Optimizing kidney donation and other markets without money

When people die, they can become organ donors for a period of about 24 to 48 hours. But 20 percent of kidneys in the U.S. that could be transplanted in these situations are never used.

Meanwhile, by some estimates, 30 to 50 percent of living people who are willing to donate a kidney never find a recipient. With around 100,000 Americans waiting for kidney transplants at any given time, those are suboptimal situations.  

What can be done to help fix this? Give the problem to a market design scholar, such as MIT economist Nikhil Agarwal, who has studied the issue in close detail.

From within the walls of MIT’s Building E52, where economics equations litter the whiteboards, Agarwal’s work has now leapt out to the medical establishment. In the last year, a new method he and some colleagues formulated for a more efficient kidney-donation system has been approved for implementation by the Alliance for Paired Donation, the second-largest platform for such transplants in the U.S.

“It’s particularly exciting,” says Agarwal, who is low-key about his accomplishments but allows that he is thrilled to see his work having a tangible effect. Currently there are about 800 kidney transplants in the U.S. annually; by Agarwal’s estimation, a more efficient exchange market could increase that number by 30 to 60 percent.

Though Agarwal’s work is still being implemented, and it is not yet easy to quantify its impact yet, it is simple enough to see his rising trajectory in academia. For his research and teaching, Agarwal was granted tenure at MIT earlier this year.

“That’s not how a lot of markets work”

At first glance, transplants might not seem to be a problem for an economist. But a growing cadre of economists have made notable progress understanding markets that match pairs of things — transplant donors and recipients, applicants and schools — and do not use money to settle matters.

“In economics,” Agarwal says, “we often [assume] there’s the demand, the supply, the price, and the market clears, somehow. It just happens.” And yet, he says, “That’s not how a lot of markets work. There are all these different important markets where we do not allow prices.”

Scholars in the field of “market design,” therefore, closely examine these nonfinancial markets, observing how their rules and procedures affect outcomes. Agarwal calls himself a specialist in “resource allocation systems that do not use prices.” These include kidney donations: The law forbids selling vital organs. Many education systems and entry-level labor markets, for example, also fit into this category. 

In Agarwal’s case, he has a specialty within his specialty. Some market-design scholars are theorists. Agarwal is an empiricist who locates data on nonpriced markets, evaluates their efficiency, and works out improvements.

“Data can teach you new things you maybe wouldn’t have otherwise thought,” Agarwal says.

In a series of papers examining the inefficiencies of kidney transplant systems in the U.S., Agarwal and a variety of co-authors looked at the numbers and came back with solutions. One major source of inefficiency, Agarwal has discovered, is a lack of scale. Bigger networks of hospitals could better match donors and recipients. Right now, 62 percent of kidney donor-and-recipient pairings consist of patients at the same hospitals; that number would be lower in a more efficient system.

One reason for this: Donors and recipients must have matching blood types. People with type O blood can donate kidneys across blood types, but they can only receive kidneys from other type O people. Due to the timing of when people enter kidney markets, a bigger network is more efficient in this regard. In single-hospital networks, 22.8 percent of type O donors give a kidney to a non-type O recipient (for whom other donors might be found), while in the biggest U.S. kidney network, just 6.5 percent do, meaning its type O participants are connecting more optimally.

Agarwal’s research also suggests that hospitals tend to be very concerned about the financial and administrative costs they incur while handling the transplant process — although such costs are small compared to the overall social value of transplants. Well-crafted subsidies and mandates, as he has detailed, can help address this particular problem.

Open questions in need of answers

Agarwal was an economics and math double major at Brandeis University, where he received his BA in 2008. Directly out of college, Agarwal was accepted into Harvard University’s PhD program in economics, but, as he recounts it, he did not have a clear idea of what he wanted to study. Before long, though, Agarwal connected at Harvard with Alvin Roth, an innovative market-design theorist who would soon be awarded the Nobel Prize, in 2012; Roth’s work helped create new mechanisms for school-choice programs.

Working with Roth, as well as Harvard professors Susan Athey (now of Stanford University) and Ariel Pakes, and MIT Professor Parag Pathak, Agarwal began focusing on market-design problems and developing his taste for empiricism. The theorists had broken the field of market design open; as a result, unanswered questions about the activity in many markets had been identified but not necessarily answered.

“I’ve always liked combining different ways of learning about something,” Agarwal says. “Initially I was training as a theorist, but then I got interested in data, because I just saw a big set of open questions there, which wasn’t informed by numbers.” Pakes, who Agarwal cites as a major influence, “showed me what data, especially when combined with theory, can teach us.”

Agarwal joined the MIT faculty in 2014 and began publishing papers on a range of topics, on a variety of markets. He has studied online advertising and school-choice systems; one of his first prominent papers, in the American Economic Review in 2015, examined the system used to allocate medical students to residencies.

Still, the majority of Agarwal’s work has been on kidney transpants specifically, a field of knowledge he has gradually built up.

“You need to have domain expertise,” Agarwal says. “It’s very important to have that. Otherwise [theories] may not be directly implementable. For that reason, people really do specialize, so they understand the setting.” One of Agarwal’s co-authors is a kidney transplant surgeon.

“I’ve learned a lot from other people,” Agarwal notes.

He has also benefitted, as he tells it, from his home in the MIT Department of Economics, where all kinds of work is valued — even work on nonpriced markets, which, as Agarwal quips, can seem like “kind of a weird thing to study,” at least to outsiders.

“The economics department is an intellectually amazing place to think about things,” Agarwal adds. “People value good work on the merits and they’re open-minded.”

Now Agarwal is also encouraging others to research markets of all kinds: His students are studying topics as diverse as electricity markets, the palm oil industry in Indonesia, and water markets in Australia, among many others. Every such market, he notes, can differ from others, in its practices and in the behavior of its participants.

“We have to think a little more carefully about how markets work and demand meets supply, and what are all the implications of that,” Agarwal says.

After all, as Agarwal has already seen, a little more careful thought about markets could have a lot more real-world impact.

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Chemists observe “spooky” quantum tunneling

A molecule of ammonia, NH₃, typically exists as an umbrella shape, with three hydrogen atoms fanned out in a nonplanar arrangement around a central nitrogen atom. This umbrella structure is very stable and would normally be expected to require a large amount of energy to be inverted.

However, a quantum mechanical phenomenon called tunneling allows ammonia and other molecules to simultaneously inhabit geometric structures that are separated by a prohibitively high energy barrier. A team of chemists that includes Robert Field, the Robert T. Haslam and Bradley Dewey Professor of Chemistry at MIT, has examined this phenomenon by using a very large electric field to suppress the simultaneous occupation of ammonia molecules in the normal and inverted states.

“It’s a beautiful example of the tunneling phenomenon, and it reveals a wonderful strangeness of quantum mechanics,” says Field, who is one of the senior authors of the study.

Heon Kang, a professor of chemistry at Seoul National University, is also a senior author of the study, which appears this week in the Proceedings of the National Academy of Sciences. Youngwook Park and Hani Kang of Seoul National University are also authors of the paper.

Suppressing inversion

The experiments, performed at Seoul National University, were enabled by the researchers’ new method for applying a very large electric field (up to 200,000,000 volts per meter) to a sample sandwiched between two electrodes. This assembly is only a few hundred nanometers thick, and the electric field applied to it generates forces nearly as strong as the interactions between adjacent molecules.

“We can apply these huge fields, which are almost the same magnitude as the fields that two molecules experience when they approach each other,” Field says. “That means we’re using an external means to operate on an equal playing field with what the molecules can do themselves.”

This allowed the researchers to explore quantum tunneling, a phenomenon often used in undergraduate chemistry courses to demonstrate one of the “spookinesses” of quantum mechanics, Field says.

As an analogy, imagine you are hiking in a valley. To reach the next valley, you need to climb a large mountain, which requires a lot of work. Now, imagine that you could tunnel through the mountain to get to the next valley, with no real effort required. This is what quantum mechanics allows, under certain conditions. In fact, if the two valleys have exactly the same shape, you would be simultaneously located in both valleys.

In the case of ammonia, the first valley is the low-energy, stable umbrella state. For the molecule to reach the other valley — the inverted state, which has exactly the same low-energy — classically it would need to ascend into a very high-energy state. However, quantum mechanically, the isolated molecule exists with equal probability in both valleys.

Under quantum mechanics, the possible states of a molecule, such as ammonia, are described in terms of a characteristic energy level pattern.  The molecule initially exists in either the normal or inverted structure, but it can tunnel spontaneously to the other structure. The amount of time required for that tunneling to occur is encoded in the energy level pattern. If the barrier between the two structures is high, the tunneling time is long. Under certain circumstances, such as application of a strong electric field, tunneling between the regular and inverted structures can be suppressed.

For ammonia, exposure to a strong electric field lowers the energy of one structure and raises the energy of the other (inverted) structure. As a result, all of the ammonia molecules can be found in the lower energy state. The researchers demonstrated this by creating a layered argon-ammonia-argon structure at 10 kelvins. Argon is an inert gas which is solid at 10 K, but the ammonia molecules can rotate freely in the argon solid. As the electric field is increased, the energy states of the ammonia molecules change in such a way that the probabilities of finding the molecules in the normal and inverted states become increasingly far apart, and tunneling can no longer occur.

This effect is completely reversible and nondestructive: As the electric field is decreased, the ammonia molecules return to their normal state of being simultaneously in both wells.

“This manuscript describes a burgeoning frontier in our ability to tame molecules and control their underlying dynamics,” says Patrick Vaccaro, a professor of chemistry at Yale University who was not involved in the study. “The experimental approach set forth in this paper is unique, and it has enormous ramifications for future efforts to interrogate molecular structure and dynamics, with the present application affording fundamental insights into the nature of tunneling-mediated phenomena.”

Lowering the barriers

For many molecules, the barrier to tunneling is so high that tunneling would never happen during the lifespan of the universe, Field says. However, there are molecules other than ammonia that can be induced to tunnel by careful tuning of the applied electric field. His colleagues are now working on exploiting this approach with some of those molecules.

“Ammonia is special because of its high symmetry and the fact that it’s probably the first example anybody would ever discuss from a chemical point of view of tunneling,” Field says. “However, there are many examples where this could be exploited. The electric field, because it’s so large, is capable of acting on the same scale as the actual chemical interactions,” offering a powerful way of externally manipulating molecular dynamics.

The research was funded by the Samsung Science and Technology Foundation and the National Science Foundation.

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Autonomous system improves environmental sampling at sea

An autonomous robotic system invented by researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) efficiently sniffs out the most scientifically interesting — but hard-to-find — sampling spots in vast, unexplored waters.

Environmental scientists are often interested in gathering samples at the most interesting locations, or “maxima,” in an environment. One example could be a source of leaking chemicals, where the concentration is the highest and mostly unspoiled by external factors. But a maximum can be any quantifiable value that researchers want to measure, such as water depth or parts of coral reef most exposed to air.

Efforts to deploy maximum-seeking robots suffer from efficiency and accuracy issues. Commonly, robots will move back and forth like lawnmowers to cover an area, which is time-consuming and collects many uninteresting samples. Some robots sense and follow high-concentration trails to their leak source. But they can be misled. For example, chemicals can get trapped and accumulate in crevices far from a source. Robots may identify those high-concentration spots as the source yet be nowhere close.

In a paper being presented at the International Conference on Intelligent Robots and Systems (IROS), the researchers describe “PLUMES,” a system that enables autonomous mobile robots to zero in on a maximum far faster and more efficiently. PLUMES leverages probabilistic techniques to predict which paths are likely to lead to the maximum, while navigating obstacles, shifting currents, and other variables. As it collects samples, it weighs what it’s learned to determine whether to continue down a promising path or search the unknown — which may harbor more valuable samples.

Importantly, PLUMES reaches its destination without ever getting trapped in those tricky high-concentration spots. “That’s important, because it’s easy to think you’ve found gold, but really you’ve found fool’s gold,” says co-first author Victoria Preston, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and in the MIT-WHOI Joint Program.

The researchers built a PLUMES-powered robotic boat that successfully detected the most exposed coral head in the Bellairs Fringing Reef in Barbados — meaning, it was located in the shallowest spot — which is useful for studying how sun exposure impacts coral organisms. In 100 simulated trials in diverse underwater environments, a virtual PLUMES robot also consistently collected seven to eight times more samples of maxima than traditional coverage methods in allotted time frames.

“PLUMES does the minimal amount of exploration necessary to find the maximum and then concentrates quickly on collecting valuable samples there,” says co-first author Genevieve Flaspohler, a PhD student and in CSAIL and the MIT-WHOI Joint Program.

Joining Preston and Flaspohler on the paper are: Anna P.M. Michel and Yogesh Girdhar, both scientists in the Department of Applied Ocean Physics and Engineering at the WHOI; and Nicholas Roy, a professor in CSAIL and in the Department of Aeronautics and Astronautics.  

Navigating an exploit-explore tradeoff

A key insight of PLUMES was using techniques from probability to reason about navigating the notoriously complex tradeoff between exploiting what’s learned about the environment and exploring unknown areas that may be more valuable.

“The major challenge in maximum-seeking is allowing the robot to balance exploiting information from places it already knows to have high concentrations and exploring places it doesn’t know much about,” Flaspohler says. “If the robot explores too much, it won’t collect enough valuable samples at the maximum. If it doesn’t explore enough, it may miss the maximum entirely.”

Dropped into a new environment, a PLUMES-powered robot uses a probabilistic statistical model called a Gaussian process to make predictions about environmental variables, such as chemical concentrations, and estimate sensing uncertainties. PLUMES then generates a distribution of possible paths the robot can take, and uses the estimated values and uncertainties to rank each path by how well it allows the robot to explore and exploit.

At first, PLUMES will choose paths that randomly explore the environment. Each sample, however, provides new information about the targeted values in the surrounding environment — such as spots with highest concentrations of chemicals or shallowest depths. The Gaussian process model exploits that data to narrow down possible paths the robot can follow from its given position to sample from locations with even higher value. PLUMES uses a novel objective function — commonly used in machine-learning to maximize a reward — to make the call of whether the robot should exploit past knowledge or explore the new area.

“Hallucinating” paths

The decision where to collect the next sample relies on the system’s ability to “hallucinate” all possible future action from its current location. To do so, it leverages a modified version of Monte Carlo Tree Search (MCTS), a path-planning technique popularized for powering artificial-intelligence systems that master complex games, such as Go and Chess.

MCTS uses a decision tree — a map of connected nodes and lines — to simulate a path, or sequence of moves, needed to reach a final winning action. But in games, the space for possible paths is finite. In unknown environments, with real-time changing dynamics, the space is effectively infinite, making planning extremely difficult. The researchers designed “continuous-observation MCTS,” which leverages the Gaussian process and the novel objective function to search over this unwieldy space of possible real paths.

The root of this MCTS decision tree starts with a “belief” node, which is the next immediate step the robot can take. This node contains the entire history of the robot’s actions and observations up until that point. Then, the system expands the tree from the root into new lines and nodes, looking over several steps of future actions that lead to explored and unexplored areas.

Then, the system simulates what would happen if it took a sample from each of those newly generated nodes, based on some patterns it has learned from previous observations. Depending on the value of the final simulated node, the entire path receives a reward score, with higher values equaling more promising actions. Reward scores from all paths are rolled back to the root node. The robot selects the highest-scoring path, takes a step, and collects a real sample. Then, it uses the real data to update its Gaussian process model and repeats the “hallucination” process.

“As long as the system continues to hallucinate that there may be a higher value in unseen parts of the world, it must keep exploring,” Flaspohler says. “When it finally converges on a spot it estimates to be the maximum, because it can’t hallucinate a higher value along the path, it then stops exploring.”

Now, the researchers are collaborating with scientists at WHOI to use PLUMES-powered robots to localize chemical plumes at volcanic sites and study methane releases in melting coastal estuaries in the Arctic. Scientists are interested in the source of chemical gases released into the atmosphere, but these test sites can span hundreds of square miles.

“They can [use PLUMES to] spend less time exploring that huge area and really concentrate on collecting scientifically valuable samples,” Preston says.

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Ten Lincoln Laboratory technologies earn 2019 R&D 100 Awards

Among the winners of the 2019 R&D 100 Awards are 10 technologies developed by researchers at MIT Lincoln Laboratory, either exclusively or in collaboration with other organizations. R&D World magazine, and its parent company WTWH Media, announced on Oct. 29 the 100 winners named the year’s most influential innovations.

"These 100 winning products and technologies are the disruptors that will change industries and make the world a better place in the coming years," says Paul Heney, vice president and editorial director for R&D World.

A 40-member judging committee composed of WTWH editors and leading technologists from academia, industry, and national laboratories selected the winners from hundreds of nominations sent in by companies and R&D institutions from around the world.

The following 10 inventions bring to 58 the number of R&D 100 Awards presented to Lincoln Laboratory since 2010.

New capabilities for communications

The Aperture Level Simultaneous Transmit and Receive Phased Array represents the first-ever demonstration of a phased-array antenna system with sufficient isolation to enable practical multi-beam full-duplex communication. This technology allows multiple devices to share a single wireless channel while maintaining high data rates over long ranges. The problem of self-interference caused by transmitting and receiving on the same frequency is solved with a combination of adaptive digital beamforming to reduce coupling between transmit and receive antenna beams and adaptive digital cancellation to further remove residual noise. In this manner, the system effectively mitigates self-interference, which is particularly challenging for phased-array systems because of the close proximity of the multiple antennas in the array.

The Dual-Mode Imaging Receiver integrates the previously disparate functions of high-frame-rate photon-counting imaging and single-photon-sensitive communications into a single optical receiver, enabling the user to simultaneously get a wide-field-of-view image of the source of the transmission while receiving data from one or more sources. This low-power, compact system requires only a single receive aperture to support multiple concurrent optical communication links from spatially separated users within the field of view, thereby reducing or eliminating the need for precision beam pointing. The receiver is enabled by a custom chip that provides automatic, concurrent, on-chip detection, tracking, and demodulation of the multiple communication signals.

Devices to advance biomedical research

The small, low-cost ArtGut (for artificial gut) device is the first in vitro platform to enable researchers to perform high-resolution, physiologically relevant gut microbiome studies. ArtGut emulates the physiochemical microenvironment of the human gut by mimicking the precise oxygen gradients and mucus substrates necessary to grow and maintain the gut's polymicrobial communities, and as such provides a solution to the lack of adequate testing models for studying the human gut microbiome.

The Mobility and Biomechanics Insert for Load Evaluation (MoBILE) is a biomechanics laboratory built into a shoe insert and small ankle package (see video). The insert and ankle package contain a variety of high-end sensors that measure a user’s weight and lower leg movements. MoBILE informs users when their gait significantly changes, when their biomechanics measurements are above acceptable thresholds, and when they are at risk for lower-leg injury. MoBILE helps users track the amount of weight they are carrying over time, select the best placement for carrying heavy equipment, and determine optimal rest cycles and training routines.

Tools for improving decision making

The Gas Mapping LiDAR sensitively images methane gas plumes, identifies the source locations of gas leaks, and quantifies the leak rates so that the owners and operators of oil and natural gas infrastructure can determine and prioritize repairs before even visiting the site. Designed and built by Bridger Photonics and enabled by Lincoln Laboratory's high-power slab-coupled optical waveguide amplifier technology, the Gas Mapping LiDAR provides aerial photography and 3D lidar data overlaid with sensitive gas-concentration maps in simple, user-friendly formats so that the oil and gas operators can make quantitative cost-benefit analyses and efficiently schedule leak repair. Because the system is capable of at least 300 times more efficient detection of leaks than foot-patrol monitoring, it can help effectively reduce greenhouse gas emissions.

The Visibility Estimation through Image Analytics (VEIA) software system, developed by the laboratory in partnership with the Federal Aviation Administration, provides air traffic managers and pilots an inexpensive, yet effective, way to automatically extract data about meteorological visibility from cameras. By using edge detection that is sensitive to changes in the visual scene, the system's algorithm compares the overall edge strength of the current image to edges of an image of a clear day to estimate the visibility in miles. With the proliferation of web-based camera imagery for monitoring conditions near airports and other remote locations, VEIA can significantly expand the quantity of visibility observations available to the aviation community, especially in areas that are not covered by traditional sensor systems and where low visibility can have dire consequences. 

Technology for enhancing efficiency

The Tactical Microgrid Standard (TMS) Open Architecture provides an interoperability standard for highly modular, resilient, scalable, and mission-specific microgrid solutions. This architecture offers a user-friendly family of interoperable devices useful for both military and commercial microgrid applications. The TMS architecture was developed by a U.S. Department of Defense-led consortium of government, industry, and academic partners, with Lincoln Laboratory in a lead role. This consortium was tasked to solve challenges faced by military personnel at remote bases, where reliable power is a critical foundation for successful operations.

The Lightweight Deployable Array Panels for Space reduce the cost of launching space-based communications and remote sensing systems by minimizing the panels' weight and size, which translates to lower rocket fuel costs and the capacity to deploy more systems per launch. After a system equipped with the laboratory's panels reaches its designated altitude, the antenna panels deploy to create the desired radiating aperture size. The panels use Lincoln Laboratory’s patented weight-reduction technique for stacked patch antenna arrays along with an innovative packing system. This design minimizes the weight and maximizes the stowed volume efficiency without substantially affecting the radio frequency performance.

The awards will be presented to the recipients at the R&D Conference being held Dec. 4-5 in San Mateo, just outside San Francisco.

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Student gets a kick out of French internship

Like many European countries, France takes its soccer — or football — very seriously. To further refine the game of top players, a French startup called Footbar created a smart device to measure players’ stats such as speed, stamina, and skill. The artificial intelligence used by the Meteor tracker determines the quality of passes, shots, dribbles, and tackles based only on the acceleration of the tracker worn on the player’s calf.

However, the AI was unable to tell whether some athletes “cheated” by wearing the tracker on their ankles, which provided better statistics than when wearing it on their calves. To solve the problem, they sought out an intern at MIT.  

When math and computer science junior Benton Wilson applied and was accepted for an internship through MIT-France, part of the MIT International Science and Technology Initiatives (MISTI), he had only a few expectations: to do something data science-related, to practice his high school French, and “to gain some perspective for my worldview.” 

He joined four French interns at Footbar, located within Paris’ Le Tremplin, an incubator for sports-related companies. At first, he was "un peu nerveux" about using his French skills in a professional setting, but his high school soccer skills came in handy when he tried out the quarter-sized device himself.

His first task was to create algorithms that would determine where a player was wearing the tracker.

Players can upload their stats to their computer, view their progress, and compare their results online against teammates, as well as Meteor-wearing clients all over the world. “At the end of a game, you can get your stats for the game, and then you can also make a profile and see how you change over time,” explains Wilson.

But results are skewed depending on where players wear the tracker. Footbar decided that Wilson needed to develop an algorithm that would “penalize” the ankle-wearing players.

“It is a pretty complicated task for someone discovering the data,” says Wilson’s supervisor at Footbar, data scientist Sébastien Benoit. “His first weeks were probably a bit complicated for him as he both had to get more familiar with both our stack (understand how our code works) and with the type of data we work with (time-series data from an accelerator).”

Over the summer, Wilson used his background in machine learning and Python, and picked up skills in GitHub, the database system Django, and signal processing, to work on the algorithm. Not only did Wilson solve the problem, he discovered some technical solutions that further impressed his supervisor.

“After showing him a few examples, Benton was able to build a model and train it on enough data to make it work well,” says Benoit. He says that Wilson’s model was 95 percent accurate, and is now being used by Footbar’s production department. “We can now successfully detect the smart guys who intentionally exploited this flaw. Thank you, Benton!”

Benoit was impressed enough to let Wilson work independently, which led to him spending his final two weeks of his internship solving a second problem that had vexed Footbar: to automatically detect which of four fitness tests was taken by an athlete: sprint; sprint down and back; running endurance test; and vertical jump test. “Some of it was difficult, such as detecting when jumps tests occur versus other kicks/jumps, but overall I just worked on trying different things,” Wilson says.    

“This task [was] probably twice as difficult as the previous one, but Benton completed this quite well and pretty quickly,” Benoit says. 

Wilson added that he appreciated working within a tight-knit community that began each day with standup meetings, ate lunch together, and gathered for soccer, cross-fit training, and jogs.

In his off-hours, Wilson shared an apartment with a fellow MIT-France intern who was researching environmental sustainability for another company. Wilson joined a local gym, shopped in local markets, watched old movies at the Latin Quarter’s Le Champo theater, attended the China vs. South Africa Women’s World Cup match, ventured to different parts of France, traveled to Barcelona and the Netherlands, and people-watched along the Seine River. 

“My favorite areas were over near the Canal de St. Martin and La Villette, where there are a ton of restaurants and places to sit along the canals,” he recalls.

He was one of 45 MIT students who participated in MISTI’s MIT-France this past summer. Its internship program, founded in 2001 with a collaboration between the French Ministry of Foreign Affairs and MIT, provides opportunities for research and experience in French companies and labs.

While the logistics of studying abroad can be daunting for many students, Wilson and his peers received a lot of help. MISTI programs cover basic needs, including airfare, housing, and food; Wilson and several others were also co-sponsored by the MIT European Club. Additionally, students receive support with their visas and other paperwork. MIT-France provides a comprehensive 377-page student guidebook to living in France. Student internships are between three to six months; Wilson stayed for three.

“MISTI has provided me with a unique opportunity to immerse myself in a totally new environment,” says Wilson, who adds that his experience gave him the confidence to consider an international career after graduation.

The deadline for next summer's internships is Dec. 1 for priority applicants. To apply, please visit mitfrance.com. 

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Better autonomous “reasoning” at tricky intersections

MIT and Toyota researchers have designed a new model to help autonomous vehicles determine when it’s safe to merge into traffic at intersections with obstructed views.

Navigating intersections can be dangerous for driverless cars and humans alike. In 2016, roughly 23 percent of fatal and 32 percent of nonfatal U.S. traffic accidents occurred at intersections, according to a 2018 Department of Transportation study. Automated systems that help driverless cars and human drivers steer through intersections can require direct visibility of the objects they must avoid. When their line of sight is blocked by nearby buildings or other obstructions, these systems can fail.

The researchers developed a model that instead uses its own uncertainty to estimate the risk of potential collisions or other traffic disruptions at such intersections. It weighs several critical factors, including all nearby visual obstructions, sensor noise and errors, the speed of other cars, and even the attentiveness of other drivers. Based on the measured risk, the system may advise the car to stop, pull into traffic, or nudge forward to gather more data.

“When you approach an intersection there is potential danger for collision. Cameras and other sensors require line of sight. If there are occlusions, they don’t have enough visibility to assess whether it’s likely that something is coming,” says Daniela Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. “In this work, we use a predictive-control model that’s more robust to uncertainty, to help vehicles safely navigate these challenging road situations.”

The researchers tested the system in more than 100 trials of remote-controlled cars turning left at a busy, obstructed intersection in a mock city, with other cars constantly driving through the cross street. Experiments involved fully autonomous cars and cars driven by humans but assisted by the system. In all cases, the system successfully helped the cars avoid collision from 70 to 100 percent of the time, depending on various factors. Other similar models implemented in the same remote-control cars sometimes couldn’t complete a single trial run without a collision.

Joining Rus on the paper are: first author Stephen G. McGill, Guy Rosman, and Luke Fletcher of the Toyota Research Institute (TRI); graduate students Teddy Ort and Brandon Araki, researcher Alyssa Pierson, and postdoc Igor Gilitschenski, all of CSAIL; Sertac Karaman, an MIT associate professor of aeronautics and astronautics; and John J. Leonard, the Samuel C. Collins Professor of Mechanical and Ocean Engineering of MIT and a TRI technical advisor.

Modeling road segments

The model is specifically designed for road junctions in which there is no stoplight and a car must yield before maneuvering into traffic at the cross street, such as taking a left turn through multiple lanes or roundabouts. In their work, the researchers split a road into small segments. This helps the model determine if any given segment is occupied to estimate a conditional risk of collision.

Autonomous cars are equipped with sensors that measure the speed of other cars on the road. When a sensor clocks a passing car traveling into a visible segment, the model uses that speed to predict the car’s progression through all other segments. A probabilistic “Bayesian network” also considers uncertainties — such as noisy sensors or unpredictable speed changes — to determine the likelihood that each segment is occupied by a passing car.

Because of nearby occlusions, however, this single measurement may not suffice. Basically, if a sensor can’t ever see a designated road segment, then the model assigns it a high likelihood of being occluded. From where the car is positioned, there’s increased risk of collision if the car just pulls out fast into traffic. This encourages the car to nudge forward to get a better view of all occluded segments. As the car does so, the model lowers its uncertainty and, in turn, risk.

But even if the model does everything correctly, there’s still human error, so the model also estimates the awareness of other drivers. “These days, drivers may be texting or otherwise distracted, so the amount of time it takes to react may be a lot longer,” McGill says. “We model that conditional risk, as well.”

That depends on computing the probability that a driver saw or didn’t see the autonomous car pulling into the intersection. To do so, the model looks at the number of segments a traveling car has passed through before the intersection. The more segments it had occupied before reaching the intersection, the higher the likelihood it has spotted the autonomous car and the lower the risk of collision.

The model sums all risk estimates from traffic speed, occlusions, noisy sensors, and driver awareness. It also considers how long it will take the autonomous car to steer a preplanned path through the intersection, as well as all safe stopping spots for crossing traffic. This produces a total risk estimate.

That risk estimate gets updated continuously for wherever the car is located at the intersection. In the presence of multiple occlusions, for instance, it’ll nudge forward, little by little, to reduce uncertainty. When the risk estimate is low enough, the model tells the car to drive through the intersection without stopping. Lingering in the middle of the intersection for too long, the researchers found, also increases risk of a collision.

Assistance and intervention

Running the model on remote-control cars in real-time indicates that it’s efficient and fast enough to deploy into full-scale autonomous test cars in the near future, the researchers say. (Many other models are too computationally heavy to run on those cars.) The model still needs far more rigorous testing before being used for real-world implementation in production vehicles.

The model would serve as a supplemental risk metric that an autonomous vehicle system can use to better reason about driving through intersections safely. The model could also potentially be implemented in certain “advanced driver-assistive systems” (ADAS), where humans maintain shared control of the vehicle.

Next, the researchers aim to include other challenging risk factors in the model, such as the presence of pedestrians in and around the road junction.

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School of Science appoints 14 faculty members to named professorships

The School of Science has announced that 14 of its faculty members have been appointed to named professorships. The faculty members selected for these positions receive additional support to pursue their research and develop their careers.

Riccardo Comin is an assistant professor in the Department of Physics. He has been named a Class of 1947 Career Development Professor. This three-year professorship is granted in recognition of the recipient's outstanding work in both research and teaching. Comin is interested in condensed matter physics. He uses experimental methods to synthesize new materials, as well as analysis through spectroscopy and scattering to investigate solid state physics. Specifically, the Comin lab attempts to discover and characterize electronic phases of quantum materials. Recently, his lab, in collaboration with colleagues, discovered that weaving a conductive material into a particular pattern known as the “kagome” pattern can result in quantum behavior when electricity is passed through.

Joseph Davis, assistant professor in the Department of Biology, has been named a Whitehead Career Development Professor. He looks at how cells build and deconstruct complex molecular machinery. The work of his lab group relies on biochemistry, biophysics, and structural approaches that include spectrometry and microscopy. A current project investigates the formation of the ribosome, an essential component in all cells. His work has implications for metabolic engineering, drug delivery, and materials science.

Lawrence Guth is now the Claude E. Shannon (1940) Professor of Mathematics. Guth explores harmonic analysis and combinatorics, and he is also interested in metric geometry and identifying connections between geometric inequalities and topology. The subject of metric geometry revolves around being able to estimate measurements, including length, area, volume and distance, and combinatorial geometry is essentially the estimation of the intersection of patters in simple shapes, including lines and circles.

Michael Halassa, an assistant professor in the Department of Brain and Cognitive Sciences, will hold the three-year Class of 1958 Career Development Professorship. His area of interest is brain circuitry. By investigating the networks and connections in the brain, he hopes to understand how they operate — and identify any ways in which they might deviate from normal operations, causing neurological and psychiatric disorders. Several publications from his lab discuss improvements in the treatment of the deleterious symptoms of autism spectrum disorder and schizophrenia, and his latest news provides insights on how the brain filters out distractions, particularly noise. Halassa is an associate investigator at the McGovern Institute for Brain Research and an affiliate member of the Picower Institute for Learning and Memory.

Sebastian Lourido, an assistant professor and the new Latham Family Career Development Professor in the Department of Biology for the next three years, works on treatments for infectious disease by learning about parasitic vulnerabilities. Focusing on human pathogens, Lourido and his lab are interested in what allows parasites to be so widespread and deadly, looking on a molecular level. This includes exploring how calcium regulates eukaryotic cells, which, in turn, affect processes such as muscle contraction and membrane repair, in addition to kinase responses.

Brent Minchew is named a Cecil and Ida Green Career Development Professor for a three-year term. Minchew, a faculty member in the Department of Earth, Atmospheric and Planetary Sciences, studies glaciers using remote sensing methods, such as interferometric synthetic aperture radar. His research into glaciers, including their mechanics, rheology, and interactions with their surrounding environment, extends as far as observing their responses to climate change. His group recently determined that Antarctica, in a worst-case scenario climate projection, would not contribute as much as predicted to rising sea level.

Elly Nedivi, a professor in the departments of Brain and Cognitive Sciences and Biology, has been named the inaugural William R. (1964) And Linda R. Young Professor. She works on brain plasticity, defined as the brain’s ability to adapt with experience, by identifying genes that play a role in plasticity and their neuronal and synaptic functions. In one of her lab’s recent publications, they suggest that variants of a particular gene may undermine expression or production of a protein, increasing the risk of bipolar disorder. In addition, she collaborates with others at MIT to develop new microscopy tools that allow better analysis of brain connectivity. Nedivi is also a member of the Picower Institute for Learning and Memory.

Andrei Negut has been named a Class of 1947 Career Development Professor for a three-year term. Negut, a member of the Department of Mathematics, fixates on problems in geometric representation theory. This topic requires investigation within algebraic geometry and representation theory simultaneously, with implications for mathematical physics, symplectic geometry, combinatorics and probability theory.

Matĕj Peč, the Victor P. Starr Career Development Professor in the Department of Earth, Atmospheric and Planetary Science until 2021, studies how the movement of the Earth’s tectonic plates affects rocks, mechanically and microstructurally. To investigate such a large-scale topic, he utilizes high-pressure, high-temperature experiments in a lab to simulate the driving forces associated with plate motion, and compares results with natural observations and theoretical modeling. His lab has identified a particular boundary beneath the Earth’s crust where rock properties shift from brittle, like peanut brittle, to viscous, like honey, and determined how that layer accommodates building strain between the two. In his investigations, he also considers the effect on melt generation miles underground.

Kerstin Perez has been named the three-year Class of 1948 Career Development Professor in the Department of Physics. Her research interest is dark matter. She uses novel analytical tools, such as those affixed on a balloon-borne instrument that can carry out processes similar to that of a particle collider (like the Large Hadron Collider) to detect new particle interactions in space with the help of cosmic rays. In another research project, Perez uses a satellite telescope array on Earth to search for X-ray signatures of mysterious particles. Her work requires heavy involvement with collaborative observatories, instruments, and telescopes. Perez is affiliated with the Kavli Institute for Astrophysics and Space Research.

Bjorn Poonen, named a Distinguished Professor of Science in the Department of Mathematics, studies number theory and algebraic geometry. He, his colleagues, and his lab members generate algorithms that can solve polynomial equations with the particular requirement that the solutions be rational numbers. These types of problems can be useful in encoding data. He also helps to determine what is undeterminable, that is exploring the limits of computing.

Daniel Suess, named a Class of 1948 Career Development Professor in the Department of Chemistry, uses molecular chemistry to explain global biogeochemical cycles. In the fields of inorganic and biological chemistry, Suess and his lab look into understanding complex and challenging reactions and clustering of particular chemical elements and their catalysts. Most notably, these reactions include those that are essential to solar fuels. Suess’s efforts to investigate both biological and synthetic systems have broad aims of both improving human health and decreasing environmental impacts.

Alison Wendlandt is the new holder of the five-year Cecil and Ida Green Career Development Professorship. In the Department of Chemistry, the Wendlandt research group focuses on physical organic chemistry and organic and organometallic synthesis to develop reaction catalysts. Her team fixates on designing new catalysts, identifying processes to which these catalysts can be applied, and determining principles that can expand preexisting reactions. Her team’s efforts delve into the fields of synthetic organic chemistry, reaction kinetics, and mechanics.

Julien de Wit, a Department of Earth, Atmospheric and Planetary Sciences assistant professor, has been named a Class of 1954 Career Development Professor. He combines math and science to answer questions about big-picture planetary questions. Using data science, de Wit develops new analytical techniques for mapping exoplanetary atmospheres, studies planet-star interactions of planetary systems, and determines atmospheric and planetary properties of exoplanets from spectroscopic information. He is a member of the scientific team involved in the Search for habitable Planets EClipsing ULtra-cOOl Stars (SPECULOOS) TRANsiting Planets and Planetesimals Small Telescope (TRAPPIST), made up of an international collection of observatories. He is affiliated with the Kavli Institute.

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