Modeling relationships to solve complex problems efficiently

The German philosopher Fredrich Nietzsche once said that “invisible threads are the strongest ties.” One could think of “invisible threads” as tying together related objects, like the homes on a delivery driver’s route, or more nebulous entities, such as transactions in a financial network or users in a social network.

Computer scientist Julian Shun studies these types of multifaceted but often invisible connections using graphs, where objects are represented as points, or vertices, and relationships between them are modeled by line segments, or edges.

Shun, a newly tenured associate professor in the Department of Electrical Engineering and Computer Science, designs graph algorithms that could be used to find the shortest path between homes on the delivery driver’s route or detect fraudulent transactions made by malicious actors in a financial network.

But with the increasing volume of data, such networks have grown to include billions or even trillions of objects and connections. To find efficient solutions, Shun builds high-performance algorithms that leverage parallel computing to rapidly analyze even the most enormous graphs. As parallel programming is notoriously difficult, he also develops user-friendly programming frameworks that make it easier for others to write efficient graph algorithms of their own.

“If you are searching for something in a search engine or social network, you want to get your results very quickly. If you are trying to identify fraudulent financial transactions at a bank, you want to do so in real-time to minimize damages. Parallel algorithms can speed things up by using more computing resources,” explains Shun, who is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Such algorithms are frequently used in online recommendation systems. Search for a product on an e-commerce website and odds are you’ll quickly see a list of related items you could also add to your cart. That list is generated with the help of graph algorithms that leverage parallelism to rapidly find related items across a massive network of users and available products.

Campus connections

As a teenager, Shun’s only experience with computers was a high school class on building websites. More interested in math and the natural sciences than technology, he intended to major in one of those subjects when he enrolled as an undergraduate at the University of California at Berkeley.

But during his first year, a friend recommended he take an introduction to computer science class. While he wasn’t sure what to expect, he decided to sign up.

“I fell in love with programming and designing algorithms. I switched to computer science and never looked back,” he recalls.

That initial computer science course was self-paced, so Shun taught himself most of the material. He enjoyed the logical aspects of developing algorithms and the short feedback loop of computer science problems. Shun could input his solutions into the computer and immediately see whether he was right or wrong. And the errors in the wrong solutions would guide him toward the right answer.

“I’ve always thought that it was fun to build things, and in programming, you are building solutions that do something useful. That appealed to me,” he adds.

After graduation, Shun spent some time in industry but soon realized he wanted to pursue an academic career. At a university, he knew he would have the freedom to study problems that interested him.

Getting into graphs

He enrolled as a graduate student at Carnegie Mellon University, where he focused his research on applied algorithms and parallel computing.

As an undergraduate, Shun had taken theoretical algorithms classes and practical programming courses, but the two worlds didn’t connect. He wanted to conduct research that combined theory and application. Parallel algorithms were the perfect fit.

“In parallel computing, you have to care about practical applications. The goal of parallel computing is to speed things up in real life, so if your algorithms aren’t fast in practice, then they aren’t that useful,” he says.

At Carnegie Mellon, he was introduced to graph datasets, where objects in a network are modeled as vertices connected by edges. He felt drawn to the many applications of these types of datasets, and the challenging problem of developing efficient algorithms to handle them.

After completing a postdoctoral fellowship at Berkeley, Shun sought a faculty position and decided to join MIT. He had been collaborating with several MIT faculty members on parallel computing research, and was excited to join an institute with such a breadth of expertise.

In one of his first projects after joining MIT, Shun joined forces with Department of Electrical Engineering and Computer Science professor and fellow CSAIL member Saman Amarasinghe, an expert on programming languages and compilers, to develop a programming framework for graph processing known as GraphIt. The easy-to-use framework, which generates efficient code from high-level specifications, performed about five times faster than the next best approach.

“That was a very fruitful collaboration. I couldn’t have created a solution that powerful if I had worked by myself,” he says.

Shun also expanded his research focus to include clustering algorithms, which seek to group related datapoints together. He and his students build parallel algorithms and frameworks for quickly solving complex clustering problems, which can be used for applications like anomaly detection and community detection.

Dynamic problems

Recently, he and his collaborators have been focusing on dynamic problems where data in a graph network change over time.

When a dataset has billions or trillions of data points, running an algorithm from scratch to make one small change could be extremely expensive from a computational point of view. He and his students design parallel algorithms that process many updates at the same time, improving efficiency while preserving accuracy.

But these dynamic problems also pose one of the biggest challenges Shun and his team must work to overcome. Because there aren’t many dynamic datasets available for testing algorithms, the team often must generate synthetic data which may not be realistic and could hamper the performance of their algorithms in the real world.

In the end, his goal is to develop dynamic graph algorithms that perform efficiently in practice while also holding up to theoretical guarantees. That ensures they will be applicable across a broad range of settings, he says.

Shun expects dynamic parallel algorithms to have an even greater research focus in the future. As datasets continue to become larger, more complex, and more rapidly changing, researchers will need to build more efficient algorithms to keep up.

He also expects new challenges to come from advancements in computing technology, since researchers will need to design new algorithms to leverage the properties of novel hardware.

“That’s the beauty of research — I get to try and solve problems other people haven’t solved before and contribute something useful to society,” he says.

de MIT News https://ift.tt/0RkrwVZ

Laura Lewis and Jing Kong receive postdoctoral mentoring award

MIT professors Laura Lewis and Jing Kong have been recognized with the MIT Postdoctoral Association’s Award for Excellence in Postdoctoral Mentoring. The award is given annually to faculty or other principal investigators (PIs) whose current and former postdoctoral scholars say they stand out in their efforts to create a supportive work environment for postdocs and support postdocs’ professional development.

This year, the award identified exceptional mentors in two categories. Lewis, the Athinoula A. Martinos Associate Professor in the Institute for Mechanical Engineering and Science and the Department of Electrical Engineering and Computer Science (EECS), was recognized as an early-career mentor. Kong, the Jerry McAfee (1940) Professor In Engineering in the Research Laboratory of Electronics and EECS, was recognized as an established mentor.

“It’s a very diverse kind of mentoring that you need for a postdoc,” said Vipindev Adat Vasudevan, who chaired the Postdoctoral Association committee organizing the award. “Every postdoc has different requirements. Some of the people will be going to industry, some of the people are going for academia… so everyone comes with a different objective.”

Vasudevan presented the award at a luncheon hosted by the Office of the Vice President for Research on Sept. 25 in recognition of National Postdoc Appreciation Week. The annual luncheon, celebrating the postdoctoral community’s contributions to MIT, is attended by hundreds of postdocs and faculty.

“The award recognizes faculty members who go above and beyond to create a professional, supportive, and inclusive environment to foster postdocs’ growth and success,” said Ian Waitz, vice president for research, who spoke at the luncheon. He noted the vital role postdocs play in advancing MIT research, mentoring undergraduate and graduate students, and connecting with colleagues from around the globe, while working toward launching independent research careers of their own. 

“The best part of my job”

Nomination letters for Lewis spoke to her ability to create an inclusive and welcoming lab. In the words of one nominator, “She invests considerable time and effort in cultivating personalized mentoring relationships, ensuring each postdoc in her lab receives guidance and support tailored to their individual goals and circumstances.”

Other nominators commented on Lewis’ ability to facilitate collaborations that furthered postdocs’ research goals. Lewis encouraged them to work with other PIs to build their independence and professional development, and to develop their own research questions, they said. “I was never pushed to work on her projects — rather, she guided me towards finding and developing my own,” wrote one.

Lewis’ lab explores new ways to image the human brain, integrating engineering with neuroscience. Improving neuroimaging techniques can improve our understanding of the brain’s activity when asleep and awake, allowing researchers to understand sleep’s impact on brain health.

“I love working with my postdocs and trainees; it’s honestly the best part of my job,” Lewis says. “It’s important for any individual to be in an environment to help them grow toward what they want to do.”

Recognized as an early-career mentor, Lewis looks forward to seeing her postdocs’ career trajectories over time. Group members returning as collaborators come back with fresh ideas and creative approaches, she says, adding, “I view this mentoring relationship as lifelong.”

“No ego, no bias, just solid facts”

Kong’s nomination also speaks to the lifelong nature of the mentoring relationship. The 13 letters supporting Kong’s nomination came from past and current postdocs. Nearly all touched on Kong’s kindness and the culture of respect she maintains in the lab, alongside high expectations of scientific rigor.

“No ego, no bias, just solid facts and direct evidence,” wrote one nominator: “In discussions, she would ask you many questions that make you think ‘I should have asked that to myself’ or ‘why didn’t I think of this.’”

Kong was also praised for her ability to take the long view on projects and mentor postdocs through temporary challenges. One nominator wrote of a period when the results of a project were less promising than anticipated, saying, “Jing didn't push me to switch my direction; instead, she was always glad to listen and discuss the new results. Because of her encouragement and long-term support, I eventually got very good results on this project.”

Kong’s lab focuses on the chemical synthesis of nanomaterials, such as carbon nanotubes, with the goal of characterizing their structures and identifying applications. Kong says postdocs are instrumental in bringing new ideas into the lab.

“I learn a lot from each one of them. They always have a different perspective, and also, they each have their unique talents. So we learn from each other,” she says. As a mentor, she sees her role as developing postdocs’ individual talents, while encouraging them to collaborate with group members who have different strengths.

The collaborations that Kong facilitates extend beyond the postdocs’ time at MIT. She views the postdoctoral period as a key stage in developing a professional network: “Their networking starts from the first day they join the group. They already in this process establish connections with other group members, and also our collaborators, that will continue on for many years.”

About the award

The Award for Excellence in Postdoctoral Mentoring has been awarded since 2022. With support from Ann Skoczenski, director of Postdoctoral Services in the Office of the VPR, and the Faculty Postdoctoral Advisory Committee, nominations are reviewed on four criteria:

• excellence in fostering and encouraging professional skills development and growth toward independence;
• ability to foster an inclusive work environment where postdoctoral mentees across a diversity of backgrounds and perspectives are empowered to engage in the mentee-mentor relationship;
• ability to support postdoctoral mentees in their pursuit of a chosen career path; and
• a commitment to a continued professional mentoring relationship with mentees, beyond the limit of the postdoctoral term.

The Award for Excellence in Postdoctoral Mentoring provides a celebratory lunch for the recipient’s research group, as well as the opportunity to participate in a mentoring seminar or panel discussion for the postdoctoral community. Last year’s award was given to Jesse Kroll, the Peter de Florez Professor of Civil and Environmental Engineering, professor of chemical engineering, and director of the Ralph M. Parsons Laboratory.

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

MIT engineers create a chip-based tractor beam for biological particles

MIT researchers have developed a miniature, chip-based “tractor beam,” like the one that captures the Millennium Falcon in the film “Star Wars,” that could someday help biologists and clinicians study DNA, classify cells, and investigate the mechanisms of disease.

Small enough to fit in the palm of your hand, the device uses a beam of light emitted by a silicon-photonics chip to manipulate particles millimeters away from the chip surface. The light can penetrate the glass cover slips that protect samples used in biological experiments, enabling cells to remain in a sterile environment.

Traditional optical tweezers, which trap and manipulate particles using light, usually require bulky microscope setups, but chip-based optical tweezers could offer a more compact, mass manufacturable, broadly accessible, and high-throughput solution for optical manipulation in biological experiments.

However, other similar integrated optical tweezers can only capture and manipulate cells that are very close to or directly on the chip surface. This contaminates the chip and can stress the cells, limiting compatibility with standard biological experiments.

Using a system called an integrated optical phased array, the MIT researchers have developed a new modality for integrated optical tweezers that enables trapping and tweezing of cells more than a hundred times further away from the chip surface.

“This work opens up new possibilities for chip-based optical tweezers by enabling trapping and tweezing of cells at much larger distances than previously demonstrated. It’s exciting to think about the different applications that could be enabled by this technology,” says Jelena Notaros, the Robert J. Shillman Career Development Professor in Electrical Engineering and Computer Science (EECS), and a member of the Research Laboratory of Electronics.

Joining Notaros on the paper are lead author and EECS graduate student Tal Sneh; Sabrina Corsetti, an EECS graduate student; Milica Notaros PhD ’23; Kruthika Kikkeri PhD ’24; and Joel Voldman, the William R. Brody Professor of EECS. The research appears today in Nature Communications.

A new trapping modality

Optical traps and tweezers use a focused beam of light to capture and manipulate tiny particles. The forces exerted by the beam will pull microparticles toward the intensely focused light in the center, capturing them. By steering the beam of light, researchers can pull the microparticles along with it, enabling them to manipulate tiny objects using noncontact forces.

However, optical tweezers traditionally require a large microscope setup in a lab, as well as multiple devices to form and control light, which limits where and how they can be utilized.

“With silicon photonics, we can take this large, typically lab-scale system and integrate it onto a chip. This presents a great solution for biologists, since it provides them with optical trapping and tweezing functionality without the overhead of a complicated bulk-optical setup,” Notaros says.

But so far, chip-based optical tweezers have only been capable of emitting light very close to the chip surface, so these prior devices could only capture particles a few microns off the chip surface. Biological specimens are typically held in sterile environments using glass cover slips that are about 150 microns thick, so the only way to manipulate them with such a chip is to take the cells out and place them on its surface.

However, that leads to chip contamination. Every time a new experiment is done, the chip has to be thrown away and the cells need to be put onto a new chip.

To overcome these challenges, the MIT researchers developed a silicon photonics chip that emits a beam of light that focuses about 5 millimeters above its surface. This way, they can capture and manipulate biological particles that remain inside a sterile cover slip, protecting both the chip and particles from contamination.

Manipulating light

The researchers accomplish this using a system called an integrated optical phased array. This technology involves a series of microscale antennas fabricated on a chip using semiconductor manufacturing processes. By electronically controlling the optical signal emitted by each antenna, researchers can shape and steer the beam of light emitted by the chip.

Motivated by long-range applications like lidar, most prior integrated optical phased arrays weren’t designed to generate the tightly focused beams needed for optical tweezing. The MIT team discovered that, by creating specific phase patterns for each antenna, they could form an intensely focused beam of light, which can be used for optical trapping and tweezing millimeters from the chip’s surface.

“No one had created silicon-photonics-based optical tweezers capable of trapping microparticles over a millimeter-scale distance before. This is an improvement of several orders of magnitude higher compared to prior demonstrations,” says Notaros.

By varying the wavelength of the optical signal that powers the chip, the researchers could steer the focused beam over a range larger than a millimeter and with microscale accuracy.

To test their device, the researchers started by trying to capture and manipulate tiny polystyrene spheres. Once they succeeded, they moved on to trapping and tweezing cancer cells provided by the Voldman group.

“There were many unique challenges that came up in the process of applying silicon photonics to biophysics,” Sneh adds.

The researchers had to determine how to track the motion of sample particles in a semiautomated fashion, ascertain the proper trap strength to hold the particles in place, and effectively postprocess data, for instance.

In the end, they were able to show the first cell experiments with single-beam optical tweezers.

Building off these results, the team hopes to refine the system to enable an adjustable focal height for the beam of light. They also want to apply the device to different biological systems and use multiple trap sites at the same time to manipulate biological particles in more complex ways.

“This is a very creative and important paper in many ways,” says Ben Miller, Dean’s Professor of Dermatology and professor of biochemistry and biophysics at the University of Rochester, who was not involved with this work. “For one, given that silicon photonic chips can be made at low cost, it potentially democratizes optical tweezing experiments. That may sound like something that only would be of interest to a few scientists, but in reality having these systems widely available will allow us to study fundamental problems in single-cell biophysics in ways previously only available to a few labs given the high cost and complexity of the instrumentation. I can also imagine many applications where one of these devices (or possibly an array of them) could be used to improve the sensitivity of disease diagnostic.”

This research is funded by the National Science Foundation (NSF), an MIT Frederick and Barbara Cronin Fellowship, and the MIT Rolf G. Locher Endowed Fellowship.

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

Celebrating the people behind Kendall Square’s innovation ecosystem

While it’s easy to be amazed by the constant drumbeat of innovations coming from Kendall Square in Cambridge, Massachusetts, sometimes overlooked are the dedicated individuals working to make those scientific and technological breakthroughs a reality. Every day, people in the neighborhood tackle previously intractable problems and push the frontiers of their fields.

This year’s Kendall Square Association (KSA) Annual Meeting centered around celebrating the people behind the area’s prolific innovation ecosystem. That included a new slate of awards and recognitions for community members and a panel discussion featuring MIT President Sally Kornbluth.

“It’s truly inspiring to be surrounded by all of you: people who seem to share an exuberant curiosity, a pervasive ethic of service, and the baseline expectation that we’re all interested in impact — in making a difference for people and the planet,” Kornbluth said.

The gathering took place in MIT’s Walker Memorial (Building 50) on Memorial Drive and attracted entrepreneurs, life science workers, local students, restaurant and retail shop owners, and leaders of nonprofits.

The KSA itself is a nonprofit organization made up of over 150 organizations across the greater Kendall Square region, from large companies to universities like MIT and Harvard, along with the independent shops and restaurants that give Kendall Square its distinct character.

New to this year’s event were two Founder Awards, which were given to Sangeeta Bhatia, the the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and Michal Preminger, head of Johnson and Johnson Innovation, for their work bringing people together to achieve hard things that benefit humanity.

The KSA will donate $2,500 to the Science Club for Girls in Bhatia’s honor and $2,500 to Innovators for Purpose in honor of Preminger.

Recognition was also given to Alex Cheung of the Cambridge Innovation Center and Shazia Mir of LabCentral for their work bringing Kendall Square’s community members together.

Cambridge Mayor Denise Simmons also spoke at the event, noting the vital role the Kendall Square community has played in things like Covid-19 vaccine development and in the fight against climate change.

“As many of you know, Cambridge has a long and proud history of innovation, with the presence of MIT and the remarkable growth of the tech and life science industry examples of that,” Simons said. “We are leaving a lasting, positive impact in our city. This community has made and continues to make enormous contributions, not just to our city but to the world.”

In her talk, Kornbluth also introduced the Kendall Square community to her plans for The Climate Project at MIT, which is designed to focus the Institute’s talent and resources to achieve real-world impact on climate change faster. The project will provide funding and catalyze partnerships around six climate “missions,” or broad areas where MIT researchers will seek to identify gaps in the global climate response that MIT can help fill.

“The Climate Project is a whole-of-MIT mobilization that’s mission driven, solution focused, and outward looking,” Kornbluth explained. “If you want to make progress, faster and at scale, that’s the way!”

After mingling with Kendall community members, Kornbluth said she still considers herself a newbie to the area but is coming to see the success of Kendall Square and MIT as more than a coincidence.

“The more time I spend here, the more I come to understand the incredible synergies between MIT and Kendall Square,” Kornbluth said. “We know, for example, that proximity is an essential ingredient in our collective and distinctive recipe for impact. That proximity, and the cross-fertilization that comes with it, helps us churn out new technologies and patents, found startups, and course-correct our work as we try to keep pace with the world’s challenges. We can’t do any of this separately. Our work together — all of us in this thriving, wildly entrepreneurial community — is what drives the success of our innovation ecosystem.”

de MIT News https://ift.tt/0UN1nbR

3 Questions: Bridging anthropology and engineering for clean energy in Mongolia

In 2021, Michael Short, an associate professor of nuclear science and engineering, approached professor of anthropology Manduhai Buyandelger with an unusual pitch: collaborating on a project to prototype a molten salt heat bank in Mongolia, Buyandelger’s country of origin and place of her scholarship. It was also an invitation to forge a novel partnership between two disciplines that rarely overlap. Developed in collaboration with the National University of Mongolia (NUM), the device was built to provide heat for people in colder climates, and in places where clean energy is a challenge. 

Buyandelger and Short teamed up to launch Anthro-Engineering Decarbonization at the Million-Person Scale, an initiative intended to advance the heat bank idea in Mongolia, and ultimately demonstrate its potential as a scalable clean heat source in comparably challenging sites around the world. This project received funding from the inaugural MIT Climate and Sustainability Consortium Seed Awards program. In order to fund various components of the project, especially student involvement and additional staff, the project also received support from the MIT Global Seed Fund, New Engineering Education Transformation (NEET), Experiential Learning Office, Vice Provost for International Activities, and d’Arbeloff Fund for Excellence in Education.

As part of this initiative, the partners developed a special topic course in anthropology to teach MIT undergraduates about Mongolia’s unique energy and climate challenges, as well as the historical, social, and economic context in which the heat bank would ideally find a place. The class 21A.S01 (Anthro-Engineering: Decarbonization at the Million-Person Scale) prepares MIT students for a January Independent Activities Period (IAP) trip to the Mongolian capital of Ulaanbaatar, where they embed with Mongolian families, conduct research, and collaborate with their peers. Mongolian students also engaged in the project. Anthropology research scientist and lecturer Lauren Bonilla, who has spent the past two decades working in Mongolia, joined to co-teach the class and lead the IAP trips to Mongolia. 

With the project now in its third year and yielding some promising solutions on the ground, Buyandelger and Bonilla reflect on the challenges for anthropologists of advancing a clean energy technology in a developing nation with a unique history, politics, and culture. 

Q: Your roles in the molten salt heat bank project mark departures from your typical academic routine. How did you first approach this venture?

Buyandelger: As an anthropologist of contemporary religion, politics, and gender in Mongolia, I have had little contact with the hard sciences or building or prototyping technology. What I do best is listening to people and working with narratives. When I first learned about this device for off-the-grid heating, a host of issues came straight to mind right away that are based on socioeconomic and cultural context of the place. The salt brick, which is encased in steel, must be heated to 400 degrees Celsius in a central facility, then driven to people’s homes. Transportation is difficult in Ulaanbaatar, and I worried about road safety when driving the salt brick to gers [traditional Mongolian homes] where many residents live. The device seemed a bit utopian to me, but I realized that this was an amazing educational opportunity: We could use the heat bank as part of an ethnographic project, so students could learn about the everyday lives of people — crucially, in the dead of winter — and how they might respond to this new energy technology in the neighborhoods of Ulaanbaatar.

Bonilla: When I first went to Mongolia in the early 2000s as an undergraduate student, the impacts of climate change were already being felt. There had been a massive migration to the capital after a series of terrible weather events that devastated the rural economy. Coal mining had emerged as a vital part of the economy, and I was interested in how people regarded this industry that both provided jobs and damaged the air they breathed. I am trained as a human geographer, which involves seeing how things happening in a local place correspond to things happening at a global scale. Thinking about climate or sustainability from this perspective means making linkages between social life and environmental life. In Mongolia, people associated coal with national progress. Based on historical experience, they had low expectations for interventions brought by outsiders to improve their lives. So my first take on the molten salt project was that this was no silver bullet solution. At the same time, I wanted to see how we could make this a great project-based learning experience for students, getting them to think about the kind of research necessary to see if some version of the molten salt would work.

Q: After two years, what lessons have you and the students drawn from both the class and the Ulaanbaatar field trips?

Buyandelger: We wanted to make sure MIT students would not go to Mongolia and act like consultants. We taught them anthropological methods so they could understand the experiences of real people and think about how to bring people and new technologies together. The students, from engineering and anthropological and social science backgrounds, became critical thinkers who could analyze how people live in ger districts. When they stay with families in Ulaanbaatar in January, they not only experience the cold and the pollution, but they observe what people do for work, how parents care for their children, how they cook, sleep, and get from one place to another. This enables them to better imagine and test out how these people might utilize the molten salt heat bank in their homes.

Bonilla: In class, students learn that interventions like this often fail because the implementation process doesn’t work, or the technology doesn’t meet people’s real needs. This is where anthropology is so important, because it opens up the wider landscape in which you’re intervening. We had really difficult conversations about the professional socialization of engineers and social scientists. Engineers love to work within boxes, but don’t necessarily appreciate the context in which their invention will serve.

As a group, we discussed the provocative notion that engineers construct and anthropologists deconstruct. This makes it seem as if engineers are creators, and anthropologists are brought in as add-ons to consult and critique engineers’ creations. Our group conversation concluded that a project such as ours benefits from an iterative back-and-forth between the techno-scientific and humanistic disciplines.

Q: So where does the molten salt brick project stand?

Bonilla: Our research in Mongolia helped us produce a prototype that can work: Our partners at NUM are developing a hybrid stove that incorporates the molten salt brick. Supervised by instructor Nathan Melenbrink of MIT’s NEET program, our engineering students have been involved in this prototyping as well.

The concept is for a family to heat it up using a coal fire once a day and it warms their home overnight. Based on our anthropological research, we believe that this stove would work better than the device as originally conceived. It won’t eliminate coal use in residences, but it will reduce emissions enough to have a meaningful impact on ger districts in Ulaanbaatar. The challenge now is getting funding to NUM so they can test different salt combinations and stove models and employ local blacksmiths to work on the design.

This integrated stove/heat bank will not be the ultimate solution to the heating and pollution crisis in Mongolia. But it will be something that can inspire even more ideas. We feel with this project we are planting all kinds of seeds that will germinate in ways we cannot anticipate. It has sparked new relationships between MIT and Mongolian students, and catalyzed engineers to integrate a more humanistic, anthropological perspective in their work.

Buyandelger: Our work illustrates the importance of anthropology in responding to the unpredictable and diverse impacts of climate change. Without our ethnographic research — based on participant observation and interviews, led by Dr. Bonilla, — it would have been impossible to see how the prototyping and modifications could be done, and where the molten salt brick could work and what shape it needed to take. This project demonstrates how indispensable anthropology is in moving engineering out of labs and companies and directly into communities.

Bonilla: This is where the real solutions for climate change are going to come from. Even though we need solutions quickly, it will also take time for new technologies like molten salt bricks to take root and grow. We don’t know where the outcomes of these experiments will take us. But there’s so much that’s emerging from this project that I feel very hopeful about.

de MIT News https://ift.tt/GIcYz4T

How AI is improving simulations with smarter sampling techniques

Imagine you’re tasked with sending a team of football players onto a field to assess the condition of the grass (a likely task for them, of course). If you pick their positions randomly, they might cluster together in some areas while completely neglecting others. But if you give them a strategy, like spreading out uniformly across the field, you might get a far more accurate picture of the grass condition.

Now, imagine needing to spread out not just in two dimensions, but across tens or even hundreds. That's the challenge MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers are getting ahead of. They've developed an AI-driven approach to “low-discrepancy sampling,” a method that improves simulation accuracy by distributing data points more uniformly across space.

A key novelty lies in using graph neural networks (GNNs), which allow points to “communicate” and self-optimize for better uniformity. Their approach marks a pivotal enhancement for simulations in fields like robotics, finance, and computational science, particularly in handling complex, multidimensional problems critical for accurate simulations and numerical computations.

“In many problems, the more uniformly you can spread out points, the more accurately you can simulate complex systems,” says T. Konstantin Rusch, lead author of the new paper and MIT CSAIL postdoc. “We've developed a method called Message-Passing Monte Carlo (MPMC) to generate uniformly spaced points, using geometric deep learning techniques. This further allows us to generate points that emphasize dimensions which are particularly important for a problem at hand, a property that is highly important in many applications. The model’s underlying graph neural networks lets the points 'talk' with each other, achieving far better uniformity than previous methods.”

Their work was published in the September issue of the Proceedings of the National Academy of Sciences.

Take me to Monte Carlo

The idea of Monte Carlo methods is to learn about a system by simulating it with random sampling. Sampling is the selection of a subset of a population to estimate characteristics of the whole population. Historically, it was already used in the 18th century,  when mathematician Pierre-Simon Laplace employed it to estimate the population of France without having to count each individual.

Low-discrepancy sequences, which are sequences with low discrepancy, i.e., high uniformity, such as Sobol’, Halton, and Niederreiter, have long been the gold standard for quasi-random sampling, which exchanges random sampling with low-discrepancy sampling. They are widely used in fields like computer graphics and computational finance, for everything from pricing options to risk assessment, where uniformly filling spaces with points can lead to more accurate results. 

The MPMC framework suggested by the team transforms random samples into points with high uniformity. This is done by processing the random samples with a GNN that minimizes a specific discrepancy measure.

One big challenge of using AI for generating highly uniform points is that the usual way to measure point uniformity is very slow to compute and hard to work with. To solve this, the team switched to a quicker and more flexible uniformity measure called L2-discrepancy. For high-dimensional problems, where this method isn’t enough on its own, they use a novel technique that focuses on important lower-dimensional projections of the points. This way, they can create point sets that are better suited for specific applications.

The implications extend far beyond academia, the team says. In computational finance, for example, simulations rely heavily on the quality of the sampling points. “With these types of methods, random points are often inefficient, but our GNN-generated low-discrepancy points lead to higher precision,” says Rusch. “For instance, we considered a classical problem from computational finance in 32 dimensions, where our MPMC points beat previous state-of-the-art quasi-random sampling methods by a factor of four to 24.”

Robots in Monte Carlo

In robotics, path and motion planning often rely on sampling-based algorithms, which guide robots through real-time decision-making processes. The improved uniformity of MPMC could lead to more efficient robotic navigation and real-time adaptations for things like autonomous driving or drone technology. “In fact, in a recent preprint, we demonstrated that our MPMC points achieve a fourfold improvement over previous low-discrepancy methods when applied to real-world robotics motion planning problems,” says Rusch.

“Traditional low-discrepancy sequences were a major advancement in their time, but the world has become more complex, and the problems we're solving now often exist in 10, 20, or even 100-dimensional spaces,” says Daniela Rus, CSAIL director and MIT professor of electrical engineering and computer science. “We needed something smarter, something that adapts as the dimensionality grows. GNNs are a paradigm shift in how we generate low-discrepancy point sets. Unlike traditional methods, where points are generated independently, GNNs allow points to 'chat' with one another so the network learns to place points in a way that reduces clustering and gaps — common issues with typical approaches.”

Going forward, the team plans to make MPMC points even more accessible to everyone, addressing the current limitation of training a new GNN for every fixed number of points and dimensions.

“Much of applied mathematics uses continuously varying quantities, but computation typically allows us to only use a finite number of points,” says Art B. Owen, Stanford University professor of statistics, who wasn’t involved in the research. “The century-plus-old field of discrepancy uses abstract algebra and number theory to define effective sampling points. This paper uses graph neural networks to find input points with low discrepancy compared to a continuous distribution. That approach already comes very close to the best-known low-discrepancy point sets in small problems and is showing great promise for a 32-dimensional integral from computational finance. We can expect this to be the first of many efforts to use neural methods to find good input points for numerical computation.”

Rusch and Rus wrote the paper with University of Waterloo researcher Nathan Kirk, Oxford University’s DeepMind Professor of AI and former CSAIL affiliate Michael Bronstein, and University of Waterloo Statistics and Actuarial Science Professor Christiane Lemieux. Their research was supported, in part, by the AI2050 program at Schmidt Futures, Boeing, the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator, the Swiss National Science Foundation, Natural Science and Engineering Research Council of Canada, and an EPSRC Turing AI World-Leading Research Fellowship. 

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

An interstellar instrument takes a final bow

They planned to fly for four years and to get as far as Jupiter and Saturn. But nearly half a century and 15 billion miles later, NASA’s twin Voyager spacecraft have far exceeded their original mission, winging past the outer planets and busting out of our heliosphere, beyond the influence of the sun. The probes are currently making their way through interstellar space, traveling farther than any human-made object.

Along their improbable journey, the Voyagers made first-of-their-kind observations at all four giant outer planets and their moons using only a handful of instruments, including MIT’s Plasma Science Experiments — identical plasma sensors that were designed and built in the 1970s in Building 37 by MIT scientists and engineers.

The Plasma Science Experiment (also known as the Plasma Spectrometer, or PLS for short) measured charged particles in planetary magnetospheres, the solar wind, and the interstellar medium, the material between stars. Since launching on the Voyager 2 spacecraft in 1977, the PLS has revealed new phenomena near all the outer planets and in the solar wind across the solar system. The experiment played a crucial role in confirming the moment when Voyager 2 crossed the heliosphere and moved outside of the sun’s regime, into interstellar space.

Now, to conserve the little power left on Voyager 2 and prolong the mission’s life, the Voyager scientists and engineers have made the decision to shut off MIT’s Plasma Science Experiment. It’s the first in a line of science instruments that will progressively blink off over the coming years. On Sept. 26, the Voyager 2 PLS sent its last communication from 12.7 billion miles away, before it received the command to shut down.

MIT News spoke with John Belcher, the Class of 1922 Professor of Physics at MIT, who was a member of the original team that designed and built the plasma spectrometers, and John Richardson, principal research scientist at MIT’s Kavli Institute for Astrophysics and Space Research, who is the experiment’s principal investigator. Both Belcher and Richardson offered their reflections on the retirement of this interstellar piece of MIT history.

Q: Looking back at the experiment’s contributions, what are the greatest hits, in terms of what MIT’s Plasma Spectrometer has revealed about the solar system and interstellar space?

Richardson: A key PLS finding at Jupiter was the discovery of the Io torus, a plasma donut surrounding Jupiter, formed from sulphur and oxygen from Io’s volcanos (which were discovered in Voyager images). At Saturn, PLS found a magnetosphere full of water and oxygen that had been knocked off of Saturn’s icy moons. At Uranus and Neptune, the tilt of the magnetic fields led to PLS seeing smaller density features, with Uranus’ plasma disappearing near the planet. Another key PLS observation was of the termination shock, which was the first observation of the plasma at the largest shock in the solar system, where the solar wind stopped being supersonic. This boundary had a huge drop in speed and an increase in the density and temperature of the solar wind. And finally, PLS documented Voyager 2’s crossing of the heliopause by detecting a stopping of outward-flowing plasma. This signaled the end of the solar wind and the beginning of the local interstellar medium (LISM). Although not designed to measure the LISM, PLS constantly measured the interstellar plasma currents beyond the heliosphere. It is very sad to lose this instrument and data!

Belcher: It is important to emphasize that PLS was the result of decades of development by MIT Professor Herbert Bridge (1919-1995) and Alan Lazarus (1931-2014). The first version of the instrument they designed was flown on Explorer 10 in 1961. And the most recent version is flying on the Solar Probe, which is collecting measurements very close to the sun to understand the origins of solar wind. Bridge was the principal investigator for plasma probes on spacecraft which visited the sun and every major planetary body in the solar system.

Q: During their tenure aboard the Voyager probes, how did the plasma sensors do their job over the last 47 years?

Richardson: There were four Faraday cup detectors designed by Herb Bridge that measured currents from ions and electrons that entered the detectors. By measuring these particles at different energies, we could find the plasma velocity, density, and temperature in the solar wind and in the four planetary magnetospheres Voyager encountered. Voyager data were (and are still) sent to Earth every day and received by NASA’s deep space network of antennae. Keeping two 1970s-era spacecraft going for 47 years and counting has been an amazing feat of JPL engineering prowess — you can google the most recent rescue when Voyager 1 lost some memory in November of 2023 and stopped sending data. JPL figured out the problem and was able to reprogram the flight data system from 15 billion miles away, and all is back to normal now. Shutting down PLS involves sending a command which will get to Voyager 2 about 19 hours later, providing the rest of the spacecraft enough power to continue.

Q: Once the plasma sensors have shut down, how much more could Voyager do, and how far might it still go?

Richardson: Voyager will still measure the galactic cosmic rays, magnetic fields, and plasma waves. The available power decreases about 4 watts per year as the plutonium which powers them decays. We hope to keep some of the instruments running until the mid-2030s, but that will be a challenge as power levels decrease.

Belcher: Nick Oberg at the Kapteyn Astronomical Institute in the Netherlands has made an exhaustive study of the future of the spacecraft, using data from the European Space Agency’s spacecraft Gaia. In about 30,000 years, the spacecraft will reach the distance to the nearest stars. Because space is so vast, there is zero chance that the spacecraft will collide directly with a star in the lifetime of the universe. However, the spacecraft surface will erode by microcollisions with vast clouds of interstellar dust, but this happens very slowly. 

In Oberg’s estimate, the Golden Records [identical records that were placed aboard each probe, that contain selected sounds and images to represent life on Earth] are likely to survive for a span of over 5 billion years. After those 5 billion years, things are difficult to predict, since at this point, the Milky Way will collide with its massive neighbor, the Andromeda galaxy. During this collision, there is a one in five chance that the spacecraft will be flung into the intergalactic medium, where there is little dust and little weathering. In that case, it is possible that the spacecraft will survive for trillions of years. A trillion years is about 100 times the current age of the universe. The Earth ceases to exist in about 6 billion years, when the sun enters its red giant phase and engulfs it.

In a “poor man’s” version of the Golden Record, Robert Butler, the chief engineer of the Plasma Instrument, inscribed the names of the MIT engineers and scientists who had worked on the spacecraft on the collector plate of the side-looking cup. Butler’s home state was New Hampshire, and he put the state motto, “Live Free or Die,” at the top of the list of names. Thanks to Butler, although New Hampshire will not survive for a trillion years, its state motto might. The flight spare of the PLS instrument is now displayed at the MIT Museum, where you can see the text of Butler’s message by peering into the side-looking sensor. 

de MIT News https://ift.tt/W1StZT6

Q&A: A new initiative to help strengthen democracy

In the United States and around the world, democracy is under threat. Anti-democratic attitudes have become more prevalent, partisan polarization is growing, misinformation is omnipresent, and politicians and citizens sometimes question the integrity of elections. 

With this backdrop, the MIT Department of Political Science is launching an effort to establish a Strengthening Democracy Initiative. In this Q&A, department head David Singer, the Raphael Dorman-Helen Starbuck Professor of Political Science, discusses the goals and scope of the initiative.

Q: What is the purpose of the Strengthening Democracy Initiative?

A: Well-functioning democracies require accountable representatives, accurate and freely available information, equitable citizen voice and participation, free and fair elections, and an abiding respect for democratic institutions. It is unsettling for the political science community to see more and more evidence of democratic backsliding in Europe, Latin America, and even here in the U.S. While we cannot single-handedly stop the erosion of democratic norms and practices, we can focus our energies on understanding and explaining the root causes of the problem, and devising interventions to maintain the healthy functioning of democracies.

MIT political science has a history of generating important research on many facets of the democratic process, including voting behavior, election administration, information and misinformation, public opinion and political responsiveness, and lobbying. The goals of the Strengthening Democracy Initiative are to place these various research programs under one umbrella, to foster synergies among our various research projects and between political science and other disciplines, and to mark MIT as the country’s leading center for rigorous, evidence-based analysis of democratic resiliency.

Q: What is the initiative’s research focus?

A: The initiative is built upon three research pillars. One pillar is election science and administration. Democracy cannot function without well-run elections and, just as important, popular trust in those elections. Even within the U.S., let alone other countries, there is tremendous variation in the electoral process: whether and how people register to vote, whether they vote in person or by mail, how polling places are run, how votes are counted and validated, and how the results are communicated to citizens.

The MIT Election Data and Science Lab is already the country’s leading center for the collection and analysis of election-related data and dissemination of electoral best practices, and it is well positioned to increase the scale and scope of its activities.

The second pillar is public opinion, a rich area of study that includes experimental studies of public responses to misinformation and analyses of government responsiveness to mass attitudes. Our faculty employ survey and experimental methods to study a range of substantive areas, including taxation and health policy, state and local politics, and strategies for countering political rumors in the U.S. and abroad. Faculty research programs form the basis for this pillar, along with longstanding collaborations such as the Political Experiments Research Lab, an annual omnibus survey in which students and faculty can participate, and frequent conferences and seminars.

The third pillar is political participation, which includes the impact of the criminal justice system and other negative interactions with the state on voting, the creation of citizen assemblies, and the lobbying behavior of firms on Congressional legislation. Some of this research relies on machine learning and AI to cull and parse an enormous amount of data, giving researchers visibility into phenomena that were previously difficult to analyze. A related research area on political deliberation brings together computer science, AI, and the social sciences to analyze the dynamics of political discourse in online forums and the possible interventions that can attenuate political polarization and foster consensus.

The initiative’s flexible design will allow for new pillars to be added over time, including international and homeland security, strengthening democracies in different regions of the world, and tackling new challenges to democratic processes that we cannot see yet.

Q: Why is MIT well-suited to host this new initiative?

A: Many people view MIT as a STEM-focused, highly technical place. And indeed it is, but there is a tremendous amount of collaboration across and within schools at MIT — for example, between political science and the Schwarzman College of Computing and the Sloan School of Management, and between the social science fields and the schools of science and engineering. The Strengthening Democracy Initiative will benefit from these collaborations and create new bridges between political science and other fields. It’s also important to note that this is a nonpartisan research endeavor. The MIT political science department has a reputation for rigorous, data-driven approaches to the study of politics, and its position within the MIT ecosystem will help us to maintain a reputation as an “honest broker,” and to disseminate path-breaking, evidence-based research and interventions to help democracies become more resilient.

Q: Will the new initiative have an educational mission?

A: Of course! The department has a long history of bringing in scores of undergraduate researchers via MIT’s Undergraduate Research Opportunities Program. The initiative will be structured to provide these students with opportunities to study various facets of the democratic process, and for faculty to have a ready pool of talented students to assist with their projects. My hope is to provide students with the resources and opportunities to test their own theories by designing and implementing surveys in the U.S. and abroad, and use insights and tools from computer science, applied statistics, and other disciplines to study political phenomena. As the initiative grows, I expect more opportunities for students to collaborate with state and local officials on improvements to election administration, and to study new puzzles related to healthy democracies.

Postdoctoral researchers will also play a prominent role by advancing research across the initiative’s pillars, supervising undergraduate researchers, and handling some of the administrative aspects of the work.

Q: This sounds like a long-term endeavor. Do you expect this initiative to be permanent?

A: Yes. We already have the pieces in place to create a leading center for the study of healthy democracies (and how to make them healthier). But we need to build capacity, including resources for a pool of researchers to shift from one project to another, which will permit synergies between projects and foster new ones. A permanent initiative will also provide the infrastructure for faculty and students to respond swiftly to current events and new research findings — for example, by launching a nationwide survey experiment, or collecting new data on an aspect of the electoral process, or testing the impact of a new AI technology on political perceptions. As I like to tell our supporters, there are new challenges to healthy democracies that were not on our radar 10 years ago, and no doubt there will be others 10 years from now that we have not imagined. We need to be prepared to do the rigorous analysis on whatever challenges come our way. And MIT Political Science is the best place in the world to undertake this ambitious agenda in the long term.

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

Microelectronics projects awarded CHIPS and Science Act funding

MIT and Lincoln Laboratory are participants in four microelectronics proposals selected for funding to the Northeast Microelectronics Coalition (NEMC) Hub. The funding comes from the Microelectronics Commons, a $2 billion initiative of the CHIPS and Science Act to strengthen U.S. leadership in semiconductor manufacturing and innovation. The regional awards are among 33 projects announced as part of a $269 million federal investment.

U.S. Department of Defense (DoD) and White House officials announced the awards during an event on Sept. 18, hosted by the NEMC Hub at MIT Lincoln Laboratory. The NEMC Hub, a division of the Massachusetts Technology Collaborative, leads a network of more than 200 member organizations across the region to enable the lab-to-fab transition of critical microelectronics technologies for the DoD. The NEMC Hub is one of eight regional hubs forming a nationwide chip network under the Microelectronics Commons and is executed through the Naval Surface Warfare Center Crane Division and the National Security Technology Accelerator (NSTXL).

"The $38 million in project awards to the NEMC Hub are a recognition of the capability, capacity, and commitment of our members," said Mark Halfman, NEMC Hub director. "We have a tremendous opportunity to grow microelectronics lab-to-fab capabilities across the Northeast region and spur the growth of game-changing technologies."

"We are very pleased to have Lincoln Laboratory be a central part of the vibrant ecosystem that has formed within the Microelectronics Commons program," said Mark Gouker, assistant head of the laboratory's Advanced Technology Division and NEMC Hub advisory group representative. "We have made strong connections to academia, startups, DoD contractors, and commercial sector companies through collaborations with our technical staff and by offering our microelectronics fabrication infrastructure to assist in these projects. We believe this tighter ecosystem will be important to future Microelectronics Commons programs as well as other CHIPS and Science Act programs."

The nearly $38 million award to the NEMC Hub is expected to support six collaborative projects, four of which will involve MIT and/or Lincoln Laboratory.

"These projects promise significant gains in advanced microelectronics technologies," said Ian A. Waitz, MIT's vice president for research. "We look forward to working alongside industry and government organizations in the NEMC Hub to strengthen U.S. microelectronics innovation, workforce and education, and lab-to-fab translation."

The projects selected for funding support key technology areas identified in the federal call for competitive proposals. MIT campus researchers will participate in a project advancing commercial leap-ahead technologies, titled "Advancing DoD High Power Systems: Transition of High Al% AlGaN from Lab to Fab," and another in the area of 5G/6G, called "Wideband, Scalable MIMO arrays for NextG Systems: From Antennas to Decoders."

Researchers both at Lincoln Laboratory and on campus will contribute to a quantum technology project called "Community‐driven Hybrid Integrated Quantum‐Photonic Integrated circuits (CHIQPI)."

Lincoln Laboratory researchers will also participate in the "Wideband Same‐Frequency STAR Array Platform Based on Heterogeneous Multi-Domain Self‐Interference Cancellation" project.

The anticipated funding for these four projects follows a $7.7 million grant awarded earlier this year to MIT from the NEMC Hub, alongside an agreement between MIT and Applied Materials, to add advanced nanofabrication equipment and capabilities to MIT.nano.

The funding comes amid construction of the Compound Semiconductor Laboratory – Microsystem Integration Facility (CSL-MIF) at Lincoln Laboratory. The CSL-MIF will complement Lincoln Laboratory's existing Microelectronics Laboratory, which has remained the U.S. government's most advanced silicon-based research and fabrication facility for decades. When completed in 2028, the CSL-MIF is expected to play a vital role in the greater CHIPS and Science Act ecosystem.

"Lincoln Laboratory has a long history of developing advanced microelectronics to enable critical national security systems," said Melissa Choi, Lincoln Laboratory director. "We are excited to embark on these awarded projects, leveraging our microelectronics facilities and partnering with fellow hub members to be at the forefront of U.S. microelectronics innovation."

Officials who spoke at the Sept. 18 event emphasized the national security and economic imperatives to building a robust microelectronics workforce and innovation network.

"The Microelectronics Commons is an essential part of the CHIPS and Science Act's whole-of-government approach to strengthen the U.S. microelectronics ecosystem and secure lasting technical leadership in this critical sector," said Dev Shenoy, the principal director for microelectronics in the Office of the Under Secretary of Defense for Research and Engineering. "I believe in the incredible impact this work will have for American economies, American defense, and the American people."

"The secret sauce of what made the U.S. the lead innovator in the world for the last 100 years was the coming together of the U.S. government and the public sector, together with the private sector and teaming up with academia and research," said Amos Hochstein, special presidential coordinator for global infrastructure and energy security at the U.S. Department of State. "That is what enabled us to be the forefront of innovation and technology, and that is what we have to do again."

de MIT News https://ift.tt/Pufhw0r