Physics Prize with Dillon Brout, Mathew Madhavacheril, Maria Vincenzi, Daniel Scolnic, and W. L. Kimmy Wu for their results measuring the expansion and composition of the universe, with Hill’s focus on advancing analyses of data from the cosmic microwave background radiation left over from the Big Bang.

Hong Wang PhD ’19 received a New Horizons in Mathematics Prize for resolving or making advances on a family of notoriously difficult problems in harmonic analysis, a branch of mathematics that studies functions by decomposing them into fundamental components. 

In addition, Bryan Traynor, a former student in the Harvard-MIT Program in Health Sciences and Technology, shared a Breakthrough Prize in Life Sciences with Rosa Rademakers for discovering the most common genetic cause of both amyotrophic lateral sclerosis and frontotemporal dementia.

Founded by a group of Silicon Valley entrepreneurs, the Breakthrough Prizes recognize the world’s top scientists in life sciences, fundamental physics, and mathematics. The laureates were honored at a gala ceremony in Los Angeles on April 18.

A supermassive black hole lies at the heart of every galaxy, including the Milky Way. When a black hole is active, it pulls material in as a whirpool of high-temperature gas and dust. As this cosmic material piles up and falls onto a black hole, it lights up its vicinity, radiating a huge amount of energy. 

The most energetic supermassive black holes are known as quasars, and they are some of the most active and luminous objects in the universe. These voracious systems take in so much material that the energy they emit can outshine all the light in the surrounding galaxy. The pattern of light from a quasar can give scientists clues to how active supermassive black holes shape the galaxies around them. 

Now astronomers at MIT and elsewhere have detected a quasar flickering from the very early universe. The scientists traced the light from the quasar back to the “cosmic dawn,” just 850 million years after the Big Bang. The discovery represents the earliest flickering quasar detected to date. 

“Although there have been a lot of quasars found in the cosmic dawn, this is the first time we actually see one flickering,” says Gene Leung, a postdoc in the MIT Kavli Institute for Astrophysics and Space Research. 

The quasar’s flicker enabled the researchers to determine that, surprisingly, the ancient quasar’s whirpool of gas and dust, known as an accretion disk, resembled a flat pancake, similar in shape to that of more modern-day quasars. 

Their findings add to a longstanding mystery in cosmology: Why do supermassive black holes exist so early in the universe’s history? Physicists have assumed that a flat accretion disk reflects a relatively mature black hole that is in a calm and stable state. Black holes that are just starting to form, like those in the very early universe, should be more unsettled systems, with accretion disks that appear more puffy and chaotic. 

The flat accretion disk around this very early quasar heightens the mystery of how supermassive black holes can grow and mature in a very short amount of cosmic time. 

“I think what this suggests is that  all the messy, very rapid growth phases that we expect all black holes to go through at some point happen very, very early on, before we see them as these very bright luminous quasars,” says Anna-Christina Eilers, assistant professor of physics at MIT. “That’s the picture that’s emerging.”

Eilers, Leung, and their colleagues report their results in a paper appearing today in Nature Astronomy. Their co-authors include members of MIT Kavli and multiple other institutions. 

Past a pinprick

A supermassive black hole can be billions of times more massive than the sun. These gravitational giants are the central “engines” of most galaxies, helping to regulate a galaxy’s star formation and growth. 

“Without supermassive black holes, no galaxy would look the way it does today,” Eilers says. “Black holes play a major role in shaping how galactic ecosystems look.”

It was long assumed that it should take more than a billion years for the first galaxies to settle and mature, so scientists didn’t expect to see supermassive black holes in the very early universe. But observations since the early 2000s showed otherwise. Scientists have spotted more than 200 supermassive black holes in the universe’s first billion years. Such objects were detectable because they were in an extremely active quasar phase, giving off enormous blasts of radiation that could be seen from Earth, 13 billion light years away. 

These earliest quasars were observed as pinpricks of light, which signal the existence of a supermassive black hole at early times. But from these bright and distant dots, scientists aren’t able to tell much more about the black holes and their cosmic dawn environments. To do so, they need to catch a quasar’s “flicker.”

“People have known that quasars in the nearby universe can flicker,” Leung says. “The flickering comes from fluctuations in the way the gas is being fed into the black hole. And how a quasar flickers tells us something about the structure of a black hole’s accretion disk, and the kind of ‘bites’ that the black hole is eating.”

Mapping a flicker

Leung and Eilers looked to detect a flickering quasar from the early universe in hopes of learning more about the shape and structure of the earliest supermassive black holes. To do so would be a technical challenge: The further back in time and space an object is, the more distorted its light appears. This effect is due to the expanding universe, which effectively stretches, or “redshifts” light to redder, longer wavelengths. The same stretching occurs in time: Any flicker that naturally occurs over several weeks, for instance, would appear stretched out, flickering only every few months when seen from billions of light years away. 

To spot a flickering quasar from the cosmic dawn, the team needed to observe the distant universe at redder wavelengths, and specifically within the infrared spectrum, and over long timescales of many years. 

“This was the technical challenge we had to overcome,” Eilers says. “We needed data at longer, infrared wavelengths taken repeatedly over very long timescales.” 

The team ultimately found a flicker in data collected by NASA’s Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) mission — a space-based infrared telescope that scanned the entire sky over a total of about 14 years. Former MIT postdoc Kishalay De, who is now a faculty member at Columbia University, had launched a project to re-process archival data from NEOWISE. Based on the re-processed data, the team unearthed a signal, from just 850 million years after the Big Bang, which was confirmed to be the earliest flickering quasar. 

“We saw the quasar flickering randomly over the 14-year period, much like a candle’s flame flickers without a fixed pattern,” Leung notes. 

They estimate that the quasar is as bright as 12 trillion suns, and it is flickering by about 20 percent, meaning that it fluctuates up and down, by a brightness of about 2 trillion suns. 

The researchers also tracked how the quasar’s light flickered over several different wavelengths. The wavelength of light reflects a certain temperature of the material that is emitting the light. The closer material is to a black hole, the hotter it is. Researchers can therefore use wavelengths of light to map the shape and structure of material within the accretion disk around a black hole. 

Using NEOWISE data, the team analyzed the quasar’s flicker to determine the shape of the accretion disk surrounding the central supermassive black hole. They found that the disk is surprisingly thin and flat — a structure that astronomers mostly see around nearby, older black holes, that have had much longer to settle and mature. 

“This provides direct evidence that the same feeding processes and structures observed in the nearby universe were already in place at very early times, despite very different cosmic environments, which had never been seen before,” Eilers says. 

“This means something happened even earlier on that led to these systems to look so mature,” Leung adds. 

The team hopes to peer even further back in cosmic time to catch a quasar’s earlier, premature development. Then, scientists can start to piece together the conditions that brewed up the first supermassive black holes. 

This research was supported, in part, by NASA.

The silicon that forms the foundation of most computer chips has fundamental limits to how much power it can manage, which constrains the speed and energy-efficiency of wireless communication systems.

A promising solution is to build future wireless electronics out of transistors made from gallium nitride, an advanced material that can handle the speed and energy required for demanding wireless applications like 6G and satellite communications. 

But even in the best transistors, a very large fraction of that energy becomes heat. As researchers pack more gallium nitride transistors into a smaller area on a silicon chip, localized hot spots degrade reliability and hamper performance.

Now, a team from MIT and elsewhere has broken through this bottleneck by embedding gallium nitride transistors into an ultrathin layer of diamond. The diamond acts as a heat spreader that normalizes the temperature and allows the transistors to approach peak performance without reducing reliability.

The researchers used this technique to manufacture a power amplifier for wireless communications, which outperformed every similar amplifier they found in the literature. 

While their fabrication technique is extremely precise and requires the integration of different material systems, it can be performed at the scale needed for commercial applications.

“No single material can do everything well in a wireless device, so these 3D heterogeneously integrated systems are here to stay. The key challenge left has been reliability and thermal management, and we might have now unlocked the final step we need to make these systems operate at scale and high volume,” says Pradyot Yadav, an electrical engineering and computer science (EECS) graduate student at MIT and lead author of a paper on this advance.

Yadav is joined on the paper by Tomás Palacios, the Clarence J. LeBel Professor of EECS, director of the Microsystems Technology Laboratories (MTL), and the MIT Institute for Soldier Nanotechnology; and Ruonan Han, a professor in EECS and a member of MTL and the Research Laboratory of Electronics; as well as others at Georgia Tech and Penn State University. The research was presented at the Radio Frequency Integrated Circuits Symposium, part of the IEEE International Microwave Symposium.

A multimaterial method

To build faster and more energy-efficient electronics, researchers are studying heterogeneously integrated systems in which multiple materials are stacked into a unified package to leverage the beneficial properties of each one. 

For instance, MIT researchers previously stacked gallium nitride (GaN) on top of silicon as well as on top of glass to create higher-performance chips.

But in a heterogeneously integrated chip, each material has a different operating temperature, which can degrade the reliability of an electronic device. 

“If we can incorporate a material that manages the heat so the GaN and silicon are at the same temperature, then the reliability of the entire 3D chip will improve. The best material for that is diamond,” Yadav explains.

The researchers use lab-grown, jewelry-grade diamond — the same type one would find in some engagement rings. Diamond has the highest thermal conductivity of any known material. 

Advances in the growth process have significantly reduced the cost of single-crystal diamond wafers, making their use in computer chips more feasible.

In prior work, scientists have grown ultrathin, single-crystal layers of diamond on top of GaN transistors to manage heat. 

But this growth process, which is not easy to scale up, introduces unwanted capacitances in the chip. These store energy flowing through the circuit, diverting it from the transistors and slowing down their operations. 

The MIT researchers developed a completely different approach that reduces these unwanted capacitive effects. They embedded extremely tiny GaN transistors, known as dielets, into an ultrathin interposer, or substrate, made of single-crystal diamond. This diamond layer spreads and manages the heat, so the GaN and silicon operate at the same temperature without the unwanted capacitances.

“By putting these GaN transistors into a diamond interposer, we are actually able to improve the performance of the device, as opposed to degrading it. We can get the best of both worlds,” Yadav says.

Meticulous manufacturing

The fabrication process begins with the use of a lightning-fast femtosecond laser to cut prepared gallium nitride dielets out of a wafer. 

The researchers use the laser to drill precisely sized cavities into the diamond substrate. They carefully place a die attach film, which is only 20 microns thick, at the bottom of the cavity and drop a dielet on top of the film. 

Once the dielet is in place, they apply heat and pressure to mold it with the film and diamond substrate.

“That interface is key. If you don’t have that thermal die attach film placed just right, then the heat flow through the diamond to the GaN transistor will not be good enough. So you really need to have a very smooth, clean surface,” Yadav says.

The researchers then stack additional dielectric and metal layers on top of the GaN and diamond to build a working circuit.

They used this technique to fabricate a power amplifier, which is one of the key building blocks of any wireless system. Power amplifiers convert small electrical signals into larger ones that can then be transmitted long distances.

The amplifier they developed achieved higher output power, efficiency, and gain than any similar device the researchers are aware of, including an amplifier they designed in prior work.

“The power amplifier is the beating heart of a wireless device front end. Its performance will dictate the entire performance of your communication system. Our amplifier is powerful enough to ensure that a signal can be propagated for miles,” Yadav says.

These results show how their technique could be well-suited for demanding applications, like high-power radars, space communications, and industrial drones. 

It could also be used to manage heat in systems that perform power conversions inside data centers, improving energy-efficiency. 

Yadav hopes other researchers will build on these advances as they develop more complex heterogeneously integrated systems, opening the door to new possibilities with next-generation electronics.

“When I started my PhD, we wondered if any of this was even doable. It seemed like science fiction. Now we’ve shown all these systems that have outperformed anything that exists on the market today. GaN and 3D heterogeneous systems are going to be at the forefront of so many future applications. It is rewarding to know that we contributed a little bit to that space,” he says.

This research was funded, in part, by the Department of War, the Air Force Office of Scientific Research, the MIT Institute for Soldier Nanotechnologies, and the Qualcomm Innovation Fellowships. Device fabrication and microscopy were conducted at MIT.nano and the Georgia Tech Institute for Matter and Systems. 

On April 30, the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing (SERC) initiative hosted a full-day research symposium examining how artificial intelligence is shaping the world and its implications for society. 

The symposium included research talks by SERC’s latest seed grant recipients on topics such as air pollution forecasting and responsible computer vision deployment, panels on AI alignment and AI in education, and a keynote address by Jon Kleinberg PhD ’96, the Tisch University Professor of Computer Science and Information Science at Cornell University. The event also featured a poster session, where student researchers showcased projects they worked on throughout the year as SERC Scholars.

“There is so much amazing research being done at MIT on how AI and computing can be forces for good that benefit humanity. It was inspiring to see so much community interest in all this cutting-edge work,” said Brian Hedden, co-associate dean of SERC and professor of philosophy, who holds an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS).

“As computing and AI become increasingly embedded in nearly every dimension of society, SERC’s mission is to help ensure that ethical reflection and technical progress advance together,” said Nikos Trichakis, co-associate dean of SERC and the J.C. Penney Professor of Management. “This year’s symposium highlights the extraordinary range of work underway across MIT, and creates a forum for our community to engage deeply with the responsibilities that come with shaping the future of computing.”

Aligning AI with human values — and what values those might be

The challenges with AI alignment and moral meshing lie in the ethical questions of how to instill “human values” onto a very powerful and rapidly changing technology. Who makes the decision on what values and rationalities are included in an ethical framework? How does one account for distortion when translating these values from user to machine? 

These questions, among others, were posed by Dylan Hadfield-Menell, associate professor of EECS, during a panel he moderated that brought together an interdisciplinary group of speakers.

Iason Gabriel, a philosopher and research scientist at Google DeepMind, used the example of a judge to illustrate his point. “You want a judge to have good character, but to still interpret the rules. A reasonable person, though not necessarily the best person who ever lived. When it comes to AI, it’s not appropriate to model it as perfect. AI should be doing what we tell it to do, while using its character to interpret according to our moral values.”

Bailey Flanigan, assistant professor of political science in a shared appointment with the MIT Schwarzman College of Computing in EECS, took this a step further. To her, the most important problem to AI alignment is “resolving fundamental questions on who is entitled to govern different types of AI systems in the first place.”

Joining Flanigan on the panel was Bernado Zacka, associate professor of political science. Given the momentum of AI and complex institutional designs, Zacka expressed, “one of the most urgent problems is understanding the wisdom contained in the systems we are replacing, and why they function the way they do.” 

As deployment pressure increases, it can often feel like people are building the plane as they fly it, although the panelists overall seemed optimistic about the trajectory of AI alignment, emphasizing how crucial human components are to shaping these systems.

Offloading versus uplifting

As students across all levels of education begin to use AI, questions arise on whether there’s a way to ethically incorporate AI tools while maintaining academic accuracy and rigor. At a panel on AI and education, MIT faculty and Marta McAlister, the director of Gemini for Education, explored how AI is already being used in their classrooms and discussed ways it can support learning while remaining aligned with instructional and curricular goals.

Professors Eric Klopfer and Samuel Madden, co-chairs of MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, homed in on a central dilemma of whether AI is being used to offload work, rather than being used to help scaffold the concepts being taught. 

Madden, faculty head of computer science in EECS and the MIT College of Computing Distinguished Professor, described the process of cognitive struggle, whereby learning is done through a series of trials and failures. He said, “students now, when they hit that wall, their first instinct is to ask AI. They don’t see this as excelling in this process, and they haven’t actually acquired the skill you’re assessing.” The question then becomes how instructors maintain the process of cognitive struggle so it provides just enough of a challenge to combat the urge to use AI. 

Klopfer, who serves as director of the Scheller Teacher Education Program and the Education Arcade at MIT, echoed similar sentiments, in that critical thinking is no longer becoming a crucial step in the output of the work. Regarding where to start in keeping material just challenging enough, Klopfer suggested examining the curriculum as a whole. “Some core content has to go. We keep adding, instead of parsing or pruning,” he said. 

Moderator Justin Reich, director of the Teaching Systems Lab and an associate professor in the Comparative Media Studies Program/Writing, noted that while teens know that AI is bad, it doesn’t necessarily stop their AI usage. However, by inviting them into the discussion on how AI is implemented and incorporating a more reflective exchange with instructors, students could be more equipped to choose how they use these tools and why.

Regardless, AI tools and their implementation should not be treated as a one-size-fits-all policy. Pat Pataranutaporn, the Asahi Broadcasting Corporation Career Development Professor of Media Arts and Sciences and head of the Cyborg Psychology research group at the MIT Media Lab, said, “AI is not just one thing. It can and should be designed differently to promote things like creativity and critical thinking. What we measure, and how, shouldn’t be about getting the answer right. We should think about it would really mean for a student to learn these days.”

Is mimicking human reasoning just as good as the real thing?

With a slide deck that included chess grandmasters and film references, Kleinberg’s keynote address, titled “AI’s Models of the World, and Ours,” evaluated instances where AI systems have inadvertently set us up to fail due to a mismatch between the system’s model of the world and ours. 

To illustrate this point, Kleinberg used chess, where modern chess engines can compete at superhuman levels, but when paired with human partners, their strategies aren’t understandable or inferable to their human counterpart. These human handoffs would then lead to confusion. Kleinberg used the example of “The Fellowship of the Ring,” where Gandalf, a powerful wizard, entrusts a highly dangerous and important quest to a ragtag group of adventurers. For those familiar with the story, the group is unexpectedly left without Gandalf’s guidance, sending them into a temporary bout of very serious turmoil. 

When the chess engine hands a turn over to its human partner, the human struggles to pick up on the predictive move pattern that the engine has been following up until this point. “The danger of human-algorithm teams is that when the human takes over, the algorithm knows what it wants to do next, but the human doesn’t,” explained Kleinberg.

These analogies showcase the differences in the ways AI understands a world — through predictive simulations, pattern recognition, and constraints — to mimic human reasoning versus the innate, embodied knowledge that comes with the human experience, and whether these systems truly understand the worlds in which they’re operating. But the question remains that if the game still results in a checkmate, does it matter?

This April, humanity had front-row seats to space as the Artemis II Orion spacecraft transmitted crystal-clear footage of its historic journey around the moon over more than 250,000 miles back to Earth at speeds on par with those of home internet connections. 

The livestreaming of high-definition videos and high-resolution photos of the moon and Earth was made possible through the Orion Artemis II Optical Communications System (O2O). Developed by MIT Lincoln Laboratory in collaboration with NASA Goddard Space Flight Center, the onboard O2O payload was the space end of a high-speed laser communications (lasercom) link. 

This link reached Earth when Orion had a line of sight with primary optical ground stations located at NASA’s White Sands Test Facility in New Mexico and Caltech/NASA Jet Propulsion Laboratory’s Table Mountain Facility in California, or an experimental ground station at Australian National University’s Mount Stromlo Observatory. 

Together with terrestrial networks, O2O formed an internet backbone between the Artemis II Orion spacecraft and the Mission Control Center at NASA's Johnson Space Center in Texas.

Toward a high-speed internet in space

"Our goal was to demonstrate O2O's operational utility for human spaceflight, extending the high-bandwidth connections that internet users enjoy on Earth to astronauts in deep space," says lead systems engineer Farzana Khatri, a senior staff member in Lincoln Laboratory's Optical and Quantum Communications Group. "We not only demonstrated the first use of lasercom on a crewed mission beyond low Earth orbit, but also attracted massive public engagement as the astronauts shared multimedia from their journey in near-real time."

During the last missions to the moon in the late 1960s and early '70s, astronauts relied on radio-frequency systems to communicate. But radio waves can only carry so much data per second because of their low carrier frequency; the grainy, poor-quality video and images of the moon from that time speak to this limited bandwidth. 

With its much higher carrier frequency, infrared laser light can transmit 10 to 100 times more data per second than can radio waves. The switch from Apollo-era radios to Artemis-era lasers is analogous to the move from dial-up to high-speed internet. And a high-speed internet is rapidly becoming a key requirement for NASA missions as they collect more high-resolution data and push humans farther into deep space.

Lasering in on unprecedented views

During the Artemis II mission, from April 1 to 11, O2O downlinked nearly half a terabyte of data at speeds up to 260 megabits per second. This data trove contained never-before-seen views of the basins and craters on the far side of the moon, a crescent Earth setting behind the moon, a nearly hour-long total solar eclipse with other planets scattered across a star-filled sky, and flashes of light from tiny meteoroids striking the lunar surface.

"O2O was able to downlink all the data stored on multiple onboard cameras, allowing mission control to erase the memory cards and refill them with new photos and videos," explains Khatri. "For any space mission, scientists and spacecraft engineers are concerned that data not sent down during the mission can become corrupted or get destroyed. And, when the spacecraft capsule returns, downloading the data can sometimes take months. The lasercom capability provided by O2O ensured the data were preserved and immediately available for analysis."

O2O is based on the laboratory's R&D 100 Award–winning Modular, Agile, and Scalable Optical Terminal (MAScOT), which contains subassembly modules for pointing the laser beams, establishing a communications link with ground stations, and maintaining this link despite atmospheric conditions. MAScOT made its debut in space on the International Space Station in 2023, demonstrating NASA's first LEO user for their lasercom relay system.

Over the moon for O2O

Leading up to the launch of Artemis II, operations teams from the laboratory traveled to NASA's White Sands Test Facility and Mission Control Center (MCC) to conduct monthly maintenance on ground hardware and simulate different mission stages. During the 10-day mission, laboratory teams provided 24/7 coverage. 

At mission control, one laboratory team, along with NASA Goddard colleagues, interfaced with a mission flight controller to command the O2O payload, coordinated with U.S. and Australian ground terminals to bring up the O2O physical link, assessed whether overall O2O mission requirements were being met, and analyzed data to ensure payload health and optimize performance. Another laboratory team oversaw subsystems of the optical ground terminal at White Sands, while staff at the laboratory's main campus in Massachusetts offered subject-matter expertise.

Initially, O2O had a scheduled operational window of one hour per day, with the onboard radio system set to downlink most data. However, mission operators found O2O so useful that they maximized its operational time as the mission progressed. On the fly, mission operators adjusted Orion's attitude — how the spacecraft is oriented in space — so that O2O could have line-of-sight access with the ground.

"One special aspect of this mission that enabled our technology to be so impactful was the flexibility built into the planning process to account for the fact that humans hadn't been to the moon in more than 50 years, and it would be the first time sending astronauts on Orion," says Bryan Robinson, leader of the Optical and Quantum Communications Group. "An established process for making real-time changes to the plan and the willingness of operators to try out this new technology had a huge impact, even for this short mission. This impact was tangible by everyone in mission operations and by the public watching from home."

With Artemis II completed, engineers, scientists, and mission specialists are analyzing mission data. Their analyses will provide insights into spacecraft and subsystem performance and moon geology, which will inform lunar landings and deep-space exploration. While the laboratory team is still processing O2O performance data, they believe the system could downlink at least 10 times more data by improving the efficiency of the downlink process and by addressing data-flow bottlenecks in space and ground networks.

The laboratory team is now evaluating how lasercom could support future moon plans for Artemis and Ignition. Aligning with the National Space Policy to secure U.S. leadership in space, Ignition is a recently announced initiative to establish a permanent lunar base with a sustainable human presence.

"Participating in this historic mission from the MCC and having O2O be useful, I couldn't have asked for anything more amazing in my career," Khatri says.

"When I came home, I was floored by the response of people who engaged with the mission while it was happening. Much of that engagement was enabled by the technology we developed. That's a rare moment in a career doing what we do," Robinson adds. 

When you picture a worker at a retail store, you probably think of someone at a cash register or helping a customer. But employees also spend a lot of their time combing through stockrooms and shop floors, fulfilling requests or online orders and generally trying to keep track of all their inventory.

Keeping track of inventory takes so much time, in part, because retailers don’t always know where everything is located. That’s why when you ask a store associate to check if they have a shirt in your size, it may take them 20 minutes to get back to you.

Cartesian is helping retailers keep track of inventory with a technology invented at MIT. The system uses wireless signals from radio frequency identification (RFID) tags attached to items to find their precise location in a store, from the stockroom to the shop floor.

Last year, Cartesian did a study with a retailer and found its platform delivered meaningful annual savings at the store level by streamlining inventory tracking, optimizing workflows, and improving customer experiences.

“The big problem we’re solving is that about 50 percent of working hours in retail stores go to managing inventory,” says co-founder Fadel Adib SM ’13, PhD ’17, an associate professor at MIT. “That is roughly a $15 billion problem in the U.S. alone. We use algorithms to decipher indoor locations using wireless signals. The core technology enables a new level of indoor localization.”

Cartesian is already deployed in more than 700 stores across 15 countries and is working with one of the world’s largest fashion groups, Inditex, which is the parent company to brands like ZARA, Pull&Bear, and Oysho.

Beyond retailers and warehouses, Cartesian’s platform could also improve indoor location tracking for manufacturers, logistics operators, and robotics companies.

“The broad vision for what we are doing is spatial AI,” says Adib. “Today, AI does extremely well in the digital world. Now it has to move into the physical world. That means allowing machines to perceive their environment in such a way that they can interact with it. That’s where spatial AI comes in and where Cartesian sits.”

From technology to product

Adib, who holds a joint appointment in MIT’s Media Lab and Department of Electrical Engineering and Computer Science, has been studying wireless signals at the Institute for more than 15 years, dating back to research during his master’s degree.

“My group today researches how to use wireless signals to sense the world in ways that were not possible before,” Adib says. “We develop the fundamental technology and then we build systems around them. Our goal is to see these systems deployed in the real world for impact.”

When Adib joined MIT’s faculty, the first project he worked on was indoor localization using RFID tags. Isaac Perper ’20, MEnG ’21 later joined his lab as a student, and together they developed machine-learning algorithms to process RFID data to translate them into location patterns, with an initial focus on helping robots locate RFIDs indoors.

In 2021, Adib went through the National Science Foundation’s I-Corps program, which challenges researchers to interview potential customers to find the right problems to solve with their technologies. That’s when he realized how big of a problem inventory management is for retailers.

Cartesian was officially founded by Adib and Perper in the beginning of 2023, after they received a small business award from the National Science Foundation. The pair worked with MIT’s Technology Licensing Office to license patents from Adib’s lab. They also received support from MIT’s Venture Mentoring Service.

“Our goal was to reduce the cost of the technology to make it scalable,” Adib recalls. “Isaac focused on simplifying the product, leveraging progress in machine learning, and making it fast. It was a lot of iterating and testing early on.”

Retail workers spend much of their time locating items for a number of reasons. They might get an online order to fulfill, need to restock store shelves, or get a customer inquiry about items in the back.

Stores differ in how they organize their inventory. Most separate items by categories in specific shelves and bins then use barcodes or inventory systems that tend to get outdated fast.

“It’s a big problem for stores because customers may just leave before asking an employee to look for their size, or customers may get frustrated and leave if it takes too long,” Adib says. “The associate also wastes time looking for items they could spend doing higher-value work.”

Cartesian’s platform works with retailers’ existing handheld RFID readers, which store associates already use to manage inventory. Each store installs Cartesian’s software into their existing inventory apps or uses a custom app for employees to access directly.

“The RFID readers are how stores tell what’s in stock and what’s out of stock,” Perper says. “We figured out a way to leverage the same scans they’re already using with the reader, put the data they generate into our machine-learning algorithms, and generate maps of where all the items are.”

Customers can build analytics on top of Cartesian’s technology to keep track of inventory levels, show customers maps of where each item is located, and create other services.

“They use our location intelligence platform and build different products on top,” Adib says. “We can work with any device, any store, any type of RFID. It’s a simple interface. All the sophisticated location algorithms sit in the cloud.”

Beyond retail

Cartesian signed its first big contract in 2025 and soon expanded to several hundred stores. One of Cartesian’s advantages is its ability to quickly scale. Perper says they can add a store in about one minute. Cartesian’s team doesn’t even have to travel to a new store to turn on its system if it’s already working with the company.

“It’s as simple as flipping a switch, preparing the data, and sending it to our customers,” Perper says. “One of our first big bets was, ‘Can we build this entirely on existing hardware?’ That bet is starting to pay off.”

Cartesian’s models can also work with Wi-Fi and Bluetooth signals, which the company plans to use with customers in other verticals.

“Right now, we’re focused on applications in retail, but this technology has a lot of value in manufacturing, warehouses, and other locations,” Adib says.

Cartesian’s team aims to be deployed in tens of thousands of stores over the next year and then begin expanding beyond retail into industries like manufacturing and robotics.

“What’s most exciting about Cartesian to me is we’ve built a lot of the technology foundation, and now that we have the fundamentals in place, we hope to build specific application layers,” Perper says. “Then we can ask customers in different verticals about their problems and apply our technology in different ways to solve it.”

Carbon capture is an important climate change mitigation strategy, but it faces technological barriers and can be energy-intensive and expensive. To help make necessary advances in this area, a team of MIT researchers, with support from the MIT Climate and Sustainability Consortium (MCSC), are exploring energy-efficient and scalable alternatives to conventional carbon dioxide (CO2) capture methods. 

Conventional amine scrubbing, which is the current standard for CO2 capture, is energy-intensive and difficult to scale, limiting its impact despite the urgent need to reduce carbon emissions and upgrade CO2 into valuable products. In a new article published in Nature Energy, MIT researchers — graduate students Fang-Yu Kuo of the Department of Chemical Engineering, and Gi Hyun Byun of the Department of Mechanical Engineering (MechE); Professor Betar Gallant of MechE; and former MCSC postdoctoral Impact Fellows Glen Junor and Akachukwu Obi — investigate a promising alternative to these conventional CO2 capture methods. Their findings could move the needle on achieving efficient and flexible carbon capture and removal.

In their paper, the team explores an alternative, electrochemically mediated CO2 capture (EMCC). This approach enables electrification of CO2 separation — driven ideally by renewables — but currently faces challenges, such as relying on sorbents that require highly reducing potentials, where oxygen reduction side reactions become significant. This can compromise both efficiency and long-term performance. To tackle this shortcoming of EMCC, the MIT team researched whether N-heterocyclic imines (NHIs) is a useful new class of EMCC sorbent.

“NHIs have shown promise in recent years as CO2 sorbents because of the ease of NHI molecular modifications for tuning basicity,” says Fang-Yu Kuo. “Our work translates these NHIs for the first time into the EMCC application space, and demonstrates that NHI-based sorbents can be modulated electrochemically for CO2 separation by a unique separation mechanism that avoids the need of applying highly reducing potentials.”

The team’s initial research establishes a novel bis(NHI) structure that can enable a theoretical CO2 modulation of two molecules per electron during cell operation. The initial published result also indicates that with further molecular engineering of bis(NHI) structures to strengthen CO2 binding affinity, the bis(NHI) could operate in more diverse electrolyte environments, opening new possibilities to optimize system performance in terms of electron efficiency, energy efficiency, and operational flexibility.

“A critical future direction of our work involves gaining deeper mechanistic insight into the stability and degradation pathways of the bis(NHI) radical cation,” says Kuo. “Understanding these pathways will inform the rational design of next-generation bis(NHI) molecules, enabling longer operational lifetimes and enhanced cycling durability for practical deployment.”

MIT, in collaboration with Georgia State University and a growing network of educational institutions, has announced expanded work under PATH (Pathways for AI Training and Hiring) — a multiyear initiative designed to scale effective, affordable, industry-aligned AI training for entry-level and current workers, with a particular focus on transforming community colleges into engines powering an AI-enabled workforce for the nation. 

“In the era of AI, economic opportunity and mobility will increasingly depend on whether people can develop practical, industry-relevant AI skill sets and mindsets, not just familiarity with tools,” says Cynthia Breazeal, principal investigator (PI) of PATH and professor of media arts and sciences at MIT. “That means combining hands-on, work-learn experiences with strong technical foundations and the responsible design, professional, and human skills that employers are looking for.”

To make that possible, the initiative is building state-based hubs anchored by research universities and community colleges. Each hub works with regional employers to design curricula that reflect local industry needs. The program also provides professional development for instructors and develops modular, open educational materials that institutions can adapt and share.

“Artificial intelligence is shaping every sector of the economy, and the United States will need far more people who understand how to build with these technologies and apply them responsibly,” says MIT President Sally Kornbluth. “Through PATH, MIT RAISE is using our convening power to bring community colleges, industry, research universities, and government together to build human-centered AI pathways that lead to shared prosperity. When research universities contribute their expertise to expand access and economic mobility, we strengthen both the nation’s workforce and our collective capacity for innovation.”

Unlike many large-scale online training efforts, PATH emphasizes in-person, collaborative learning. Students work in teams to address real problems brought by industry collaborators. These projects mirror the kinds of challenges graduates will face in the workplace, helping them build technical skills alongside the judgment, communication, collaboration, and ethical awareness that employers increasingly value.

The initiative’s first two hubs launched earlier this year in Massachusetts and Georgia.

“As PIs for the Georgia PATH hub, we are very excited with the significant early momentum, with over 1,000 GSU students enrolled in PATH courses,” says Arun Rai, regents’ professor, Howard S. Starks Distinguished Chair, and director of the Center for Digital Innovation at Georgia State University (GSU), with Balasubramaniam Ramesh, regents’ professor and the George E. Smith Eminent Scholar’s Chair at GSU. “Our curriculum, co-designed with MIT RAISE and spanning AI foundations, data science, deep learning, and agentic AI systems, is now being shared with partner institutions including Georgia Gwinnett College, GSU Perimeter College, and Clark Atlanta University. By leveraging the University System of Georgia’s FinTech Academy to expand work-based learning opportunities, we are building a collaborative ecosystem that rapidly advances the state’s AI workforce capabilities and creates tangible, job-ready skills for our diverse student population.” 

GSU President Brian Blake says, “Our collaboration with MIT reflects a shared commitment to strengthening the nation’s AI talent pipeline. Georgia State University brings a distinctive strength to this effort — the ability to prepare students from all backgrounds for AI-enabled careers at scale. By combining academic rigor with strong industry partnerships and work-based learning, we are translating advances in AI into practical skills and expanding access to opportunities in this transformative era.”

In Massachusetts, students at Quinsigamond Community College are participating in Data Science in Action, a course that introduces AI-enabled data analysis and engineering. The class includes a hands-on Action Lab, modeled after experiential learning programs at the MIT Sloan School of Management. David Birnbach, lecturer at MIT Sloan, leads the design framework for the PATH Action Labs. Working with industry partners, students tackle real data challenges while building portfolio projects and professional connections. 

Beyond individual courses, PATH is building clearer pathways for students to turn AI learning into real job opportunities. Through industry-informed micro-credentials and a shared set of workforce skills, students will gain practical abilities that employers are actually looking for, along with the human skills needed to succeed at work, like communication, problem-solving, and collaboration. 

The MIT skills taxonomy team, led by Katerina Bagiati in collaboration with Professor Tom Malone from the MIT Sloan Center for Collective Intelligence, is mapping the skills and roles emerging in AI across fields such as financial technology (fintech), information technology, and business operations, with plans to expand into areas such as health care, manufacturing, and creative media. The goal is to help students build skills that are relevant, recognized, and directly connected to growing career paths.

The initiative is supported by a grant to MIT from Google.org, which is helping MIT and its collaborators build a multi-state network for AI workforce development.

“MIT’s PATH initiative offers a blueprint for expanding opportunity in the age of AI,” says Shanika Hope, director of Google.org. “By connecting research universities, community colleges, and industry partners, it helps translate innovation into real jobs and sustainable career pathways.”

PATH is led by Breazeal, who has brought together a cross-MIT team with expertise in AI literacy, workforce pedagogy, educator professional development, open education, research, and the future of work. Breazeal is a professor and director of the MIT RAISE Initiative. Eric Klopfer, director of the STEP Lab and co-director of the MIT RAISE Initiative, serves as a co-PI on this award. The GSU leadership team includes PIs Arun Rai and Balasubramaniam Ramesh.