Dark matter is thought to make up most of the matter in the universe, but the only way it interacts with its surroundings is through gravity. If two colliding black holes spiral through a dense region of dark matter and merge, gravitational waves rippling across space and time could carry an imprint of that dark matter.

Now, physicists may be able to spot such imprints of dark matter in gravitational waves that are detected on Earth. 

Researchers at MIT and in Europe have developed a method that makes predictions for what a gravitational wave should look like if it were produced by black holes that moved through dark matter, rather than empty space. They applied the technique to publicly available gravitational-wave data previously recorded by LIGO-Virgo-KAGRA (LVK), the global network of observatories that detect gravitational waves from black hole mergers and other far-off astrophysical sources.

The researchers looked through the gravitational-wave signals recorded over the LVK’s first three observing runs. From 28 of the clearest signals, the team found that 27 originated from black holes that merged in a vacuum, as physicists expected. But the pattern of one signal, GW190728, showed possible signs of a dark matter imprint. 

The scientists emphasize that they have not detected dark matter. Rather, the new method offers a new way to screen gravitational-wave data for hints of dark matter, which physicists can then follow up and confirm with other techniques. 

“We know that dark matter is around us. It just has to be dense enough for us to see its effects,” says Josu Aurrekoetxea, a postdoc in the MIT Department of Physics. “Black holes provide a mechanism to enhance this density, which we can now search for by analyzing the gravitational waves emitted when they merge.”

Aurrekoetxea and his colleagues report their results in a study appearing today in Physical Review Letters. The study’s co-authors are LVK member Soumen Roy of Université Catholique de Louvain (UCLouvain) in Belgium, Rodrigo Vicente of the University of Amsterdam, Katy Clough of Queen Mary University of London, and Pedro Ferreira of Oxford University. 

A dark pull

Dark matter is an invisible, hypothetical form of matter that, unlike normal everyday matter, has no interactions with the electromagnetic force. Dark matter can pass through light, magnetic fields, and any other form of energy along the electromagnetic spectrum without leaving a trace. The only evidence that dark matter exists is through its apparent interaction with one other force: gravity. 

By observing how gravity bends around distant galaxies, astronomers have surmised that there must be an extra force, outside of the galaxies’ own gravitational pull, to explain the bending fields, or “lensing.” This extra force, physicists suspect, is dark matter, which could account for over 85 percent of the matter in the universe. But exactly what dark matter is is a matter of huge debate, with theories for dark matter particles that range widely in particle size and properties. 

One class of proposed dark matter consists of “light scalar” particles, whose masses are many orders of magnitude lighter than an electron. Theorists predict that such dark matter should behave not just as particles, but also as coordinated waves when moving near black holes.

When waves of dark matter come in contact with a rapidly spinning black hole, physicists predict that the black hole's rotational energy can be transferred to the dark matter, amplifying it. This phenomenon, known as superradiance, would whip up the waves to extremely high densities of dark matter, akin to churning cream into butter.

At high enough densities, light scalar dark matter, which is invisible by all other accounts, should leave an imprint on the gravitational waves that reverberate from the colliding black holes. 

But exactly what would that imprint look like? And could such an imprint be detectable in gravitational waves that arrive on Earth, from black holes that merged many millions of light years away? 

For answers to those questions, Aurrekoetxea and his colleagues developed a model to predict the gravitational waveform, or the pattern of gravitational waves that two black holes would produce, if they collided in an environment of dark matter, versus in a vacuum (empty space, with no dark matter). 

An imprint’s prediction

For their new study, the team performed detailed numerical simulations to predict the gravitational wave that would be produced given various properties of two colliding black holes — a system known as a “black hole binary.” They considered black hole binaries across a range of scenarios and properties, for example, varying the size and mass of each black hole, the environment of dark matter that the black holes might pass through, and the density of the dark matter that the black holes would spin up. 

They designed the model to predict what a gravitational wave from a black hole binary would look like if it carried an imprint of dark matter, and furthermore, what that wave would look like if it traveled a given distance across space and time, to eventually arrive at a detector on Earth.

With their model, they looked to see whether any gravitational-wave signals that have been detected on Earth match their predicted patterns of dark matter imprints. To do so, they applied the model to publicly-available data recorded by LVK over the observatories’ first three observing runs. The observatories have picked up hundreds of gravitational-wave signals during this period. For their purposes, the researchers focused on the clearest signals, comprising gravitational waves from 28 separate events. 

For each event, the team compared the pattern of the actual gravitational wave against their model of what the signal would look like if it were generated by the same event in an environment of dark matter. They also compared the gravitational wave to the more expected scenario in which the signal was produced in a vacuum. 

Of the 28 clearest signals that they analyzed, 27 were solidly within the predictions for having been produced in a vacuum. However, the pattern of one event, GW190728, showed a “preference,” or an agreement with the team’s dark matter model. In other words, the signal may carry an imprint of dark matter. 

GW190728 is a gravitational wave that is named after the date that it was detected — on July 28, 2019. Scientists previously determined that the gravitational wave originated from a black hole binary with a total mass of about 20 times the mass of the sun. With their model, the team showed that such a system could have merged through a dense cloud of dark matter and produced a similar gravitational wave to GW190728. 

“The statistical significance of this is not high enough to claim a detection of dark matter, and further checks should be performed by independent groups,” Aurrekoetxea says. “What we think is important to highlight is that without waveform models like ours, we could be detecting black hole mergers in dark matter environments, but systematically classifying them as having occurred in vacuum.”

“We now have the potential to discover dark matter around black holes as the LVK detectors keep collecting data in the coming years,” says co-author Soumen Roy, who led the data analysis part of the work. “It is an exciting time to search for new physics using gravitational waves.”

“Using black holes to look for dark matter would be fantastic,” adds co-author Rodrigo Vicente, who developed the analytical model of the signal. “We would be able to probe dark matter at scales much smaller than ever before.”

This work was supported, in part, by the U.S. National Science Foundation and MIT’s Center for Theoretical Physics — a Leinweber Institute.

Using a new technique that can create vacancies at any site across a material and then shrink it to about 1/2,000 of its original volume, MIT researchers have designed nanotechnology devices that could be used for optical computing and other applications involving the manipulation of visible light.

The new fabrication technique, known as “implosion carving,” allows researchers to imprint features throughout a hydrogel using photopatterning. If patterned with a resolution of about 800 nanometers, these features can then be shrunk to less than 100 nanometers. 

Because that resolution is smaller than the wavelength of light, the devices can bend light in specific ways that allow them to perform optical computations.

“In order to enable nanophotonic applications in visible light, we need to make nanostructures with feature sizes with a resolution less than 100 nanometers. Only in that way can we precisely create the structure that can manipulate visible light,” says Quansan Yang, a former MIT postdoc, now an assistant professor at the University of Washington, and one of the lead authors of the new study.

In their paper, the researchers demonstrated a photonic device that can perform a simple digit-classification task, but future versions could be used for high-speed imaging and information processing, they say.

Gaojie Yang, a former MIT postdoc, is the co-lead author of the paper, which appears today in Nature Photonics. The paper’s senior authors are Peter So, director of the MIT Laser Biomedical Research Center (LBCR) and an MIT professor of biological engineering and mechanical engineering, and Edward Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT and a professor of biological engineering, media arts and sciences, and brain and cognitive sciences. Boyden is also a Howard Hughes Medical Institute investigator and a member of MIT’s McGovern Institute for Brain Research, the Yang Tan Collective, and Koch Institute for Integrative Cancer Research.

Nanoscale feature sizes

Photonic devices, which transmit and manipulate light, hold potential for use as optical computer chips that could offer an energy-efficient alternative to semiconductor chips. However, existing techniques for creating 3D photonic devices haven’t yet achieved the 100-nanometer resolution that is needed to channel visible light, which has wavelengths between 380 and 750 nanometers.

Using an additive manufacturing technique called two-photon lithography, researchers can use light to create 3D nanoscale features, but with a resolution larger than 100 nanometers. Another technique, known as electron-beam lithography, can be used to etch smaller-resolution features onto a silicon chip, but it doesn’t generate 3D structures. 

To make 3D devices with the necessary feature size, the researchers extended the concept of “implosion fabrication,” which Boyden’s lab developed in 2018, to create a new variant called “implosion carving.” In implosion carving, a laser creates vacancies — tiny voids where the hydrogel material has been removed — at precisely targeted locations. These vacancies exhibit different optical properties than the surrounding hydrogel. The hydrogel is then shrunk to bring the patterned features down to the nanoscale.

The carving process begins with immersing the hydrogel in a photosensitizing dye. Then, the researchers use a laser to excite the photosensitizer at specific places in the gel, which in turn generates reactive oxygen species that cut the bonds holding the hydrogel together. This creates a vacancy in that spot.

Once the desired vacancy pattern has been carved into the hydrogel, the researchers shrink it using a two-step process. First, they soak it in a solution containing ions, which causes it to shrink about tenfold in each dimension. To shrink it a little more, and to remove the watery solution, the hydrogel then undergoes a process called supercritical drying, which can remove liquid from a gel without damaging it.

At the end of the process, the hydrogel has been shrunk more than tenfold in each dimension, leading to a 2,000-fold reduction in volume. 

Computing with light

To demonstrate the versatility of this technique, the researchers used it to create several 3D shapes, including a helix and a structure inspired by a butterfly wing. Some of these structures are too thin, and have too high an aspect ratio, to be stably created using conventional two-photon lithography.

The researchers also created a device that could perform a simple calculation known as digit classification, a task that is traditionally used to test the performance of neural networks. During this task, the device was presented with a digit, such as 1 or 5, and had to light up a specific location to indicate which number was detected.

To achieve this, the researchers patterned vacancies throughout the device so that it would act like a neural network. The pattern of vacancies would diffract input light as it passed through many layers of patterned hydrogel, so that the output light was determined by the shape of the digit that was entered into the system.

“This is a purely optical system that effectively performs optical computing,” So says. 

“One of the very attractive features of this technology is that you can manipulate the property of the material at every tiny location,” says Dushan Wadduwage, an assistant professor at Old Dominion University and former MIT postdoc, who is also an author of the paper. “You have millions of different locations that you need to decide the property of, and that turns into a really interesting design problem where we can use deep-learning algorithms to find designs over these millions of parameters and come up with parts that go into optical systems in new ways.”

The researchers now plan to use the same principles to build optical devices that could classify cells based on their state as they flow through a microfluidic device. This could help identify rare cells such as circulating tumor cells in a blood sample, they say. 

This approach could also enable the creation of high-throughput imaging techniques for applications such as analyzing tissue samples from biopsies or surgical specimens. And, if adapted to work with other materials such as hydrophobic polymers, it could also be used to create channels within 3D nanofluidic devices. 

Other authors of the paper include Gaojie Yang, Takahiro Nambara, Hiroyuki Kusaka, Yuichiro Kunai, Alex Matlock, Corban Swain, Brett Pryor, Yannick Salamin, Daniel Oran, Hasindu Kariyawasam, Ramith Hettiarachchi, and Marin Soljacic. 

The research was funded, in part, by the MIT-Fujikura Partnership Fund, the U.S. Army Research Office through the Institute for Soldier Nanotechnologies at MIT, Lisa Yang and Y. Eva Tan, John Doerr, the Open Philanthropy Project, the Howard Hughes Medical Institute, and the U.S. National Institutes of Health.

Quantum computers could someday solve pressing problems that are too convoluted for classical computers, such as modeling complex molecular interactions to streamline drug discovery and materials development. 

But to build a superconducting quantum computer that is large and resilient enough for real-world applications, scientists must precisely engineer thousands of quantum circuits so they perform operations with the lowest possible error rate.

To help scientists design more predictable circuits, researchers from MIT and Lincoln Laboratory developed a technique to measure a property that can unexpectedly cause a superconducting quantum circuit to deviate from its expected behavior. Their analysis revealed the source of these distortions, known as second-order harmonic corrections, leading to underperforming circuit architectures.

The MIT researchers fabricated a device to detect second-order harmonic corrections, identify their origin, and precisely measure their strength. This technique could help scientists deliberately design quantum circuits that can counteract the effects of these deviations.

This is especially important in larger and more complicated quantum circuits, where the negative impact of second-order harmonic corrections can be amplified. 

“As we make our quantum computers bigger and we want to have more precise control over the parameters of these devices, identifying and measuring these effects is going to be important for us to have a precise understanding of how these systems are constructed. It is always important to keep diving down into the circuit to see if there is an effect you didn’t expect, which impacts how your device is performing,” says Max Hays, a research scientist in the Engineering Quantum Systems (EQuS) group of the Research Laboratory of Electronics (RLE) and co-lead author of a paper on this research.

Hays is joined on the paper by co-lead author Junghyun Kim, an electrical engineering and computer science (EECS) graduate student in the EQuS group; senior author William D. Oliver, the Henry Ellis Warren (1894) Professor of EECS and professor of physics, leader of the EQuS group, director of the Center for Quantum Engineering, and associate director of RLE; as well as others at MIT and Lincoln Laboratory. The research appears today in Nature Physics.

A pair-wise problem

In a quantum computer that utilizes superconducting circuits, which is one of many potential computing platforms, Josephson junctions are critical elements that enable the transfer and manipulation of information. These devices utilize two superconducting wires that are brought very close together, with a nanometer-scale barrier between them. Like a traditional circuit, the electric charge in Josephson junctions is carried by electrons. 

But in a superconducting circuit, charge-carrying electrons pair up, forming what are called Cooper pairs. These Cooper pairs can “quantum tunnel” through the barrier between the two wires, transporting current from one wire to the other.

Cooper pairs can usually only tunnel one pair at a time, which is a key property that makes quantum computation possible. 

“If you try to force more Cooper pairs through, it just doesn’t work. This non-linear effect is extremely important for all our circuits. If we didn’t have that effect, then we wouldn’t be able to control or manipulate any quantum information that we store in these circuits,” Hays explains.

But sometimes, Cooper pairs can unexpectedly squeeze through the barrier two at a time, an effect that is known as a second-order harmonic correction. This effect limits the performance of a quantum circuit that has been configured to only allow single-pair tunneling.

“If two Cooper pairs tunnel at the same time, then the assumption we used to build our circuit doesn’t apply anymore. We need to fix the circuit so it can handle that,” Kim says.

But before they can fix the circuit, scientists need to know the source and strength of these distortions.

To obtain this information, the MIT researchers fabricated a quantum circuit so it would be very sensitive to these effects. Essentially, the device is designed to suppress the quantum tunneling process of single Cooper pairs, while allowing the two-pair tunneling process to continue. 

In this way, they can detect the presence of second-order harmonic corrections and precisely measure their strength. 

Straight to the source

They can also use this circuit to pinpoint the source of these harmonics, which helps researchers identify the best way to correct for them. 

There are two potential sources of second-order harmonics — one source is intrinsic to the dynamics of the Josephson junction and the other is caused by the wires connecting the junction to other circuit elements. 

While prior research had indicated the second-order harmonics could be due to the dynamics of the junction, the MIT researchers found that additional inductance — the tendency to oppose changes in the flow of electric current —from wires in the circuit was the actual source in their devices. 

“This is important because, if we know where the second-order harmonic correction is coming from, we can predict how strong it is likely to be, and use that information to engineer more predictable circuits that will hopefully perform better,” Hays says.

In the future, the researchers want to design experiments that more accurately predict how a device will perform when second-order harmonic corrections occur. They also want to study other sources of second-order harmonic corrections and whether those sources could have negative impacts on a circuit under different fabrication conditions.

This work is funded, in part, by the U.S. Department of Energy, the U.S. Co-design Center for Quantum Advantage, the U.S. Air Force, the Korea Foundation for Advanced Studies, and the Intelligence Community Postdoctoral Research Fellowship Program at MIT. 

Despite regional variability in climate, electricity sources, congestion, and the wide variation in individual driving patterns, electric vehicles generate less greenhouse gas emissions and do not cost more than comparable gas-powered vehicles for drivers and vehicle fleet owners in most parts of the United States, according to a new study by MIT researchers.

The team’s approach captures many key factors that contribute to regional and individual differences in the life-cycle emissions and ownership cost of electric vehicles, including meteorological data, the distance and duration of trips, and fuel prices.

To paint a fuller picture of emissions and costs than was previously available, the researchers sourced data from thousands of U.S. zip codes and drilled down to the level of individual drivers within those locations. Their study considers time-averaged fuel prices so as not to be overly influenced by fluctuations in prices at any one point in time. They finalized their analysis at the end of 2024 and early 2025.

Their results indicate that a person’s driving behaviors can matter as much as regional factors like the local electricity mix when it comes to the emissions savings of an electric vehicle, compared to a similar gas-powered vehicle. In most locations, a battery-electric vehicle reduces emissions between 40 and 60 percent, with larger impacts in urban areas. 

They also found that colder climates do not reduce overall emission benefits as much as some media reports assume.

The researchers utilized this detailed analysis to update a public tool they previously developed, carboncounter.com, which enables individuals to compare the life-cycle emissions and total ownership costs of nearly any car on the market. A new version of carboncounter.com is also being released today.

“There are a lot of statements being thrown around, like that electric vehicles don’t reduce emissions very much in cool climates, and we wanted to analyze these factors systematically and evaluate these statements against one another simultaneously. Rather than simply asking, ‘Are EVs better?’, this paper helps answer ‘better for whom, and under what conditions?’” says Marco Miotti PhD ’20, a senior researcher at ETH Zurich who completed this research while a graduate student in the Institute for Data, Systems, and Society (IDSS) at MIT. 

He is joined on the paper by senior author Jessika Trancik, a professor in IDSS. The research appears today in Environmental Research Letters.

A holistic approach

Many prior studies that compare emissions and costs of electric vehicles (EVs) to combustion-engine vehicles cover a few factors, like the amount of renewable energy in the grid and how gas prices impact affordability, Miotti says.

“To our knowledge, there have been few efforts so far that bring all these factors together. But if someone wants to buy a car and have a better understanding of the factors that affect emissions and costs, this holistic approach is important,” he adds.

The researchers focused on two types of EVs: battery-electric vehicles, which only operate on electricity, and plug-in hybrid electric vehicles, which also have a combustion engine that works in tandem with the battery to optimize fuel savings.

The team expanded and improved a set of previously developed vehicle cost and emissions models to incorporate a wider variety of factors and data types.

For instance, they refined an existing model that estimates energy use and gas mileage so it could capture more nuances of local climate variability. 

“But the real effort was not just in extending these different models, but in bringing together all these different data and making them work with the models in a consistent manner,” Miotti says.

The team sourced data on a wide variety of factors for each U.S. zip code, such as typical drive cycles, the amount of traffic, local gas and electricity prices, makeup of the regional electricity mix, meteorological profiles, and more. They used statistical approaches to amalgamate different types of data. 

For example, the team used a probabilistic matching technique to combine data on how often people drive, which was drawn from nationwide travel surveys, with more detailed GPS data that includes factors like drivers’ acceleration patterns and the distance they usually drive on each day of the week.

The researchers designed their analysis to focus on the spatial picture of emissions and costs, based on U.S. zip codes, while simultaneously considering the impact of the size and features of each specific vehicle model.

“At the end of the day, it’s the vehicle and fleet owners who make decisions about vehicle purchases. So, we wanted to make sure to consider their wide-ranging individual perspectives rather than simply performing a region-by-region comparison,” says Trancik.

Lower emissions, comparable costs

In the end, their modeling framework revealed that all factors they analyzed matter about equally in determining emissions-reduction potential of EVs compared to internal combustion vehicles. 

EVs reduce emissions the most in areas with a cleaner electricity mix, denser traffic, higher annual travel distances, and a mild climate, in decreasing order of importance. In each area, emission reductions increase for drivers who drive more often, drive larger vehicles, and are more frequently stuck in traffic. 

In a colder area like North Dakota, fuel economy of battery-electric vehicles might be reduced by as much as 50 percent on a particularly frigid night, but the effect on annual emission benefits is minimal. 

“We even did a sensitivity study to see if the range is reduced in very cold climates, and we found that, even in the most unfavorable conditions, EVs still reduce emissions by a substantial amount,” Miotti says.

On the cost side, the models show that, in most places across the U.S., EVs are competitive with comparable combustion-engine vehicles in terms of lifetime ownership cost, even without clean vehicle tax credits. And in areas where electricity is relatively affordable, battery-electric vehicles tend to cost less than their plug-in hybrid or combustion-engine counterparts.

In the future, the researchers want to expand this analysis to include a temporal dimension, so the framework also considers how changes in vehicle, fuel, and electricity prices affect emissions and costs over time. 

“While we found that the electricity mix is a big driver of the spatial variation in emissions savings of EVs, the electricity grid is decarbonizing everywhere. As that happens, emissions savings across space will become more homogenous for EVs, but the differences across one driver to another will remain,” Miotti says.

They could also use the framework to explore regions outside the United States or incorporate data on hybrid-electric vehicles that cannot be plugged in.

This work was funded, in part, by the MIT Martin Family Society of Fellows for Sustainability.

A crepe cake.

That’s how Camille Cunin describes the polymer-metal “sandwiches” that became a highlight of her doctoral thesis at MIT’s Department of Materials Science and Engineering (DMSE). Over close to five years, these composites were a key component of her research on bioelectronics — devices designed to interface with the human body.

Cunin completed her PhD in February — she’ll attend commencement in May — but traces her interest in bioelectronics to a formative summer internship at Massachusetts General Hospital (MGH) in Boston in 2019. There, she saw a patient with Parkinson’s disease struggle to swallow a tethered “capsule” intended to function as an exploratory gut probe. The device failed, and the gap between lab-based design and real life became all too apparent.

The incident validated the career path Cunin had already begun to pursue: to make usable products that have a positive impact on people’s lives. It’s a purpose that hasn’t gone unnoticed. “Some might be happy with a sketch of a concept and no actual demonstration, but Camille has a remarkable ability in that she wants to do materials science that can translate to real-world applications,” says her advisor, Aristide Gumyusenge.

Building blocks

The daughter of a psychologist and an engineer, Cunin grew up in Paris, encouraged by her parents to be curious about the world around her. LEGO blocks featured prominently in her childhood. When her father found some old lights in a box in the attic, 9-year-old Camille strung them to decorate her LEGO castle by creating a circuit, complete with a fuse.

Strong grades earned her a spot in France’s elite post-secondary preparatory classes for admission to the country’s prestigious grandes écoles. The intensive and competitive prep classes, however, left Cunin with a sour aftertaste — “for a while I hated science, because the environment was too competitive for me,” she says — and a bit rudderless in engineering school.

It was the research internship thousands of miles from home, at MGH — part of her master’s in engineering at École Centrale de Marseille in France — that rebooted her love of science. The open-ended nature of research appealed to her curiosity and helped her regain confidence in solving problems. She was delighted to be accepted at MIT DMSE for her doctoral studies. “In Boston, I thrived in collaborative environments, and it felt like anything was possible,” she says.

Stretching possibilities

Before starting at MIT, Cunin had a wealth of interdisciplinary experience, from internships and her graduate studies. Unsure about how to slot it all together, she was looking for an advisor at a time when Gumyusenge, Henry L. Doherty Career Development Professor in Ocean Utilization and assistant professor of materials science and engineering, was himself just establishing his lab at DMSE.

When Gumyusenge shared plans to work on projects to turn biological signals into electronic data, Cunin was excited to build on her prior research in biomedical devices. “Here was a chance to fine-tune the materials and to optimize the performance of bioelectronic devices. I really felt I could leverage my strengths in Aristide’s lab,” she remembers.

Gumyusenge proved a great fit, supporting Cunin’s broad research ambitions while helping her shape and integrate them into a coherent doctoral project. She tackled everything from developing and characterizing new materials to fabricating transistors and learning surgery to test the devices in animal models. The final dissertation focused on organic transistors, which boost body signals for easier detection in soft electronics.

Biological signals, like those from nerves in the body, are weak, and transistors amplify them so they can be measured. The challenge with developing bioelectronic devices is that traditional components are hard and rigid, while the human body is not. Devices must perform as needed and be soft and flexible to avoid irritating human tissue.

Another complication: Biological processes involve charged ions moving through fluids, while electronics rely on electrons moving through materials. Before transistors can amplify signals, they first have to convert biological signals into electronic ones for circuits to pick up.

Cunin’s transistor design needed to solve two major challenges: first, to facilitate the movement of electrons and ions in the “channel,” the hub of all signal activity, in soft, hydrated environments; and second, to be pliable enough to conform to the human body.

It was no easy task.

Elegant simplicity

Gumyusenge’s lab typically uses chemistry to modify material behavior, but Cunin took a different tack. Since polymers are soft, and metals are good conductors, she looked to the classic French pastry mille-feuille, which inspired the layered design: thin metal sheets sandwiched between layers of porous elastomer. The metal stretches with the elastomer and forms microcracks. Charges get trapped in the cracks but can still flow through the stack, while the elastomer’s strong adhesion keeps the layers together.

Her approach won Cunin high marks from her advisor. “Camille was working on a complex problem, but she found a way to simplify it with a straightforward approach,” Gumyusenge says.

Of course, even an elegant solution needs test drives. “The more crystalline the polymers are, the better the charges percolate and travel in the material,” Cunin points out, referring to how ordered the semiconducting polymers in the transistor channel are. But if they’re packed too tightly, ions don’t move freely, and the transistor channel can’t switch properly. The arrangement of the spaghetti-like polymer chains controls this balance, so Cunin studied the composites’ structure to optimize both ionic and electronic performance.

Professor Polina Anikeeva, who co-advised Cunin with Gumyusenge and calls her “unstoppable,” says her innovation in the lab was remarkable — but not surprising.

“She didn’t have to be pushed into trying something new,” says Anikeeva, head of DMSE. “I would have higher and higher expectations, and she would consistently meet those higher and higher expectations.”

That drive continues in industry. Cunin now works at the Cambridge-based neurotechnology startup Axoft — just minutes from her former lab at MIT — researching soft electrodes that can be implanted in the brain. The electrodes detect electrical signals that can shed light on the brain’s many functions. “By understanding the brain better, we can eventually develop therapies and treatments that improve patient outcomes,” Cunin says.

Creative outlets

During her time at MIT, Cunin also made time for activities outside the lab, driven by the same curiosity that fueled her research. Committed to sharing her love of materials science and engineering, she was a leading member of the Polymer Graduate Student Association and organized several editions of MIT Polymer Day, a one-day symposium connecting students, faculty, and industry to showcase cutting-edge polymer research.

She also pursued creative outlets. After learning to use 3D graphics software Blender, Cunin illustrated some of the journal covers featuring her work.

She is also a diehard salsa fan and teaches the dance style a couple of times a week. Salsa’s social and collaborative forms appeal to Cunin, who enjoys sharing her passion, experimenting with choreography, and helping fellow dancers improve. “Salsa is fast — I love the mental challenge it brings. I also like that it exposes you to different aspects of the community; it pushes you out of your bubble,” she says.

Gumyusenge appreciates that Cunin made time for other pursuits throughout the grueling demands of a doctoral degree. “She’d work 14 hours a day in the lab, but also go do some hiking and take a break. I love that — it’s something that other PhD students seem to forget sometimes,” he says.

That balance reflects her determination and resolve. “Camille has never been shy about facing challenging research problems,” he says. “She had a research vision and was dedicated to learning the lessons she needed to get it all done. I learned to not get in her way because when Camille told you she would learn how to do something, she would.”

In late October 2025, Tropical Storm Melissa moved through the Caribbean Sea with moderate winds that didn’t get much attention. But on Oct. 25, aided by a patch of warm ocean, the storm rapidly intensified. By the time it made landfall in Jamaica, it was one of the strongest Atlantic hurricanes on record, uprooting trees, tearing the roofs from buildings, and causing catastrophic flooding and power outages.

Ravi Pappu SM ’95, PhD ’01 blames the surprise on our inability to gather high-quality ocean data.

“The storm intensified because of a small pool of hot water in the Caribbean Ocean that fed it energy,” Pappu explains. “These pools are everywhere. They can be hundreds of kilometers wide and are literally invisible to us. If we knew about that pool, we could say very precisely how the hurricane would intensify and better deal with it.”

Pappu thinks he has a way to solve that problem. He is the founder of Apeiron Labs, a company deploying low-cost autonomous ocean sensors to capture more data, in more places, and at a lower cost than is possible today. The company’s devices roam the ocean up to a quarter mile below the surface and continuously gather data on temperature, acoustics, salinity, and more, providing a real-time look at one of the planet’s last known mysteries. He says the sensors can do for the ocean what small, modular CubeSat satellites did for Earth observation from space.

When the devices are ready to be recharged, trackers make it easy to scoop them from the ocean surface. Pappu envisions the recovery process being done by autonomous boats in the future.

“Humanity needs ocean measurements, and we need them at a scale that has never been attempted before,” Pappu says. “It’s a massively hard problem. In the last century, oceanographers resigned themselves to calling it the century of undersampling. If we are successful, we will have a much more fine-grained understanding of our oceans and how they impact humans. That’s what drives us.”

Homework

Pappu came to MIT after completing a 10-year homework assignment. It started when he was a child in India in the 1980s, when he saw a hologram on the cover of National Geographic for the first time.

“I was so taken by it that I decided I needed to learn how to make those three-dimensional images,” Pappu recalls. “I learned what I could by reading books and papers. I didn’t know who invented the hologram until I read a book about MIT’s Media Lab. The book named the person who invented the rainbow hologram, so I wrote him a letter. I didn’t know his address, so I just wrote on the envelope, ‘Steve Benton, holography researcher, MIT, USA.’”

To Pappu’s surprise, the letter reached Benton, and the former Media Lab professor even wrote back with some further topics he needed to learn about.

Pappu never forgot that. He earned a bachelor’s degree in electrical engineering in India, then earned his master’s degree at Villanova University, taking all the optics classes he could.

“Eventually, about 10 years after I saw my first hologram, I wrote to Steve and I said, ‘I did all these things you asked me, now I want to study with you,’” Pappu says. “That’s how I got into MIT.”

Pappu studied under Benton for the next three years. He also studied under Professor Neil Gershenfeld as part of his PhD. Following graduation, Pappu and four classmates started ThingMagic, a consulting company that eventually sold RFID readers. ThingMagic was acquired 2010. Pappu returned to MIT for two years as a visiting scientist around the time of the acquisition.

Following that experience, Pappu worked at In-Q-Tel, an organization that invested in ThingMagic and other companies with potential to advance national security. It was there that Pappu realized how badly the world needed large-scale, inexpensive ocean sensing.

“All of the ocean sensing up to that point, and even today, was about making a really expensive thing that cost $20 million, goes to the bottom of the ocean, and stays there for five years,” Pappu says. “We needed things that are cheap and scalable to deploy wherever you need them for as long as you want.”

Pappu officially founded Apeiron Labs in 2022.

“What we’re focused on is figuring out how the ocean works,” Pappu says. “How warm is it? What is the pH? How salty is it? These things vary from place to place every 10 kilometers or so. It varies over time, and it varies by season. If we knew the details of the ocean with the same fidelity we have for the atmosphere, we would be able to tell exactly when and where hurricanes hit. It would mean less uncertainty.”

Apeiron’s ocean-sensing devices are each 3 feet long and about 20 pounds. They’re designed to be dropped off a boat or plane with biodegradable parachutes and stay in the ocean for six months. Each device continuously sends data to the cloud, is controllable through a cloud-based ocean operating system, and is accessible on a mobile phone.

“We lower the carbon footprint and cost of gathering ocean data because everything else needs a diesel ship — and a fully crewed ship costs $100,000 a day,” Rappu says. “By the time you collect the first data in the old model, you’ve already committed to a lot of money in addition to millions of dollars for the sensors. “

The company’s devices currently have two types of sensors: one for measuring salinity, temperature, and depth, and the other that uses a hydrophone to passively listen for things like submarines and whales.

That could be used to detect the low-frequency calls and clicks of endangered whales and other fish species. Currently, fishermen must look for whales manually with spotters on ships or planes. The data could also be used to improve weather forecasts, monitor noise from offshore energy projects, and track currents.

“Currents are determined by temperature and salinity, so if there’s an oil spill, our data could help determine where that spill is going,” Pappu says. “Or if you’re a fisherman, knowing where the water changes from warm to cold, which is where the fish hang out, is very useful.”

An ocean of possibilities

Apeiron Labs has worked with government defense agencies including the U.S. Navy over the last two years. The company has also tested its devices off the coast of California and in the Boston Harbor.

“The most important thing is, when we show people our approach and what we’ve demonstrated so far, they are no longer asking, ‘Can it be done?’ they’re asking, ‘What can we do with it?’” Pappu says. “Our customers have spent decades working in the ocean and they understand how novel these capabilities are.”

Of all the possibilities, improved storm forecasting could be the one Pappu is most excited about.

“Our mission is to lower the barriers to ocean data,” Pappu says. “The ocean is a huge determinant of weather, climate, and short-term forecasting. Despite our best efforts to predict the intensity of storms, sudden changes are still the norm, and much of that comes down to a lack of understanding of our oceans. If we were monitoring these things over long periods of time and finer spatial scales, we could see these storms coming much earlier with more certainty.”

Jack Carson, a second-year undergraduate at MIT majoring in electrical engineering and computer science, has been named a 2026 Udall Scholar, one of up to 65 undergraduates nationally to receive the prestigious $7,500 award. 

The Udall Scholarship honors students who have demonstrated a commitment to the environment, Indigenous health care, or tribal public policy. Carson is only the third MIT student to win this award, and the first to win for tribal policy.

Carson, a member of the Cherokee Nation and resident of Oklahoma, exemplifies the multidisciplinary approach to problem-solving that the Udall Scholarship seeks to honor. His work spans artificial intelligence, biomedical research, Indigenous community development, and ethics.

"Jack is the type of leader the Udall Foundation exists to support," says Kim Benard, associate dean for distinguished fellowships. "He's not only conducting cutting-edge research, but he's actively creating opportunities for Indigenous students to enter tech fields."

At MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Carson works in the Barzilay Lab, developing multiomics models for personalized therapeutic target identification. His work on deep learning and statistical physics has resulted in a sole-author paper published at the International Conference on Machine Learning (ICML).

Carson founded Code.Tulsa, a summer technology program designed to introduce Indigenous high school students to computer science and tech careers. The initiative addresses a significant gap: Indigenous communities remain highly underrepresented in technology fields, despite the potential for tech to advance tribal sovereignty and economic development.

This year, Carson won the Elie Wiesel Prize in Ethics Essay Contest. He is an accomplished musician who has performed at Carnegie Hall and with the National Opera, a motorcycle racer, and a self-described philosopher deeply committed to questions of justice and responsibility.

Each year, faculty and researchers across the MIT School of Engineering are recognized with prestigious awards for their contributions to research, technology, society, and education. To celebrate these achievements, the school periodically highlights select honors received by members of its departments, institutes, labs, and centers. The following individuals were recognized in winter 2026:

Arup K. Chakraborty, the John M. Deutch (1961) Institute Professor in the departments of Chemical Engineering, Chemistry, and Physics, and the founding director of the Institute for Medical Engineering and Science, as well as James J. Collins, the Termeer Professor of Medical Engineering and Science in the Department of Biological Engineering and IMES, received the 2026 Laureate of the Tel Aviv University International Prize in Biophysics. The prize recognizes outstanding scientists whose work has significantly advanced the understanding of biological systems through physical principles.

Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor in the Department of Electrical Engineering and Computer Science, received the 2025 IEEE Journal of Solid-State Circuits Test of Time Award. The award recognizes an outstanding paper published in the IEEE Journal of Solid-State Circuits at least 10 years prior that has had significant impact on its field.

Charles Harvey, a professor in the Department of Civil and Environmental Engineering; Piotr Indyk, the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science; John Henry Lienhard, the Abdul Latif Jameel Professor of Water and Mechanical Engineering in the Department of Mechanical Engineering; Frances Ross, the TDK Professor in Materials Science and Engineering; Zoltán Sandor Spakovszky, the T. Wilson (1953) Professor in Aeronautics; and Ram Sasisekharan, the Alfred H. Caspary Professor of Biological Physics and Physics in the Department of Biological Engineering; were elected to the National Academy of Engineering for 2026. One of the highest professional distinctions for engineers, membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education,” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education.”

Michael Howland, the Jeffrey Cheah Career Development Professor and assistant professor in the Department of Civil and Environmental Engineering, received a 2026 Faculty Early Career Development (CAREER) Award from the National Science Foundation. The award supports early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

Yoon Kim, associate professor in the Department of Electrical Engineering and Computer Science; Anand Natarajan, an associate professor in the Department of Electrical Engineering and Computer Science; and Mengjia Yan, ITT Career Development Professor in Computer Technology and associate professor in the Department of Electrical Engineering and Computer Science, were named 2026 Sloan Research Fellows. Sloan Research Fellowships support fundamental research conducted by early-career scientists, and they are awarded annually to early-career researchers whose creativity, innovation, and research accomplishments make them stand out as the next generation of leaders.

Carlos Portela, the Robert N. Noyce Career Development Professor and associate professor in the Department of Mechanical Engineering, has received a 2026 Young Investigator Award from the Office of Naval Research. The Young Investigator Program seeks to identify and support academic scientists and engineers who are in their first or second full-time tenure-track or tenure-track-equivalent academic appointment, who have received their doctorate or equivalent degree in the past seven years, and who show exceptional promise for doing creative research.

Ellen Roche, the Abby Rockefeller Mauzé Professor and associate department head for research in the Department of Mechanical Engineering and a professor in the Institute for Medical Engineering and Science, received the 2026 Sony Women in Technology Award with Nature. The award recognizes exceptional early- to mid-career women researchers in technology who through their research are driving a positive impact on society and the planet.

Tess Smidt, an associate professor in the Department of EECS, was named co–principal investigator on a National Science Foundation (NSF) AI Research Institute award and also received a 2025 Department of Energy Office of Science Early Career Research Program Award. The NSF AI Materials Institute (AI-MI) aims to propel foundational AI research past the limitations of existing AI algorithms by pursuing materials discovery and conquering knowledge- and data-centric challenges. The DoE Early Career Research Program provides five-year awards to exceptional early career researchers at U.S. academic institutions, DoE National Laboratories, and Office of Science User Facilities to stimulate new research directions in mission critical areas supported by DoE’s Office of Science.

Antonio Torralba, the Delta Electronics Professor and faculty head of AI+D in the Department of EECS, was elected to the 2025 cohort of Association for Computing Machinery Fellows. ACM Fellows, the highest honor bestowed by the professional organization, are registered members of the society selected by their peers for outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community.

Harry L. Tuller, a professor in the Department of Materials Science and Engineering, received The Senior Scientist Award from the International Society for Solid State Ionics. The Senior Scientist Award, the most prestigious award of the International Society for Solid State Ionics, is presented to a senior solid-state ionics researcher who has made outstanding contributions to the science and engineering of solid-state ionics.

Vinod Vaikuntanathan, the Ford Foundation Professor of Engineering in the Department of Electrical Engineering and Computer Science, was named a 2026 fellow of the International Association for Cryptologic Research. ACR has established the IACR Fellows Program to recognize outstanding IACR members for technical and professional contributions.

Over 200 students, alumni, faculty, staff, funders, and community collaborators gathered at the MIT Media Lab on April 15 for the 25th annual IDEAS Social Innovation Incubator Showcase and Awards, hosted by the Priscilla King Gray (PKG) Center for Social Impact. 

Since its founding in 2001, the PKG Center’s IDEAS Incubator has launched hundreds of social ventures in over 60 countries, guiding MIT’s technical talent toward urgent social challenges — from energy and climate to health care, education, and economic development. 

“Global and local challenges are increasingly complex and interconnected,” said Lauren Tyger, assistant dean for social innovation at the PKG Center and director of IDEAS. “IDEAS educates technical founders in systems thinking and community-based innovation, helping students develop business models that achieve both measurable social outcomes and financial sustainability.” 

IDEAS alumni celebrated

The event celebrated the many successful social ventures launched by IDEAS alumni with a 25-Year Impact Report and a keynote speech from IDEAS alumnus Bill Thies ’01, ’02, MNG ’02, PhD ’09. 

Thies traced his tuberculosis medication adherence work in India from a low-cost electronic pillbox through multiple iterations that helped shift India’s treatment policies toward patient autonomy. Ultimately, his work led to Nikshay, a national electronic medical records platform now supporting 150 million people, which recently transitioned to full government control. 

“Innovations can open doors for much more important changes than the innovations themselves,” Thies said. Limitations to technical interventions surface important questions, such as “what policies do we want to change, to become more supportive and human-centered? And how can technology be a bridge to that new world we would envision?”

Thinking back on the influence of IDEAS on his own path, Theis reflected: “I always assumed that in IDEAS we were incubating projects. But what I’ve come to realize is that it’s actually the other way around: the projects are incubating us. We are the ones who will ultimately drive the change we hope to see in the world.”

Vision for scaling social entrepreneurship at MIT and catalytic gift announced  

Thies’ message was echoed by Chancellor for Academic Advancement Eric Grimson, who explained how IDEAS aligns with MIT’s strategic initiatives, including MIT’s Generative AI Impact Consortium (MGAIC), Health and Life Sciences Collaborative (MIT HEALS), and the Climate Project, as well as President Sally Kornbluth’s and Provost Anantha Chandrakasan’s recent call to accelerate entrepreneurship. “Many of the current presidential initiatives naturally include an opportunity for social entrepreneurship,” said Grimson, who applauded IDEAS alumni pursuing ventures in climate, health, and AI-powered social enterprises. 

The PKG Center’s director, Alison Badgett, shared the center’s vision for the future of IDEAS. “As MIT’s only student entrepreneurship program focused solely on social impact,” said Badgett, “we recognize the need to both scale social entrepreneurship programming at MIT and to better position our student founders for scale after graduating.” 

Badgett announced a first-in gift of $150,000 from the Morgridge Family Foundation to help realize the center’s vision. The foundation’s gift will enable the PKG Center to develop a robust social impact investor ecosystem at MIT, connecting student- and alumni-led ventures with potential funders and helping more aspiring entrepreneurs see social impact as a viable path. 

This year’s award-winning social ventures

This year’s top $20,000 award winner was Beyond Words, an assistive application for iPhone and Apple Watch that gives nonverbal individuals a layer of support by passively capturing biometrics, audio, and location, and communicating it to caregivers. 

Other award winners were:

• AyuConnect ($10,000) uses WhatsApp-native, voice-first electronic health records to enhance care access while reducing clinician burnout in India.
• PEAR ($7,500) offers a hands-on STEM research program for Nigerian and other African students, equipping them with technical skills to solve community problems.
• CommonGround ($5,000) connects Bostonians to tailored and hyper-local climate actions through an online platform, replacing eco-anxiety with collective resilience.
• Sehat Screen ($5,000) is an AI-powered cervical cancer screening device for women in Afghanistan and other resource-constrained countries.
• Breakthrough Health ($2,500) is a care coordination platform that links hepatitis C patients in recovery centers to health care.
• Sero ($2,500) is a voice-first AI tool that helps rural borrowers in Nepal understand loan contracts and access fair credit in their own language, with no dependency on literacy.

During the event, Shane Kosinski, executive director of the Office of the Vice President for Energy and Climate, announced inaugural Climate Student Innovators awards, funded by the MIT Climate Project. Four IDEAS teams received this award, which will be presented annually.

“The MIT Climate Project is an all-of-MIT initiative with the ambitious goal to make a measurable difference on climate change within a decade. We reach this global impact not by top down mandates, but by testing good ideas where they are needed most and supporting them to succeed,” explained Kosinski. “This vision is also hardwired into the character, history and purpose of PKG IDEAS.”

This year’s IDEAS teams awarded by the MIT Climate Project were:

• Q’ochas Resilientes ($15,000) co-designs climate-resilient water technology in the Peruvian Andes to uplift ancestral knowledge and support agricultural livelihoods.
• NECTICA ($15,000) tackles urban flooding in Lagos by empowering women-led cooperatives with a low-tech sorter bin to separate and monetize composite waste.
• MittiNav ($15,000) designs production and supply-chain systems to scale biochar technology that restores soil and stores carbon.
• Resilient Grid ($5,000) collects and processes food waste through anaerobic digestion on skid platforms to produce biogas for electricity and heat in Caribbean island nations.

“The Climate Project is thrilled to present the first-ever Climate Student Innovators Awards to these teams,” said Vice President for Energy and Climate Evelyn Wang. “We applaud this year’s IDEAS winners for developing systemic interventions in partnership with affected communities.” 

Several additional teams received $1,000 awards: 

• ​​1for1Health is a fertility platform offering education, testing, and insights to expand access and reduce disparities in reproductive health decisions.
• Ceed CRM brings cutting-edge AI to mission-driven organizations that have been stuck with tools built for sales teams, not social impact.
• CerviSeal created a medical device that reduces pain, tissue trauma, and risk during cervical manipulation for women undergoing hysteroscopy.
• FoodLoop connects farms and restaurants through matchmaking, demand forecasting, and forward contracts to strengthen local food systems.
• Homeroom Hero is an AI tool for teachers that instantly grades short-form assessments, reducing workload and improving student learning without putting tech in front of kids.
• Gees Health is a noninvasive, at-home hormone monitor that helps women with polycystic ovary syndrome track and manage their health with continuous insights.
• Illume makes discreet wearables that are a safe way for recovering victims of human trafficking to contact trusted people, building their support network.
• Longevia is an AI-powered platform that translates complex medical data into personalized, actionable insights for chronic kidney disease patients.
• Opta is an AI talent refinery upskilling Brazil's low-socioeconomic status students for small and medium business jobs, driving economic mobility.
• Recover Hospitality scales recovery-informed wellness coaching for hospitality workers through AI-powered motivational interviewing and benefits navigation.

The event closed with Tyger thanking the vast network of alumni, mentors, funders, and campus partners who make IDEAS possible, and the 104 volunteers who supported this year’s incubator challenge. “IDEAS builds more than social enterprises — we’re building the infrastructure and community needed for alumni and their ventures to achieve long-lasting impact. Our vision is a future where MIT entrepreneurship is not only groundbreaking, but fundamentally grounded in social impact.” 

In the new review article, “Categorization is Baked into the Brain,” cognitive scientists Earl K. Miller, Picower Professor of Neuroscience at MIT, and Lisa Feldman Barrett, university distinguished professor at Northeastern University, contend that categorization is part of a predictive process the brain uses to efficiently meet the body’s needs in a fast-paced, otherwise overwhelming sensory world. In that sense, their paper in Nature Reviews Neuroscience challenges decades of dogma about how and why the brain boils down what it sees, hears, smells, tastes, and feels.

Categories are groups of things that are similar enough to be considered functionally equivalent. When you walk through a neighborhood, you’ll naturally experience the furry, four-legged, barking animal ahead of you as a “dog.” In the classic view of cognition, your brain arrives at that categorization by soaking in lots of basic sensory features of the hound — its shape, its size, the sounds it makes, its behavior — and compares that to some prototype “dog” stored in your memory. Hundreds of milliseconds after the first sensory inputs, you can then decide what you might want to do about the dog.

Barrett and Miller argue that that’s wrong. Instead, they propose that your brain comes prepared for sensory patterns with predictions of the motor action plans that are most likely to achieve the needs and goals you bring to the moment. Those prediction signals can be described as a momentary category that the brain constructs to shape the processing of sensory signals. 

From the very start, incoming sensory signals are compressed and abstracted into that category to efficiently select the best predicted plan. If you are in an unfamiliar neighborhood your brain might construct the category “dog” to avoid being bit, resulting in: “Back away slowly while saying nice doggie.” If you are on your own block and encounter a familiar dog, your brain might construct a category to kneel and open up your arms to summon your neighbor’s adorable pup for a satisfying petting.

In either case, the category “dog” arises in the context of your needs and your prediction from a menu of learned action plans for similar situations, not from an intellectual exercise of neutrally regarding sensory inputs, comparing them to a fixed prototype, and then planning from there. If the brain really worked the classically believed way, you’d be on the back foot when the unfamiliar dog lunged at you.

“One of the main things your brain has to do is predict the world,” says Miller, a faculty member of The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “It takes several hundred milliseconds to process things, and meanwhile the world is moving on. Your brain has to anticipate things.”

The most pragmatic and efficient way to survive and thrive in such a world, Barrett says, is to have your needs and potential plans ready for the sensory situation. If your predictions are right, you’re prepared in time. If they are wrong, you adjust and learn from it.

“The stimulus, cognition, response model of the brain is wrong,” says Barrett, a faculty member in Northeastern’s Department of Psychology and co-director of the Interdisciplinary Affective Science Laboratory. “The brain prepares for a response and then perceives a stimulus. A brain is not reactive. It’s predictive. Action planning comes first. Perception comes second, as a function of the action plan.”

Anatomical and functional evidence

Throughout the review, Barrett and Miller ground the provocative proposal in copious anatomical, electrophysiological, and imaging evidence about the brain. They cite numerous experimental results that show how the brain is structured to broadcast memories to create motor plans that flow back toward signals that arrive from the body’s sensory surfaces, actively whittling them down and shaping them to give them meaning.

“The capacity to create similarities from differences — to abstract — is embedded in the architecture of the nervous system, and you can see that by looking at what is connected to what and by observing signal flow,” Barrett says.

For example, as circuits feed signals “forward” from sensory surfaces (such as the retina) to regions of the cerebral cortex that are focused on sensory processing (such as the visual cortex) toward the areas that are important for executive control (the prefrontal cortex) and control of the body (limbic cortex), information passes from many small, barely connected neurons to fewer, bigger, and more well-connected neurons. Such an architecture compresses sensory details into increasingly abstract representations that group many different features into smaller groups of similar features, and in doing so helps to select a predicted action plan from the broader category that’s already there.

“Your brain is a big funnel to take the outside world and turn it into an output,” Miller says.

Moreover, anatomical evidence shows that the neurons in the cortex maintain many more connections to provide feedback from memory that control sensory regions than to feed sensory information forward. As much as 90 percent of synapses in the visual cortex are “feedback” instead of “feedforward,” Barrett and Miller wrote. In other words, the brain is built to use memory to filter incoming sensory signals, consistent with imposing needs and goals on what would otherwise be a deluge of sights, sounds, and other sensations.

Yet another line of evidence are numerous studies from Miller’s own lab showing that at the broad network level of information flow in the cortex, the brain uses beta frequency waves that carry information about goals and plans, to constrain the expression of gamma frequency waves that carry information about specific sensory inputs.

Finally, the dominance of “feedback” over “feedforward” signals in the cortical architecture allows for the possibility that sensory signals are made meaningful in terms of predicted plans. When these plans are wrong, the resulting surprise can be integrated for future use.

“In science, there is a special name for that: learning,” Barrett says.

Implications for human thought and disease

In the end, Barrett and Miller’s proposal completely changes the idea of categorization, shifting it from being a particular intellectual skill to being a fundamental function for predictively meeting the body’s needs (or, “allostasis”).

“A category may not be a representation that an animal has, but a signal processing event than an animal does, predictively, to constrain the meaning of a high-dimensional ensemble of signals in a particular situation,” the authors wrote. “Categorization renders these signals meaningful — similar to one another and to past allostatic events — in terms of some goal or function.”

Humans, Barrett says, have a relatively massive amount of the neural network architecture to perform these pragmatic abstractions, and therefore can make categorizations that seem outright metaphorical (e.g., a functional similarity between “climbing the career ladder” and climbing a literal physical ladder).

But these processes can also go awry in disease, Barrett and Miller note. Depression can be seen as a disorder in which the brain imposes overly broad categories, such as “threat” or “criticism” on sensory episodes that don’t have to be perceived that way. By contrast, autism can manifest with features of inadequate compression of incoming sensory signals, not generalizing enough to recognize when a situation is similar enough to a prior one to select the appropriate plan.

Funding to support the paper came from the National Institutes of Health, The U.S. Army Research Institute for the Behavioral and Social Sciences, the Office of Naval Research, the Unlikely Collaborators Foundation, The Freedom Together Foundation, and The Picower Institute for Learning and Memory.

Lidar systems use pulses of infrared light to measure distance and map a 3D scene with high resolution, allowing autonomous vehicles to rapidly react to obstacles that appear in their path. But traditional lidar sensors are expensive, bulky systems with many moving parts that degrade over time, limiting how the sensors can be deployed.

A new study from MIT researchers could help to enable next-generation lidar sensors that are compact, durable, and have no moving parts. The key advance is a novel design for a silicon-photonics chip, which is a semiconductor device that manipulates light rather than electricity. 

Typically, such silicon-photonics chip-based systems have a restricted field of view, so a silicon-photonics-based lidar would not be able to scan angles in the periphery. Existing workarounds to this problem increase noise and hamper precision.

To avoid these drawbacks, the MIT researchers designed and demonstrated an array of integrated antennas that minimizes unwanted crosstalk between the antennas. Their innovation allows a lidar chip to scan a wider field of view while maintaining low-noise operation compared to other silicon-photonics-based approaches.

This novel demonstration could fuel the development of advanced lidar sensors for demanding applications like autonomous vehicle navigation, aerial surveying, and construction site monitoring.

“The functionality we demonstrated in this work solves a fundamental problem for integrated optical-phased-array technology, enabling future lidar sensors that can achieve significantly higher performance than we could demonstrate previously,” says Jelena Notaros, the Robert J. Shillman Career Development Associate Professor of Electrical Engineering and Computer Science (EECS) at MIT, a member of the Research Laboratory of Electronics, and senior author of a paper on this innovation.

She is joined on the paper by lead author and EECS graduate student Henry Crawford-Eng as well as EECS graduate students Andres Garcia Coleto, Benjamin M. Mazur, Daniel M. DeSantis, and Tal Sneh. The research appears today in Nature Communications.

Adjusting an antenna array

Many traditional lidar systems map a scene using a bulky box that spins to send pulses of light in multiple directions. The light bounces off nearby objects and returns to the sensor, providing data that are used to reconstruct the environment. 

Instead, silicon-photonics-based lidar sensors systematically scan an emitted light beam in multiple directions non-mechanically using a system called an integrated optical phased array (OPA).

Key to an OPA is an array of integrated antennas that have tiny perturbations placed periodically along their length. These corrugations allow the antenna to scatter light from an input source up and out of the photonic chip.

By adjusting the phase of light routed to each antenna, the researchers can change the angle at which the light is emitted out of the array. In this way, they can steer the beam with no moving parts.

But if engineers place the antennas too close together, the antennas will couple with each other and the light they emit will get jumbled. To avoid this, scientists typically space the antennas farther apart, but this also has downsides.

If the antennas are spaced too far apart, the array will emit multiple copies of the light beam at different angles. The researchers can only steer the primary beam so far in either direction until it is undiscernible from its neighboring copies.

“This limits our field of view, so the autonomous vehicle now only knows what is in front of it for a certain angular range,” Garcia Coleto explains.

These beam copies, known as grating lobes, can cause false positives by confusing the sensor. They also waste power.

The MIT researchers solved this problem by designing a set of reduced-crosstalk antennas that can be placed close together without causing a significant coupling effect.

In a standard OPA, all the antennas have the same design, meaning the same arrangement of corrugations. These identical antennas couple very strongly when placed close together.

To address this fundamental roadblock, the MIT researchers designed a set of three antennas with different geometries, varying the width of each antenna and the size and arrangement of corrugations. With varied geometries, each antenna has a different propagation coefficient, which determines how light travels down the antenna.

“Because the antennas have very different propagation coefficients, when we put them close together, essentially each antenna doesn’t ‘see’ the antenna next to it. Therefore, it won’t couple with its neighbor,” Garcia Coleto says. 

A photonic balancing act

But even though the antennas have different propagation coefficients, the researchers still need them to emit light in the same way. 

They achieved this by carefully designing the antennas to meet three parameters. 

First, each antenna must emit the same amount of light. Second, each antenna must emit a beam at the same angle for the same wavelength of light. Third, the emission angle must change uniformly across the array as the researchers steer it.

“We have this challenge where we require the antennas to have different geometries to reduce the crosstalk, but we need to simultaneously design the antennas to have the same emission characteristics. While it is possible to engineer this, it is extremely difficult because, typically, when antennas are designed with different geometries, they tend to behave differently,” Crawford-Eng says.

The researchers first developed the fundamental electromagnetic theory behind how radiative modes couple. They used that theory as a guide to design and simulate their antennas.

Building on those analyses, they fabricated the OPA with reduced-crosstalk antennas spaced significantly closer than they would be in a traditional OPA, then experimentally tested the system.

While a typical OPA would have coupling of about 100 percent in this experiment, their OPA reduced coupling to about 1 percent while generating a single, precise beam. Using this design, they demonstrated accurate beam steering across a wide field of view without any grating lobes. 

In the future, the researchers plan to further improve their technique to enable an even wider field of view. In addition, they are exploring a new potential solution to wide field-of-view functionality that they discovered while developing the underlying theory.

“This work addresses a longstanding challenge in integrated optical phased arrays: simultaneously achieving both a wide field of view, which requires dense antenna spacing, and high beam quality, which requires low crosstalk between neighboring antennas. The authors solve this problem with an elegant antenna design. Their innovation is an important step forward for chip-scale, solid-state beam-steering technology,” says Joyce Poon, professor of electrical and computer engineering at the University of Toronto and director of the Max Planck Institute of Microstructure Physics, who was not involved with this work.

This research was supported, in part, by the Semiconductor Research Corporation, the National Science Foundation, an MIT MathWorks Fellowship, the U.S. Department of War, and the MIT Rolf G. Locher Endowed Fellowship.