LIDS All 2020 (Vol. 16) by MITLIDS

THE SCIENCE AND ART OF INTELLIGENT SYSTEMS

Editor: Jennifer Donovan

Design & Illustration: Michael Lewy

Writers: Grace Chua

Katie DePasquale Greta Friar

Photos of LIDS Student Conference provided courtesy of Francisco Jaimes. Photo of John Tsitsiklis by Donna Coveney. Photography credits:

Massachusetts Institute of Technology Laboratory for Information and Decision Systems

77 Massachusetts Avenue, Room 32-D608

Cambridge, Massachusetts 02139

http://lids.mit.edu/ send questions or comments to lidsmag@mit.edu

A Message from the Outgoing Director

It is my pleasure to introduce the latest issue of LIDS|All.

The period covered by this issue was an extraordinary one. We began in the fall of 2019 with the very successful LIDS@80: A Celebration, an event that brought together over 400 people from around the world, to commemorate the lab’s 80th anniversary. (You can read more, and see some wonderful event photos, later in this issue.) For me, the LIDS@80 event was also a rare opportunity to see much of the LIDS community in one place — a truly special occasion that spanned disciplines and generations, and highlighted once again the warmth, energy, and intellectual depth of our community.

In January 2020, with the same forward-looking spirit showcased at our anniversary event, LIDS officially joined the MIT Stephen A. Schwarzman College of Computing, MIT’s investment in the future of computing and AI. It is a development that keeps LIDS at the center of some of the most important current developments in the information and decision sciences, and a signal of the continued importance of LIDS-style research. This idea was summed up well in remarks by Dan Huttenlocher, dean of the college, who said, “With regards to LIDS and the intellectual fabric of the world today, I think the time couldn’t be more exciting.”

The LIDS community carried this excitement even in the spring of 2020, when we saw the emergence of the Covid-19 pandemic. We were fortunate that we could take most of our work to the virtual world with relative ease. As events and research collaborations moved to Zoom, it was comforting to see our community meet the challenges of the pandemic together, and that its cohesion, energy, and vibrance was not diminished. Students, faculty, staff, and researchers continued their work from a distance, and

ABOUT LIDS

The Laboratory for Information and Decision Systems (LIDS) at MIT, established in 1940 as the Servomechanisms Laboratory, currently focuses on four main research areas: communication and networks, control and system theory, optimization, and statistical signal processing. These areas range from basic theoretical studies to a wide array of applications in the communication, computer, control, electronics, and aerospace industries.

many were involved in projects driven by the Covid-19 pandemic.

This past year, we also lost a dear friend and mentor, MIT Electrical Engineering and Computer Science Professor Emeritus Michael Athans, who passed away in May 2020. A remarkable researcher and educator — and a transformative director of the lab — it is hard to overstate the influence Michael had on the field of systems control theory. We were honored to host his virtual memorial, celebrating his life and achievements, and sharing memories of his strength, kindness, and sharp wit.

In this issue you will read more about Michael, and the ways he laid the foundations for the LIDS of today. You will also read about graduate student Manon Revel’s work investigating the effect of online advertising on trust in journalism; LIDS Financial Coordinator Yalan Yang; faculty member Cathy Wu’s research applying signals and systems thinking to integrating autonomous vehicles in the transportation system; and graduate student Alireza Fallah’s work on personalized federated learning.

By this time, my tenure as director has come to a close, and this is my last introduction to LIDS|All. Holding this position, in a lab that has been my home for more than forty years, has been a tremendous honor and privilege. On this occasion, I would like to thank Professor Eytan Modiano who served as Associate Director, and then as Interim Director when I stepped down at the end of 2020. I am thrilled that Associate Professor of Aeronautics and Astronautics, Sertac Karaman, (a LIDS alum and faculty member) has taken the helm, in July of 2021. LIDS is as strong as ever, and in good hands!

I hope you enjoy this issue of the magazine and that it finds you healthy and safe.

Sincerely,

John N. Tsitsiklis

LIDS is truly an interdisciplinary lab, home to over 100 graduate students and post-doctoral associates from EECS, Aero-Astro, and the School of Management. The intellectual culture at LIDS encourages students, postdocs, and faculty to both develop the conceptual structures of the above system areas and apply these structures to important engineering problems.

MAKING SENSE OF INFORMATION

By Greta Friar

As a student in LIDS and in the MIT Institute for Data, Systems, and Society (IDSS), Manon Revel has been investigating how advertising in online publications affects trust in journalism. The topic area is familiar for Manon— growing up, she wanted to be a journalist to follow in her father’s footsteps. As a teenager, Manon was assigned her first reporting gig covering a holiday firework display for the local radio station near her home on the Atlantic coast of France. Manon researched every one of the dozens of types of fireworks that would be launched and spent hours interviewing the person who would launch them before reporting live on the night. Manon continued reporting for radio and print (she started a high school newspaper that is still active), using the same thorough approach to prepare for each interview.

“I wanted to ask interesting people questions that they had never been asked before, so I read everything I could on them beforehand and then I tried to learn something new,” Manon says.

However, in France, students must narrow in on a field of study by the end of their secondary schooling, and Manon ultimately chose to focus on science—a field in which her penchant for meticulous research and love of discovery would be well served. She completed a bachelor’s and master’s degree in engineering and applied mathematics at École Centrale

Paris while pursuing other interests, including journalism, on the side. As she considered what to do next, Manon sought out a more interdisciplinary opportunity, and discovered that at MIT—in particular at LIDS and in the Master’s Program in Technology and Policy (TPP) at IDSS.

“I always felt like I had these different lives. Coming to MIT was the first time I felt that my interests could finally be put together, and that I could work on everything in unison,” Manon says.

Manon only intended to come for a master’s degree, but she loved the environment and the people so much that she stayed on for a PhD. She joined the new Social & Engineering Systems (SES) doctoral program within IDSS, which combines tools and methods from statistics and information sciences, engineering, and social sciences to tackle significant societal problems. It was the exact sort of interdisciplinary opportunity Manon had been seeking. She works with four academic advisors: Prof. Ali Jadbabaie (Department of Civil and Environmental Engineering), Prof. Dean Eckles (Sloan School of Management), Prof. Adam Berinsky (Department Political Science), and Prof. Munther Dahleh (Department of Electrical Engineering and Computer Science and director of IDSS). Though operating in the middle of their fields has sometimes been challenging—like having to speak four different

languages, Manon says—she’s been thrilled with the opportunities to combine the approaches of disparate disciplines in order to build on all of them and push beyond the boundaries of previous research.

The timing of Manon’s arrival, shortly after the 2016 United States presidential election, was perfect for a deep dive into journalism from an analytical perspective. Current events had underscored the importance of understanding how information is, for better and worse, disseminated in the digital era and made sense of by the people receiving it. Manon’s main project, together with fellow LIDS student Amir Tohidi, has been investigating the effect of clickbait ads on reader trust.

As publications have struggled with online revenue models, many have come to increasingly rely on “native ads” that are designed to blend in with news stories and are often supplied by a third-party network. The majority of these ads are clickbait, which Manon and her colleagues define as “something (such as a headline) designed to make readers want to click on a hyperlink especially when the link leads to content of dubious value or interest.” Manon wanted to quantify exactly how prevalent clickbait is, so she set up a Bayesian text classification program to recognize it. The AI was trained off of two testing sets labelled by users on Amazon’s Mechanical Turk service. Then, Manon scraped over 1.4 million ads from the

time period 2016-2019, and, using her text classification program, found that more than 80% were clickbait. Next, Manon ran two large-scale randomized experiments, which showed that one-time exposure to clickbait near an article could negatively impact readers’ trust in the article and publication. That effect was driven by medium-familiarity publications — defined as outlets recognized by 25-50% of the audience. On the contrary, the ads effect on the most well-known outlets, like CNN and Fox News, was found to be null.

Manon hopes that the project will raise awareness among journalism publications of the risk of losing readership in the long term in order to reap the short-term financial rewards of native advertising. She acknowledges that there’s not an easy solution, as publishers may not be able to afford losing the revenue. However, reader trust in high quality journalism is a crucial issue in a time of rampant “information disorder,” which is the umbrella term that researchers like Manon use to describe the combination of intentionally or maliciously false information, unwittingly shared misinformation, and true but harmful information that spreads like wildfire through the internet.

Recently, Manon has been working on several other projects that combine approaches across disciplines to quantify and decipher human behavior, with a focus on understanding the ways that people make sense of information

and how that impacts their voting decisions. She has been working with sociologists on a project about how audiences’ perception of the news resonates with media coverage. Another project is modeling a voting system where votes may be delegated to better-informed acquaintances.

One could imagine that so much time spent thinking about Manon’s chosen research topics—politics, journalism, untruths—could be draining, but Manon tries not to get drawn into the strong emotions that these subjects can provoke. She sees the goal of her work as understanding, rather than judging, how people think and behave. At the same time, in an occupation focused on information disorder, perhaps it is no surprise that one of the things Manon appreciates most about her colleagues is their authenticity. This, along with their willingness to admit what they don’t know, made it easy to stay on at LIDS and IDSS after her master’s, she says. It’s a wonderful environment in which Manon can do what she has always loved, which is to learn everything she can about a subject—whether fireworks, clickbait, or human behavior—and, now aided by the many scientific approaches she has acquired, try to discover something new.

THE PROMISE OF AUTONOMOUS VEHICLES

By Grace Chua

There’s a mesmerizing video animation on YouTube of simulated, self-driving traffic streaming through a six-lane, four-way intersection. Dozens of cars flow through the streets, pausing, turning, slowing and speeding up to avoid colliding with their neighbors. And not a single car stopping. But what if even one of those vehicles was not autonomous? What if only one was?

In the coming decades, autonomous vehicles will play a growing role in society, whether keeping drivers safer, making deliveries, or increasing accessibility and mobility for elderly or disabled passengers.

But, Prof. Cathy Wu argues, autonomous vehicles are just part of a complex transport system that may involve individual self-driving cars, delivery fleets, human drivers, and a range of last-mile solutions to get passengers to their doorstep – not to mention road infrastructure like highways, roundabouts and, yes, intersections.

transportation system? Equally important, how can we use this understanding to guide us towards better-functioning systems?

Cathy, who joined LIDS and MIT in 2019, is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering as well as a core faculty member of the MIT Institute for Data, Systems, and Society. Growing up in a Philadelphia-area family of electrical engineers, Cathy sought a field that would enable her to harness engineering skills to solve societal challenges.

During her undergraduate years at MIT, she reached out to Professor Seth Teller of CSAIL to discuss her interest in self-driving cars.

Teller, who passed away in 2014, met her questions with warm advice, said Cathy. “He told me, ‘If you have an idea of what your passion in life is, then you have to go after it as hard as you possibly can. Only then can you hope to find your true passion.’

Transport today accounts for about a third of US energy consumption. The decisions we make today about autonomous vehicles could have a big impact on this number — ranging from a 40 percent decrease in energy use to a doubling of energy consumption.

So how can we better understand the problem of integrating autonomous vehicles into the

“Anyone can tell you to go after your dreams, but his insight was that dreams and ambitions are not always clear from the start. It takes hard work to find and pursue your passion.”

Chasing that passion, Cathy would go on to work with Teller, as well as in Professor Daniela Rus’s Distributed Robotics Lab, and finally as a graduate student at University of Califor-

nia, Berkeley, where she won the IEEE Intelligent Transportation Systems Society’s best PhD award in 2019.

In graduate school, Cathy had an epiphany: she realized that for autonomous vehicles to fulfill their promise of fewer accidents, time saved, lower emissions, and greater socioeconomic and physical accessibility, these goals must be explicitly designed for, whether as physical infrastructure, algorithms used by vehicles and sensors, or deliberate policy decisions.

At LIDS, Cathy uses a type of machine learning called reinforcement learning to study how traffic systems behave, and how autonomous vehicles in those systems ought to behave to get the best possible outcomes.

Reinforcement learning, which was most famously used by AlphaGo, DeepMind’s human-beating Go program, is a powerful class of methods which capture the idea behind trialand-error — given an objective, a learning agent repeatedly attempts to achieve the objective, failing and learning from its mistakes in the process.

In a traffic system, the objectives might be to maximize the overall average velocity of vehicles, to minimize travel time, to minimize energy consumption, and so on.

When studying common components of traffic

networks such as grid roads, bottlenecks, and on- and off-ramps, Cathy and her colleagues have found that reinforcement learning can match, and in some cases exceed, the performance of current traffic control strategies. And more importantly, reinforcement learning can shed new light towards understanding complex networked systems -- which have long evaded classical control techniques. For instance, if just five to ten percent of vehicles on the road were autonomous and used reinforcement learning, that could eliminate congestion and boost vehicle speeds by 30 to 140 percent. And the learning from one scenario often translates well to others. These insights could one day soon help to inform public policy or business decisions.

In the course of this research, Cathy and her colleagues helped improve a class of reinforcement learning methods called policy gradient methods. Their advancements turned out to be a general improvement to most existing deep reinforcement learning methods.

But reinforcement learning techniques will need to be continually improved to keep up with the scale and shifts in infrastructure and changing behavior patterns. And research findings will need to be translated into action by urban planners, auto makers and other organizations.

Today, Cathy is collaborating with public agencies in Taiwan, and Indonesia to use insights

from her work to guide better dialogues and decisions. By changing traffic signals or using nudges to shift drivers’ behavior, are there other ways to achieve lower emissions or smoother traffic?

“I’m surprised by this work every day,” Cathy said. “We set out to answer a question about self-driving cars, and it turns out you can pull apart the insights, apply them in other ways, and then this leads to new exciting questions to answer.”

Cathy is happy to have found her intellectual home at LIDS. Her experience of it is as a “very deep, intellectual, friendly, and welcoming place.” And she counts among her research inspirations MIT course 6.003 Signals and Systems — a class she encourages everyone to take — taught in the tradition of Prof. Alan Oppenheim (RLE) and Prof. Alan Willsky (LIDS). “The course taught me that so much in this world could be fruitfully examined through the lens of signals and systems, be it electronics or institutions or society,” she said.

“I am just realizing as I’m saying this, that I’ve been empowered by LIDS thinking all along!”

Research and teaching through a pandemic haven’t been easy, but Cathy is making the best of a challenging first year as faculty. (“I’ve been working from home in Cambridge – my short walking commute is irrelevant at this point,” she said, wryly.) To unwind, she enjoys run-

ning, listening to podcasts covering topics ranging from science to history, and reverseengineering her favorite Trader Joe’s frozen foods.

She’s also been working on two COVID-related projects born at MIT: one explores how data from the environment, such as data collected by IoT connected thermometers, can help identify emerging community outbreaks. Another project asks if it’s possible to ascertain how contagious the virus is on public transport, and how different factors might decrease the transmission risk.

Both are in their early stages, Cathy said. “We hope to contribute a bit to the pool of knowledge that can help decision-makers somewhere. It’s been very enlightening and rewarding to do this and see all the other efforts going on around MIT.”

TEACHING MACHINES

By Katie DePasquale

Adaptation is at the heart of graduate student Alireza Fallah’s research. Currently in his fourth year at MIT, he received his master’s degree in 2019 and is continuing for his doctorate, finding a way forward despite the significant alterations to study, work, and life caused by the COVID-19 pandemic. Supervised by Prof. Asu Ozdaglar in the Electrical Engineering and Computer Science Department, he came to LIDS after getting his Bachelor of Science in electrical engineering and mathematics from Sharif University of Technology in Tehran, Iran. He chose MIT because of the lab’s stellar reputation. “What LIDS is famous for is using math for characterizing and modelling many of the things that happen in the real world. So the idea of borrowing all these mathematical tools and using them to deal with these problems was something I was looking for, and at MIT, lots of people are doing it.”

Alireza’s main interest is optimization. “What I have been working on recently is characterizing the theory of optimization algorithms used in various machine learning problems, in particular in meta learning and federated learning,” he explains. “In machine learning, the idea is you have some data set and you’re trying to train the model so that it works well on potentially unseen data…[but] the idea of meta learning is to train a meta model for lots of tasks so that it can be updated and then used for a specific task.”

To get a sense of meta and federated learning, think of Apple training a model to finish a person’s words when they’re writing a text message. This is quite a complex process because these automatic suggested endings have to adapt to the language and slang of the hundreds of millions of people worldwide who use iPhones. So Apple needs to get information from a percentage of their users, but they need to do it in a secure way, rather than the user sending them all of their data directly.

One way to approach this would be to use federated learning, which can take advantage of the combined computational power of all users, yielding a richer model trained over a larger set of data points while maintaining privacy. If they were to use federated learning, Apple would start with a general model, or algorithm, that they send from their server to a user’s phone. Then, as each user sends texts — in effect training the model — their data, combined with that of other users, would be used to update the general model on the server. This improved, updated model would then be sent back to all users.

“Federated learning means that you’ll be training that [model] by basically giving the model a slight update and sending it back to the server,” says Alireza. “The issue with that you can immediately see is that there’s one model. There’s one model that’s supposed to work for your text messages, my text messages, someone

living in England, in New York, in California, but people have different languages, different slangs…[and] having a shared model might not be a good idea, and having a little bit of personalization might be good.”

So what you actually have at server is a meta model — an algorithm that gets updated locally, based on each user’s data. Alireza explains, “You update it with your own data, I update it with my own data, so I get a personalized model. We all start from that meta model, but we get our own copy and update it with respect to our data.”

In his recent work, Alireza has been designing an algorithm that takes advantage of the benefits of federated learning while keeping the personalization that meta learning can provide. He and his colleagues have found that taking a meta-learning approach to federated learning can give crucial and timely personalization.

Another application that could see benefit from meta-learning is autonomous cars. Alireza says, “Let’s say you want to train the model that is behind the artificial intelligence model used in this car. So the thing is that sometimes your car needs to make a decision within a second.

Let’s say you’re going from your home to your workplace, you don’t know whether the light will be green or red, you don’t know whether you’re going to see a kid and need to stop, and so forth. So the idea is that if I can have a meta

model trained so that it can adjust to each of the situations with just a little amount of data, if my sensor just gives me one second of data and it can adjust the meta model, that would be perfect.” In other words, the model should behave as much like the human brain as pos-

sible, able to adapt quickly and make the safest, objectively best decision in a given situation without a lot of data.

Part of Alireza’s goal is to design and analyze algorithms for training such a model. “There are some algorithms where we have shown promising results in practice in real-world applications, in robotics for example. But the issue is we still have a lack of theory. You want to have guarantees for your algorithm. If you’re sending an autonomous car to the road, you want to be sure that it’s going to work. Our goal is to give theories and new algorithms for that training part of that meta model.”

Because meta learning in particular is still in the early stages of research and development, there’s a lot of theory to explore in the search to discover why and how it works. “This is a challenge for me, to complete the piece of the puzzle that is missing,” Alireza says. “And I like the problem also because it has its own challenges, it’s not straightforward, but it’s not impossible to tackle. And at the same time, you get something that you can use.”

This past summer, despite the pandemic, he completed an internship at Apple, working remotely from his home in Cambridge rather than on site in California. Of the ways his working style has changed due to COVID-19, he says, “There are definitely challenges. In the beginning, it was hard to really just stay at

home and work. For people who are trying to do the theory, say you are dealing with a math problem for like two days, and if you’re in the office, you can talk with your office mate, talk with your friend. But at home you’re just yourself and yourself and yourself talking through the problem. But over the past few months, I have learned more how to push myself when I’m stuck and at home.” Since conferences have mainly been moved online, it’s still possible to attend some of them, although as Alireza says, it makes networking harder. But overall, the MIT community has pulled together to make this unusual situation work until they can be together in person again — something Alireza is very much looking forward to.

What do you do at LIDS?

I have been the Financial Coordinator at LIDS since February 2019. My role is to assist with the coordination and administration of financial activities, especially in the area of research. I serve on a three-member team that provides pre- and post-award grant administration service. I am responsible for devel-

Sound Bites: Yalan Yang

oping an integrated system and setting up processes for full pre- to post-award management. This includes everything from budget development and proposal coordination to oversight and reconciliation of financial expenses and balances, among many other things. (It’s a complex process!) Another important aspect of my job is serving as a resource regarding MIT financial policies and procedures, such as credit card, B2P invoices, travel reports, and employee expense reimbursements. I also maintain graduate students’ appointments and specific fellowships; prepare general financial reports and perform additional tasks as needed.

What do you like about financial work?

Financial work requires attention to detail. It also requires analytical skill, sound judgment and effective communication skill. At MIT, financial work often involves multi-tasking, organization and

teamwork. Timely and accurate financial work adds values to PIs. I enjoy using these skills and being helpful to the lab.

What brought you to the Boston area?

I have an undergraduate degree in Accounting and Finance, and a graduate certificate on Social Marketing and Social Change in Public Health Education and Promotion. Before coming to Boston, I worked on Coca-Cola editing team for 2 years and worked for 13 years under the Controller’s Office at University of South Florida. My aging parents live in Boston and this is one of the major reasons I came to Boston. I always aspire to bigger challenges in my professional career, too, and I am so excited to become a member of MIT, the most prestigious educational institution in the world and a great organization to work for.

How do you like Boston and being at MIT?

I like Boston very much because of its rich history, culture and many great universities. MIT is not only top institution for science & technologies, but also

a leading organization for diversity. I love living here. What do you do outside of work?

I spend time together with my parents and take them to the places they like to go. I love hiking and traveling, learning about different cultures. I enjoy trying different food. I also love cooking do a lot of good cooking at home — my specialty dishes are Hot Pot, Kung Pao Chicken and Ma Po Tofu.

What are

some interesting

facts you’d like to share about yourself?

I went to the boarding school when I was 11 years old, where I learned team work and how to independently solve problems as needed. I was on my middle school and high school gymnastic team. I also joined a line dance club for two more years. I love singing as well.

LIDS GRADUATION CELEBRATION

In honor of our 2020 graduates, LIDS hosted a virtual commencement reception, which was held as part of a larger IDSS celebration. Although not the same as our usual in-person festivities, the online version offered some unique advantages, most notably that friends and family from around the world, who might not otherwise have been able to attend, were able to join. For the LIDS portion in particular, the format change also provided the opportunity for each graduating student’s advisor speak about their advisee, a special and memorable addition to the event.

Our congratulations to the amazing class of 2020!

PhDs received by:

Wenhan Dai

Michael Fleder

Julia Gaudio

Ana Jevtic

Xia Miao

Hajir Roozbehani

Tuhin Sarkar

Ian Schneider

Omer Tanovic

Martin Zubeldia

SMs or MEngs received by:

Raj Agrawal SM

Rares Buhai MEng

Winter Guerra MEng

Arjun Gupta MEng

Theia Henderson MEng

Peter Li SM

Jing Lin MEng

Samuel Miller SM

Edward Nguyen Meng

Mira Anita Partha MEng

Tianyi Peng SM

Sebastian Quilter MEng

Maryann Rui SM

Basil Saeed MEng

Soumya Sudhakar SM

Andrew Y Tsai MEng

Zehao Yu SM

LIDS Awards & Honors

cant contributions to building a sense of community in the Department of Aeronautics and Astronautics.

Student Enric Boix was named to the 2020 cohort of Siebel Scholars in recognition of his academic achievements, leadership, and commitment to addressing crucial global challenges.

A Lehigh University team, led by LIDS Visiting Scholar Alberto Lamadrid (Lehigh University) and with LIDS co-PIs Principal Research Scientist Audun Botterud, Senior Research Scientist Marija Ilic, and Prof. Patrick Jaillet, was awarded $2.5 million by the U.S. Department of Energy’s ARPA-E.

Prof. Guy Bresler received an NSF CAREER Award.

Team CoSTAR, co-led by Prof. Luca Carlone and including LIDS student Yun Chang, won second place in the DARPA Subterranean Challenge’s Tunnel Circuit (August 2019) and first place in the Challenge’s Urban Circuit (February 2020). In addition, Prof. Carlone, together with LIDS postdoc Vasileios Tzoumas, and students Heng Yang and Pasquale Antonante, won the Best Paper Award in the Robotic Vision category at the 2020 International Conference on Robotics and Automation.

Prof. Luca Carlone received a 2020 Robotics: Science and Systems (RSS) Early Career Award. He also received the 2020 Vickie Kerrebrock Faculty Award recognizing faculty who have made signifiAwards

Prof. Jon How with LIDS student Stewart Jamieson and their collaborator won the Best Paper Award in the Service Robotics category at the 2020 International Conference on Robotics and Automation.

Senior Research Scientist Marija Ilic received the 2020 IEEE Power & Energy Society (PES) Outstanding Power Engineering Educator Award “for contributions to mentorship and education on modeling and control in power engineering.”

Prof. Patrick Jaillet and collaborators were finalists in the 2019 INFORMS Data Mining Competition. Prof. Jaillet also received the 2020 Burgess (1952) and Elizabeth Jamieson Prize for Excellence in Teaching from the Department of Electrical Engineering and Computer Science.

Student Peter Li, who is jointly advised by Professors Sertac Karaman (LIDS) and Vivienne Sze (RLE and MTL), was a 2020 Grand Finals winner of ACM’s Student Research Competition.

Students Samuel Miller, Zhenyu Liu, Zehao Yu, and George Denove, working with project advisors Prof. Moe Win, Dr. Christopher C. Yu (Draper Laboratory), and Prof. Andrea Conti (University of Ferrara), won first prize in the 2019 IEEE Communications Society’s Communication Technology Changing the World Student Competition.

Prof. Eytan Modiano was recognized by MIT’s Office of Graduate Education with a Committed to Caring award, given to faculty for providing excep -

tional mentoring, advising, and support to graduate students. Prof. Modiano also won the 2020 IEEE INFOCOM Achievement Award for his pioneering contributions to the analysis and design of cross-layer resource allocation algorithms for wireless, optical, and satellite networks.

Prof. Pablo Parrilo, together with LIDS alums Hamza Fawzi (University of Cambridge) and James Saunderson (Monash University), received the 2020 SIAM Activity Group on Optimization (SIAG/OPT) Best Paper Prize. Prof. Parrilo also received the 2020 Burgess (1952) and Elizabeth Jamieson Prize for Excellence in Teaching from the Department of Electrical Engineering and Computer Science.

Prof. Yury Polyanskiy received the IEEE Information Theory Society’s 2020 James L. Massey Award.

Prof. Devavrat Shah, along along with LIDS alum Tauhid Zaman (Yale University), received the prestigious ACM Sigmetrics Test of Time Paper Award for 2020. The award “recognizes an influential performance evaluation paper whose impact is still felt 10-12 years after its initial publication.” Notably, he was recipient of the same award in 2019.

Student Chandler Squires received the 2020 David Adler Memorial Thesis Award for his MEng thesis entitled “Causal Structure Discovery from Incomplete Data.” He is supervised by Prof. Caroline Uhler.

Postdoc Cesar Uribe and collaborator won Yahoo Research’s 2019 Faculty and Research Engagement Program (FREP) Award for their proposal, “Acceleration for Data Science and Machine Learning.”

Prof. Moe Win, together with students Wenhan Dai and Tianyi Peng, received the IEEE ICNC Best Paper Award at the International Conference on Computing, Networking and Communication, 2020. Prof. Win and colleagues also received the IEEE Communications Society Best Tutorial Paper Award.

Honors

Prof. G. David Forney was named an Eminent Member of IEEE by IEEE-HKN in their 2019 selection process.

Senior Research Scientist Marija Ilic was elected as a Member of the Mathematical Institute of the Serbian Academy of Sciences and Arts (MISANU).

Dr. Ilic was also elected an Ordinary Member of Academia Europaea.

Prof. Ali Jadbabaie was named head of the Department of Civil and Environmental Engineering.

Prof. Caroline Uhler was promoted to Associate Professor with Tenure by the Department of Electrical Engineering and Computer Science.

Prof. Moe Win was elected a Fellow of the European Association for Signal Processing (EURASIP) for “contributions to the theory and practice of cooperative localization.”

Prof. Cathy Wu was named the Gilbert W. Winslow Career Development Assistant Professor by the Department of Civil and Environmental Engineering.

LIDS Seminars 2019-2020

Weekly seminars are a highlight of the LIDS experience. Each talk, which features a visiting or internal invited speaker, provides the LIDS community an unparalleled opportunity to meet with and learn from scholars at the forefront of their fields.

Listed in order of appearance.

Marija

Ilic

MIT

Laboratory for Information and Decision Systems & Lincoln Laboratory

Aarti Singh

Carnegie Mellon University

Machine Learning Department

Alejandro Dominguez-Garcia

University of Illinois at Urbana-Champaign

Department of Electrical and Computer Engineering

George Pappas

University of Pennsylvania

Department of Electrical and Systems Engineering

Roy Yates

Rutgers University

Electrical and Computer Engineering Department

Sujay Sanghavi

University of Texas at Austin Department of Electrical and Computer Engineering

Rayadurgam Srikant

University of Illinois at Urbana-Champaign

Electrical and Computer Engineering Department

Mengdi Wang

Princeton University

Department of Electrical Engineering

Muriel Médard

MIT

Department of Electrical Engineering and Computer Science

Aleksander Mądry

MIT

Department of Electrical Engineering and Computer Science

P.R. Kumar

Texas A&M University

Electrical & Computer Engineering Department

Alexander Olshevsky

Boston University

Electrical and Computer Engineering Department

2020 LIDS Student Conference

The annual LIDS Student Conference is a student-organized, student-run event that provides an opportunity for graduate students to present their research to peers and the community at large. The conference also features a set of distinguished plenary speakers. The 2020 Student Conference marks 25 years of this signature lab event.

Committee Members

Janak Agrawal

Abdullah Alomar

Nadya Balabanska

Josef Biberstein

Moise Blanchard

Sarah Cen

Alireza Fallah

Xinzhe (Roger) Fu

Victor Gonzalez

Arjun Gupta

Vindula Jayawardana

Bai Liu

Oscar Mickelin

David Miculescu

Ajinkya Nene

Michail Ouroutzoglou

Mira Anita Partha

Sarath Pattathil

Joshua Robinson

Chandler Squires

Austin Stromme

Ezra Tal

Jerrod Wigmore

Karren Dai Yang

Chenyang Yuan

Xiyu Zhai

Student Speakers

Anish Agarwal

Matthew Brennan

Sitan Chen

Ryan Cory-Wright

Shuvomoy Das Gupta

Arthur Delarue

Marwa El Halabi

Alireza Fallah

Yuzhou Gu

Sagar Indurkhya

Igor Kadota

Cihan Emre Kement

Dongchan Lee

Michael Li

Jason Cheuk Nam Liang

Agni Orfanoudaki

Jean-Baptiste Seby

Rahul Singh

Soumya Sudhakar

Rajat Talak

Manxi Wu

Yunzong Xu

Chulhee (Charlie) Yun

Jinglong Zhao

Chulhee (Charlie) Yun

Jinglong Zhao lou

Dheeraj M. Nagaraj

Student Conference Chairs

Jason Liang

Emily Meigs

Tianyi Peng

Amir Tohidi

Vishrant Tripathi

Panelists

Prof. Ali Jadbabaie