New chip could help tiny robots traverse complex environments

A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.

The chip allows small autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments in real-time using only about as much power as a single LED. A robot could use such a map to plan a collision-free path to reach its goal.

Typically, generating such thorough maps requires power-hungry systems and a great deal of memory to build and store 3D representations of the obstacles in a robot’s environment.

The MIT researchers took a different approach by combining an extremely efficient mapping algorithm with specialized hardware designed to accelerate its workload, which minimizes memory and power consumption. 

This system-on-a-chip consumes only about 6 milliwatts of power, a fraction of the power required by other systems. 

This low-power operation could also make the chip well-suited for lightweight augmented reality headsets that can be worn for extended periods, for applications like educational medical simulation or detailed repair and assembly work.

“This paper showcases a key example of how you can leverage co-design of the algorithm and hardware to really push energy efficiency. While there has been a lot of work looking into compact 3D maps, what stands out about this work is that it also ensures that the process to generate those maps is as efficient as possible. Our chip allows you to store very large maps in a very small space, and do it in a very energy efficient manner,” says Vivienne Sze, a professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory of Electronics (RLE), and senior author of a paper on the chip.

She is joined on the paper by co-lead authors and MIT graduate students Zih-Sing Fu and Peter Zhi Xuan Li as well as Sertac Karaman, a professor of aeronautics and astronautics and the director of LIDS. The work was recently presented at the IEEE Very Large-Scale Integrated Circuits Symposium.

A more compact map

For a robot, generating a 3D map that includes the obstacles in its environment usually demands a lot of power because it must store images captured by its camera, and process all the 3D pixels in each image multiple times.

Instead of representing the environment using 3D pixels, which are cubes called voxels, the MIT researchers utilized a technique that maps the obstacles in space using ellipsoid blobs called Gaussians. 

The size, shape, and thickness of these ellipsoids can be smoothly adapted, so they match the shape of curved objects more efficiently than if one uses rigid, cube-shaped voxels. 

Importantly, the map captures the obstacles and free space around the robot, and together these let the robot plan a safe, collision-free path. Mapping obstacles and free space with voxels typically consumes a lot of memory, which makes traditional methods power-hungry. Because Gaussians can flexibly fit the geometry, a single elongated ellipsoid can represent a region that would take many voxels, so occupied surfaces and free space are captured far more compactly.

For their new system-on-a-chip, called Gleanmer, the researchers employed an algorithm their lab developed called GMMap that efficiently generates a 3D map of the robot’s environment using Gaussians to represent obstacles. 

With traditional approaches, a robot would need to load and process each depth image several times to adjust the size and shape of the ellipsoids. The system would usually construct Gaussians by comparing all the pixels in an image to each other. But the amount of memory and power needed to do this remains too high for many edge devices.

To solve this problem, the MIT researchers invented a technique that can generate highly accurate Gaussians from depth images with only one pass, after which they can discard the images, so the chip never has to store an entire image at once. 

Instead of comparing each pixel to every other pixel in the 3D image, their algorithm assumes that nearby pixels belong in the same Gaussian, so it only needs to compare each pixel to its neighbors.

“At any point in time, we only need to store a few pixels in memory, which significantly reduces the memory footprint our algorithm requires,” Li says.

Leveraging co-design

But as the robot moves through the space, it usually sees the same object from different viewpoints. When it generates Gaussians, some will overlap because they represent the same object. This can make the 3D map too large to store on an edge device.

Fusing overlapping Gaussians makes the map more compact, but doing so typically requires the algorithm to process many raw pixels stored in memory. The researchers developed a novel technique to perform this fusion process directly on overlapping Gaussians, without needing to revisit the original pixels. Since Gaussians are more compact than pixels, this significantly reduces memory and power requirements.

The same principle runs through their algorithm — most computations operate directly on compact Gaussians rather than the original pixels, enabling energy efficiency.

The researchers exploit this principle to design a chip that keeps the Gaussians it is actively working on within small, fast on-chip memory right beside the computational units. This is only possible because the Gaussian map is so compact.

The Gaussians the robot needs to work on next are waiting in the on-chip memory units, so they don’t need to be fetched from more distant, power-hungry, off-chip storage. 

“By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” Fu explains.

They tested the system-on-a-chip by reconstructing a range of diverse, pre-existing 3D environments. The chip can also reconstruct obstacles and free space directly from live data streamed from an iPhone camera.

Gleanmer generated detailed 3D maps in real-time while consuming about 6 milliwatts of power. It required only about 2.5 percent of the power that the best existing chip for map construction would need. 

By reusing compact Gaussians along the path as it plans, the chip lets a robot chart a safe trajectory using only about 20 percent of the energy it would otherwise need.

“We reduce the memory consumption by making sure the algorithm is efficient. Then we accelerate the workload that is performed by that efficient algorithm, so in the end, our chip is as efficient as possible,” Li says.

The researchers plan to further improve energy efficiency by moving the processing units on the chip closer to the sensors that gather environmental data. They could also explore additional applications, such as the use of Gaussians to represent schematics. This could help AI systems reason about complex blueprints more efficiently.

“Real-time 3D mapping has been the missing piece for small autonomous systems. A drone inspecting a pipeline or a pair of AR glasses navigating a room both need to understand the space around them — instantly, continuously, and at almost no power cost. Gleanmer makes that possible for the first time in a chip you can hold between your fingers,” says Karaman.

This work is supported, in part, by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel. 

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Meet the leader of the Department of Biology’s all-important “kitchen”

Early mornings in the halls of Building 68 feature the sounds of rolling wheels on big metal carts, the rattling of glassware, the whooshing of faucets, and the clanking of autoclaves. 

These aren’t the sounds of researchers at work, but rather those of keeping the labs sterilized and stocked with the sundries of research: pipette tips, test tubes, flasks, petri dishes, and more.

Orchestrating this sunrise cacophony and the staff that undertakes it is Karen O’Leary, lab associate and acting supervisor in the Glassware Sterilization Facility, also known as the “kitchen.” 

Thanks, in part, to O’Leary’s proactivity and hard work, the kitchen staff were recently recognized with an MIT Excellence Award in 2025 for exceptional contributions in service of the community. 

“My goal is to get the scientists everything they need to do their research,” O’Leary says. “I’m good at what I do.” 

O’Leary admits she did not always possess such confidence. In almost 40 years at MIT, O’Leary has grown into this critical role for the department, and the department itself has evolved, moving into a brand-new building and away from previously standard practices like submerging equipment in acid for sterilization. 

From rookie to running the show

On Sept. 7, 1987, Karen O’Leary joined the MIT community as a staff member for the first time. The 18-year-old was fresh from vocational high school, where she studied cosmetology but felt too shy to pursue that as a career. She was also nervous about joining a research institution.

“When I started, I didn’t even know what a beaker was,” she recalls. 

Too embarrassed to admit in her interview that she couldn’t remember her brand-new home phone number, “I just made one up.” Fortunately, this didn’t prevent her from getting the job, where she worked under the mentorship of Thelma Watkins, who would retire in 1996 after 21 years at MIT. Watkins was critical for instilling a good work ethic and boosting O’Leary’s confidence. 

“She taught me to show up every day, and work hard, and laugh,” O’Leary says.

Even now, O’Leary continues to bring joy to that daily diligence, for herself and for her staff.

“Karen is always on top of things,” says longtime friend and fellow Lab Associate AnnMarie Budhai. “She doesn’t refuse work and always goes above and beyond.” 

Facilities and Operations Manager Cesar Duarte says that O’Leary’s long tenure, support, and knowledge have been invaluable as he transitioned into his role in Building 68 starting in 2023.

“Karen is one of those people who makes everything around her run more smoothly and more pleasantly,” Duarte says. 

Better, faster, safer

Although some might consider it drudgery, O’Leary says that washing glassware is her favorite task. 

“I like that when I wash, I can see the job is complete at the end of the day,” she says. 

Although washing glassware is a perennial task, safety and efficiency have come a long way in the past 38 years. More-effective autoclaves and dishwashers have eliminated steps like steaming to dissolve agar solvents before autoclaving, and scrubbing individual test tubes before washing.

O’Leary was working for the department in 2011 when Building 68 piloted a new approach to MIT’s management of regulated medical waste (RMW), such as petri dishes, blood, and needles — the new system, which is cheaper and produces less waste, is now used by all departments at MIT that produce RMW.

“EHS [the Environment, Health and Safety Office] has come really far — I’m glad we got away from acid,” O’Leary notes of the bygone era of submerging glass pipettes for sterilization. “Back then, no one knew of a better way.” 

Other tasks include cleaning velvets, which are used for replicating bacterial colonies on petri dishes, and pouring agar plates. 

“Everyone knows how to do almost every job, so we can take turns doing different tasks,” O’Leary says. “If you get sick, there’s always someone to cover.”

All in the family

For O’Leary, kinship with MIT has spanned generations. O’Leary was raised in Weymouth, Massachusetts, by a father who worked at MIT as a supervisor in the sheet metal shop. Having raised children of her own, now grown, O’Leary came to greatly appreciate the flexibility her job has granted her.

“I’ve had great work-family balance here,” she says. Even though she’s often at work more than an hour before the researchers that the kitchen serves, “The hours are great, and with MIT Health right across the street, it was easy to take everyone to doctors’ appointments.” 

She’s also gained a chosen family at MIT, spending breaks at work taking long walks along the Charles River, “talking about anything and everything” with colleagues like Budhai and Lab Aide Janet Katin. 

“We really grew up together,” she says. 

Working at MIT has provided O’Leary with support and community, and she’d like to pay it forward. In addition to strolling with colleagues, she hits the gym to help maintain the energy required for her highly active work. 

“I don’t like sitting around,” she says.

In addition to maintaining her stamina at work, she hopes that taking care of herself will keep her actively involved if she ever has grandchildren, and enable her to help neighborhood kids when she someday retires.

“I owe a lot to MIT,” she says. “I have been allowed to work hard and get satisfaction and have been appreciated and given space to care for my family.”

O’Leary returns this care to the Department of Biology in spades.

“It’s an understatement to say that Biology is lucky to have her,” says Duarte. “Karen’s overflowing energy, attention to detail, and care for the Biology research community are nothing short of amazing.”

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A better way to model the behavior of metal alloys

Companies working at the frontier of aerospace, energy, and computing are constantly looking for new materials to improve performance. But in order to understand how those materials will actually behave once they’re inside rockets or on computer chips, companies first have to make the material and then test it. That’s because even the most powerful simulation techniques struggle to model the complex chemical arrangements in most of today’s solid materials. The problem adds costs and time to materials innovation.

Now a team of MIT researchers has created a way to accurately model the behavior of metals, regardless of the complexity of their chemical arrangement. At the center of the approach are machine-learning models that make simulations of materials faster and more accurate. The researchers improved those models by building training datasets that capture the diversity of atomic environments in chemically disordered materials.

In a new paper in Sciences Advances, the researchers showed their approach could be used to accurately predict material properties for a diverse group of metal alloys under a range of conditions. They also showed how the approach could be used to develop new materials, especially in scenarios where experimentation is expensive.

“The focus of the paper is metallic alloys, which is the field I work in, but this could be adapted to other types of materials, like semiconductors,” says senior author Rodrigo Freitas, MIT’s TDK Career Development Professor in Materials Science and Engineering. “This is not specific to any one application — you could use this approach to create new sustainable steels, new materials for aerospace, and more. That’s what makes this exciting.”

Joining Freitas on the paper are first author Killian Sheriff PhD ’26; MIT PhD students Daniel Xiao and Yifan Cao; and University of Sheffield Senior Lecturer Lewis R. Owen.

Modeling metals

Material properties are mostly determined by the internal arrangement of their chemical elements. Even if two materials have the same mix of chemical elements, different chemical arrangements can make the difference between a brittle material and one that deforms without breaking.

Capturing that distinction requires simulating materials atom by atom. To do that, researchers rely on models that describe how atoms interact with each other. Over the last two decades, machine learning has become the most accurate way to build those models. Such models work well when the chemical arrangements inside materials follow highly ordered patterns, but that’s not the case with most solid materials, whose atomic chemical arrangements are disordered and vary from one region to another.

“The real challenge in our field is modelling these chemically disordered phases,” Freitas says. “Chemical disorder means there’s a huge variety of local chemical environments, which is hard for the machine-learning model to learn. This is a problem because every single metal we use in practice is chemically disordered.”

The problem comes down to a lack of representative training data for those atom-by-atom simulations. The current leading approach for creating such data works by brute force, often requiring more than 100,000 hours of computation to create the training data for a single material. Even then, it does not transfer well when researchers change the material’s composition.

In previous work, Freitas’ group had developed a way to measure the chemical complexity of solid materials by analyzing the frequency and spacing of tiny groups of atoms. For this study, the researchers used that capability to build better training datasets. They used a mathematical approach known as information theory to generate training datasets that capture a wider variety of local chemical environments inside disordered materials. The method works by swapping out atoms from samples to reduce repetition and expose the model to chemical environments it might otherwise miss.

“We kept optimizing the training set so it captured as many different local environments as possible,” Freitas says. “If the same kind of environment showed up many times, we replaced redundant examples with ones the model hadn’t seen before. That makes the training set much more informative because each example adds something new.”

When trained on the researchers’ datasets, the models predicted material properties more accurately than models trained using random sampling or another popular sampling method.

“The starting point for all these atom-by-atom simulations is: Are you able to accurately describe the chemical bond between atoms?” Freitas explains. “If not, it can still teach you about materials in general, but it doesn’t tell you what will happen to specific materials in the real world. This approach makes the simulations high fidelity in terms of their chemistry, to better reflect what’s happening to materials.”

The researchers applied their technique to create machine-learning training datasets for a group of chemically diverse metal alloys. Using a set of machine-learning models, they showed the models trained on their datasets are more accurate than much larger models created by companies like Google and Microsoft.

“We got to a point where we were convinced it worked without using these expensive brute-force methods,” Freitas says. “I told Killian, ‘This is a good paper. But if you can show that simulations with these models can now accurately predict useful materials properties, then it becomes a very good paper.’ Killian took that to heart and tested this as widely as he could.”

Sheriff worked with Xiao and Cao to test the approach across different alloys and properties. The team also drew on Owen’s experimental data to compare the simulations against real measurements of atomic ordering in alloys.

From the lab to industry

The method works, in part, by capturing hidden patterns in the sample data. The researchers describe the patterns in the paper as “subtle energetic biases toward certain local chemical configurations.”

Those small energetic differences matter because they determine which phases form in an alloy, how those phases change with temperature and composition, and ultimately which properties the material will have. As one test, Daniel Xiao led simulations showing that the team’s models could predict phase diagrams that closely matched experimental data. Phase diagrams map which phases are stable across different temperatures and chemical compositions, and they are a central tool for designing and processing alloys.

“Phase diagrams are one of the main ways people connect materials modeling to real processing decisions,” Freitas says. “If you are welding, casting, or heat-treating an alloy, you need to know which phases are likely to form under different conditions. Our goal is to make these kinds of predictions accurate enough, and accessible enough, that they become part of how people design materials.”

The researchers are now using the approach to study how changing an alloy’s composition affects mechanical properties and radiation tolerance, with the goal of designing materials that remain strong and damage-tolerant in harsh environments. They are also working to make the method easier to use with the kinds of tools and workflows materials engineers already rely on.

“Industry isn’t going to change the way they do things if what you’re creating doesn’t fit into their existing operating procedures,” Freitas says. “The goal is to make these predictions useful in the places where materials decisions are actually made.”

The research was supported by the U.S. Air Force Office of Scientific Research.

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MIT in the media: For the future of tech, "Massachusetts can absolutely lead"

On June 9, The Boston Globe released its 2026 “Tech Power Players” list, recognizing 50 influential local leaders in technology and business across Massachusetts. The list includes eight MIT affiliates including President Sally Kornbluth, Prof. Daniela Rus (director of CSAIL), Prof. Regina Barzilay, Prof. Yet-Ming Chiang, Prof. Max Tegmark, Ana Bakshi (executive director of the Martin Trust Center for MIT Entrepreneurship), Katie Rae CEO and Managing Partner of Engine Ventures), and Senior Lecturer Brian Halligan, along with a number of MIT alumni.

In addition to recognizing individual leaders, the Power Players coverage highlights MIT’s research labs, its culture of innovation and entrepreneurship, industry connections, new AI initiatives, and the Institute’s deep commitment to maintaining Massachusetts’ technological leadership.

“Massachusetts can absolutely lead in this next wave,” says President Kornbluth, noting that the future is bright with burgeoning opportunities to advance technologies in fields from manufacturing, life and health sciences to quantum technologies and energy in service of Americans across the country.

Advancing AI and entrepreneurship 

When it comes to AI, MIT is “working to drive artificial intelligence forward in sectors where the region is strongest, from biotechnology and robotics to defense and clean energy. It’s also trying to broaden entrepreneurship through a ‘dorm-to-startup’ push, creating a pipeline of support services — from hack-a-thons to venture funding — to help students to start companies between classes,” writes Robert Weisman for The Globe. 

Looking ahead, The Globe highlights how MIT aims to remain a central driver of AI advancement within higher ed. 

“President Sally Kornbluth is reinvigorating the school’s support of the local innovation ecosystem,” writes Aaron Pressman, noting how MIT is “unveiling new online classes dedicated to AI — with free entry-level classes for anyone — and encouraging more entrepreneurship on campus.”

MIT’s free, online AI courses could help local tech leaders in their challenge “to ensure people, not only corporations, benefit from the technology,” writes Pressman.

And when it comes to applying AI technologies to real-world problems, MIT aims to ensure the greater Boston area remains a leader.

“Some schools in Massachusetts, including MIT, are carving out a specialty in applied AI — sometimes called ‘AI+X’ — deploying the technology to help businesses, hospitals, and research institutions to supercharge productivity, innovation, and scientific breakthroughs,” explains Weisman.

Aman Narang ‘04, CEO of Toast, adds: “The superpower has always been the university system. The best thing Boston can do is keep these people around.”

MIT startups are a key driver of the region’s entrepreneurial ecosystem. To ensure the greater Boston area remains a hub for innovators and to respond to growing student interest, MIT is looking to build upon its existing entrepreneurship resources for students, including the more than 150 courses and 85 centers and programs dedicated to fostering an entrepreneurial community. Additionally, President Sally Kornbluth and Provost Anantha Chandrakasan recently formed the Committee on Accelerating Translation and Entrepreneurship (CATE) to explore anew how the Institute can best support, remove barriers to, and accelerate the movement of ideas from MIT’s research and innovative discoveries into new ventures. 

Further, reflecting on the optimism surrounding the Greater Boston tech scene, The Globe describes how applications for The Martin Trust Center for MIT Entrepreneurship’s startup accelerator program have doubled from last year, and nearly one-fifth of MIT undergraduates — about 800 students — attended a recent startup career fair.

Innovating change beyond MIT

The simple worm could drive the future of AI. This might sound like a squishy premise, but that’s the idea behind MIT startup Liquid AI, which is developing AI models inspired by the brain structure of a simple worm and could significantly reduce AI energy consumption. Liquid AI’s models, “which can uncover financial fraud and pilot autonomous drones, require far less electricity to operate than large language models, saving energy and water, which is used to cool data centers,” Pressman explains.

The Globe highlights how Liquid AI recently signed a deal with Mercedes-Benz to incorporate its technology into the onboard systems of cars sold in North America.

To power new AI technologies – and ensure Americans across the country can have reliable and affordable energy sources – researchers at MIT and a number of alumni are also turning their attention to the future of energy. 

In Prof. Yet-Ming Chiang’s lab, researchers are developing batteries that can store more electricity over longer periods, creating “more opportunities for wind, solar, and other clean energy sources.”

Weisman highlights how “Chiang’s lab and other MIT research centers are also working on innovations in microchips, critical minerals, fusion technology, and defense tech. All are examples of ‘tough tech’ projects combining science and engineering, which Chiang says ‘are in the sweet spot of the Boston ecosystem.’“

Soon, 80 MIT students will work as summer interns and employees at GE Vernova, thanks to the MIT-GE Vernova Climate and Energy Alliance, a collaboration aimed at advancing research and education that will accelerate the global energy transition.

GE Vernova CEO Scott Strazik wanted his organization to “plug into the city’s innovation culture,” particularly the MIT campus and community. The company announced it would dedicate $50 million over five years to fund internships and research projects in which students and faculty work alongside GE Vernova engineers and technicians.

The most promising area for the Greater Boston tech scene

The Globe concludes by asking each Power Player what the most promising thing about the Greater Boston tech scene is right now.

For Rus, the answer is: “talent. Boston has the best AI researchers in the world, and they're producing genuinely new ideas, not incremental ones,” she explains. 

When it comes to realizing the potential of fusion energy, Bob Mumgaard SM ’15, co-founder and CEO of Commonwealth Fusion Systems, explains that he couldn’t have built the company anywhere but Massachusetts thanks to the region’s expertise in engineering, designing, and manufacturing hardware and equipment and access to university researchers.

“The ecosystem has the building blocks,” says Mumgaard. “Massachusetts is the strongest in the nation in innovation in energy.”

President Kornbluth points to quantum.

“There isn’t a more important technological field right now than quantum science and technology, and the Boston area has the greatest concentration of quantum talent anywhere in the world,” Kornbluth emphasizes.

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“We can’t ship goods without functioning ports”

In the small coastal town of Prince Rupert, British Columbia, the port is the backbone of the community.

Growing up there, with a father who works as a longshoreman, Chelsea Mitchell witnessed the port’s importance firsthand. From an early age, she understood that the port was essential to the transportation of goods in and out of not only Prince Rupert but all of British Columbia’s North Coast. Disruptions to port operations could have ripple effects reaching from dockworkers’ families to the regional economy and beyond. 

“The port is central to my hometown’s economy,” Mitchell says. “Having family in the industry gave me visibility into the complexity and the volatility of the shipping industry.”

Today, that industry and the forces that shape it are the subject of Michell’s research as a fourth-year PhD student in MIT’s Department of Economics. She studies how ports and shipping companies compete, how goods move through congested terminals, and how disruptions affect global supply chains.

“When I was younger, I never would have imagined I would get to conduct research at MIT,” Mitchell says. “Prince Rupert is largely a blue-collar town, so I had minimal insight into the world of academic research growing up. But in high school I realized I thrived in an academic environment, especially studying math, and hoped one day I could pursue a PhD.”

She left British Columbia to attend the University of Toronto, where she studied math and economics. There, faculty mentors introduced her to economic research and encouraged her to apply to doctoral programs, eventually leading her to the Institute.

“I was lucky to have mentors in college who encouraged me to apply to MIT. The level of support and quality of advising here has consistently amazed me,” Mitchell says.

Her research focus became clearer in 2023, when longshore workers along Canada’s West Coast walked off the job during a labor dispute centered, in part, on automation and its effect on port employment. The strike lasted roughly two weeks and shut down 35 terminals across the province. That experience left a lasting impression on Mitchell.

“These labor disruptions made me acutely aware that ports were a choke point in our supply chains,” Mitchell says. “They seemed understudied relative to how important they are.”

Because of her family’s ties to the industry, Mitchell was able to spend time speaking not only with her father’s co-workers who were involved in the strike but also with people working throughout the shipping industry. 

One of her first major projects examined labor negotiations and competition among American ports. She found that even just the possibility of work disruptions in ports could alter shipping patterns, prompting companies to reroute cargo away from West Coast ports and toward East Coast facilities despite added logistical cost.

Her current work focuses on another major shift in the industry: the growing number of shipping companies that own container terminals.

Traditionally, carriers relied on independent terminal operators to load and unload cargo. Increasingly, however, major shipping lines have begun acquiring terminals themselves. Using detailed vessel-tracking and port-call data, Mitchell studies what happens after those acquisitions occur.

Her findings suggest that ships operated by the acquiring carrier often receive faster service, particularly during periods of congestion when terminal capacity is limited. Competing carriers, meanwhile, face longer wait times and are more likely to divert cargo to other terminals.

“Ports are notoriously capacity constrained, but all carriers need access to them,” Mitchell says. “A central question is what advantages these acquisitions create and whether they affect competition.”

More broadly, Mitchell hopes her work highlights the importance of an industry that has often gone unnoticed by consumers. Approximately 80 percent of global trade moves by sea, making ports essential infrastructure for the modern economy.

“People have become increasingly aware of the shipping industry, but we can’t ship goods without functioning ports,” she says. “We want ports to be reliable and efficient so that supply chains function and goods can remain affordable.”

Mitchell credits her advisors, Nancy Rose and Tobias Salz, with helping her navigate her research, especially through difficult obstacles. More broadly, she says the people she has met at MIT have been the most rewarding part of her experience thus far.

Outside of economics, Mitchell enjoys exercising, skiing, reading, and spending time with friends. She finds that having a work-life balance is essential to her success as a researcher.

“Research is extremely challenging,” Mitchell says. “You invest a lot of time trying to answer questions that you don’t necessarily know are answerable given the data you have. It’s important to have rewarding aspects of your life outside of research that can help keep you motivated.”

Still, whether she is analyzing data in Cambridge, Massachusetts, or returning home to the rugged coastline of northern British Columbia, Mitchell takes a people-first approach to her research.

“I see numbers. I see data. But it’s challenging to tell a story with that data when you don’t have insights from the people who are actually doing the work,” Mitchell says. “Talking to people in the industry has been fundamental to understanding what’s really happening.”

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QS ranks MIT the world’s No. 1 university for 2026-27

MIT has again been named the world’s top university by the QS World University Rankings, which were announced today. This is the 15th year in a row MIT has received this distinction.

The full 2027 edition of the rankings — published by Quacquarelli Symonds, an organization specializing in education and study abroad — can be found at TopUniversities.com. The QS rankings are based on factors including academic reputation, employer reputation, citations per faculty, student-to-faculty ratio, proportion of international faculty, and proportion of international students. 

MIT was also ranked the world’s top university in 12 of the subject areas ranked by QS, as announced in March of this year. 

The Institute received a No. 1 ranking in the following QS subject areas: Chemical Engineering; Civil and Structural Engineering; Computer Science and Information Systems; Data Science and Artificial Intelligence; Electrical and Electronic Engineering; Engineering and Technology; Linguistics; Materials Science; Mechanical, Aeronautical, and Manufacturing Engineering; Mathematics; Physics and Astronomy; and Statistics and Operational Research.

MIT also placed second in seven subject areas: Architecture/Built Environment; History of Art; Biological Sciences; Economics and Econometrics; Marketing; Natural Sciences; and Statistics and Operational Research.

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Flexible cryogenic cables solve a challenge in quantum system development

By harnessing the unique properties of quantum mechanics, scientists and engineers worldwide seek to enable systems with extraordinary capabilities. Many of them are working on the highly anticipated development of quantum computers capable of completing complex calculations at unprecedented speeds. These computers could meet the growing computational demands of both scientific research and data-intensive industries like finance, cybersecurity, and medicine.  

Necessary for quantum system development is an environment in which the fragile nature of quantum bits (qubits) is stabilized and the thermal noise (fluctuations in current/voltage) inherent in superconducting electronics is dampened. That environment requires cryogenic temperatures, those ranging from 5 to 10 millikelvins, colder than the extreme temperatures encountered in space. Dilution refrigerators create this needed cryogenic condition.

Dilution refrigerators used for quantum R&D need a wiring system that can operate in cryogenic temperatures, maintain a power-efficient direct current, and support high-speed data transmission. Researchers at MIT Lincoln Laboratory prototyped flexible, ribbon-like, low-frequency (LF) cables that not only meet these demands, but also are compatible with commercial circuit-board manufacturing processes. Maybell Quantum, a Colorado-based company supplying hardware for developing quantum systems, licensed the design for these cables and is adapting them for use in their dilution refrigerators.

"We’re planning to integrate Maybell LF CryoTrace, the ribbon wiring system transferred from MIT Lincoln Laboratory, across all thermal stages of our dilution refrigerators. Initially, the cables will be used for LF services, such as thermometry, heaters, and sensors, with feasibility studies planned for additional functions," says Lasse Nielsen, strategy and operations lead at Maybell Quantum. "After qualification testing, LF CryoTrace is planned for the next iteration of our internal wiring across the Maybell product family."

Motivation for invention

To support government initiatives in quantum computing, the Lincoln Laboratory research team investigated alternatives to conventional coaxial cables for use in hardware like dilution refrigerators. Coaxial cables can generate heavy heat loads for cryogenic hardware to address. And, as the number of qubits in quantum computers will increase, so will the number of coaxial cables in the infrastructure, making it difficult to fit stiff, bulky cable arrays into hardware supporting superconducting qubits.    

The team chose a stripline cable configuration with conductive layers positioned between flexible polymer layers that shield against electromagnetic interference (also known as crosstalk). Striplines offer consistency across different frequencies and minimal signal loss. The new cables were designed to accommodate large numbers of simultaneous signal transmissions; support direct-current operation without warming the cryogenic environment; and, importantly, provide easier integration into hardware than achievable with brittle coaxial cables.

"The main innovation is that the laboratory's cables can be fabricated by a traditional printed-circuit-board manufacturer. They're cheaper to fabricate and easier to install than traditional coaxial cables," says John Cummings, a principal investigator in the flexible cables project of the Lincoln Laboratory Quantum-Enabled Computation Group.

Citing ease of installation and durability as two factors making these cables attractive, Maybell Quantum says the ribbon format is mechanically robust, reducing handling-related breakages common with thin coaxials and improving repeatability in production. The supple flex cables allow assembly tasks that took days to complete to be done in a few hours.

"Over time, we think ribbonized, quantum-specific internal wiring can reshape manufacturing norms: faster and more consistent builds, easier field service, and more modular upgrades," Nielsen says.

Future outlook

Maybell Quantum is looking toward supporting quantum computing's transition from a laboratory-based capability to an industrial, commercially viable one. The huge gap between the current highly specialized quantum-laboratory environment and the robust infrastructure required for future industrial quantum computing lies in the hardware promoting the development of functional chips.

Maybell's mission is to develop reliable tools that commercial developers of quantum computers can use with ease and without the high costs and expert training associated with the equipment in today's quantum labs. The flex cables and Maybell's continued R&D into their capabilities and integration into various tools will foster a future infrastructure that could enable industry to scale manufacture of quantum computers to a level at which these powerful machines could cost-effectively find use in myriad enterprises.

"If you want to scale to hundreds of chips, you need interconnects that can handle more signals more reliably. That’s why the Lincoln Laboratory cables are so exciting for us — they enable true scalability," says Kyle Thompson, founder and chief technology officer of Maybell Quantum. "We believe this technology will materially improve our systems and strengthen the broader U.S. quantum ecosystem by moving federally funded innovation into American manufacturing."

de MIT News https://ift.tt/Rvprm9Q