LIDS ALL
Laboratory for Information and Decision Systems|MIT
An
Interdisciplinary Approach to Fighting Climate Change
Volume #18
Editor: Jennifer Donovan
Design & Illustration: Michael Lewy Wing Ngan
Writers: Grace Chua
Raleigh McElvery
Greta Friar
Photography credits:
Photos of Sathwik Chadaga, Christina Fucarino, Audun Botterud, Mohammad Khojasteh, Tamara Broderick, and Navid Azizan provided courtesy of the interviewees. Photo of Rose Faghih taken by Jeff Lautenberger, Cullen College of Engineering University of Houston.
Massachusetts Institute of Technology
Laboratory for Information and Decision Systems
77 Massachusetts Avenue, Room 32-D608
Cambridge, Massachusetts 02139 lids.mit.edu
send questions or comments to lids-comm@mit.edu
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.
LIDS is truly an interdisciplinary lab, home to over 150 graduate students and post-doctoral associates from EECS, Aero-Astro, and many other departments across MIT. 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.
A Message from the Director
I am very happy to introduce this year’s issue of LIDS|All. The 2021-22 academic year marked my first full year as LIDS director. I was privileged to see our community thrive as we welcomed new faculty and students, advanced research in critically important domains such as autonomy and climate, and continued to play a major role in shaping education and research as part of the Schwarzman College of Computing.
This year also marked a return to campus for many of us as the Covid-19 pandemic, and our range of tools for navigating it, evolved. It was wonderful to see classrooms and lecture halls start to fill again, as well as the care our community took in keeping each other healthy and safe during this transition. While fall and early spring were very much a hybrid experience (you will see that certain key LIDS events remained virtual) it was a heartening step. By late spring we were able to shift further towards being in person. It was a special thrill to have the LIDS graduation celebration on campus, and to include classes from the previous two years whose celebrations were virtual.
You will see pictures of this very welcome return to in-person celebrations in this issue of the magazine. You will also read about Principal Research Scientist Audun Botterud’s interdisciplinary approach to decarbonizing the electric grid; assistant to the LIDS director Christina Fucarino; postdoc Mohammad Khojasteh and his work in quantum physics; graduate student Sathwik Chadaga and his research on communications networks; as well as alum Rose Faghih and her award-winning work developing portable, wearable technology that monitors brain states.
I hope you enjoy this issue of the magazine and reading about the exceptional people in our community.
Sincerely,
Sertac Karaman, Director
An Interdisciplinary Approach to Fighting Climate Change
By Greta Friar
In early 2021, the United States government set an ambitious goal: to decarbonize its power grid, the system that generates and transmits electricity throughout the country, by 2035. It’s an important goal in the fight against climate change, and will require a switch from current, greenhouse-gas producing energy sources (such as coal and natural gas), to predominantly renewable ones (such as wind and solar).
Getting the power grid to zero carbon will be a challenging undertaking, as Audun Botterud, a principal research scientist at LIDS who has long been interested in the problem, knows well. It will require building lots of renewable energy generators and new infrastructure; designing better technology to capture, store, and carry electricity; creating the right regulatory and economic incentives; and more. Decarbonizing the grid also presents many computational challenges, which is where Audun’s focus lies. Audun has modeled different aspects of the grid, the mechanics of energy supply and storage, and electricity markets—where economic factors can have a huge effect on how quickly renewable solutions get adopted.
A major challenge of decarbonization is that the grid must be designed and operated to reliably meet demand. Using renewable energy sources complicates this, as wind and solar power depend on an infamously volatile system: the weather. A sunny day becomes grey and blustery, and wind turbines get a boost but solar farms go idle. This will make the grid’s energy supply variable and
unpredictable. Additional resources, including batteries and backup power generators, will need to be incorporated to regulate supply. Extreme weather events, which are becoming more common with climate change, can further strain both supply and demand. Managing a renewablesdriven grid will require algorithms that can minimize uncertainty in the face of constant, sometimes random fluctuations to make better predictions of supply and demand, guide how resources are added to the grid, and inform how those resources are committed and dispatched across the entire United States.
“The problem of managing supply and demand in the grid has to happen every second throughout the year, and given how much we rely on electricity in society, we need to get this right,” Audun says. “You cannot let the reliability drop as you increase the amount of renewables, especially because I think that will lead to resistance towards adopting renewables.”
That is why Audun feels fortunate to be working on the decarbonization problem at LIDS—even though a career here is not something he had originally planned. Audun’s first experience with MIT came during his time as a graduate student in his home country of Norway, when he spent a year as a visiting student with what is now called the MIT Energy Initiative. He might never have returned, except that while at MIT, Audun met his future wife, Bilge Yildiz. The pair both ended up working at the Argonne National Laboratory outside of Chicago, with Audun focusing on
challenges related to power systems and electricity markets. Then Bilge got a faculty position at MIT, where she is a professor of nuclear and materials science and engineering. Audun moved back to the Cambridge area with her and continued to work for Argonne remotely, but he also kept an eye on local opportunities. Eventually, a position at LIDS became available, and Audun took it, while maintaining his connections to Argonne.
“At first glance, it may not be an obvious fit,” Audun says. “My work is very focused on a specific application, power system challenges, and LIDS tends to be more focused on fundamental methods to use across many different application areas. However, being at LIDS, my lab (Energy Analytics Group) has access to the most recent advances in these fundamental methods, and we can apply them to power and energy problems. Other people at LIDS are working on energy too, so there is growing momentum to address these important problems.”
Much of Audun’s research involves optimization, using mathematical programming to compare alternatives and find the best solution. Common computational challenges include dealing with large geographical areas that contain regions with different weather, different types and quantities of renewable energy available, and different infrastructure and consumer needs—such as the entire United States. Another challenge is the need for granular time resolution, sometimes even to the sub-second, to account for changes in energy supply and demand.
Often, Audun’s group will use decomposition to solve such large problems piecemeal and then stitch together solutions. However, it’s also important to consider systems as a whole. For example, in a recent paper, Audun’s lab looked at the effect of building new transmission lines as part of national decarbonization. They modeled solutions assuming coordination at the state, regional, or national level, and found that the more
“The problem of managing supply and demand in the grid has to happen every second throughout the year, and given how much we rely on electricity in society, we need to get this right.”
regions coordinate to distribute electricity, the less they will need to spend to reach zero carbon.
In other projects, Audun uses game theory approaches to study strategic interactions in electricity markets. For example, he has designed agent-based models to analyze electricity markets. These assume each actor will make strategic decisions in their own best interest and then simulate interactions between them. Interested parties can use the models to see what would happen under different conditions and market rules, which may lead companies to make different investment decisions, or governing bodies to issue different regulations and incentives. These choices can shape how quickly the grid gets decarbonized.
Audun is also collaborating with researchers in MIT’s chemical engineering department who are working on improving battery storage. Batteries will help manage variable renewable energy supply by capturing surplus energy during periods of high generation to release during periods of insufficient generation. Audun’s group models the sort of charge cycles that batteries are likely to experience in the power grid, so that the engineers can test their batteries’ abilities in more realistic scenarios. In turn, this also leads to a more realistic representation of batteries in power system optimization models.
These are only some of the problems that Audun works on. He enjoys the challenge of tackling a variety of different projects, collaborating with everyone from engineers to architects to economists. He also believes that such collaboration leads to better solutions. The problems created by climate change are myriad and complex, and solving them will require researchers to cooperate and explore.
“In order to have a real impact on interdisciplinary problems like energy and climate,” Audun says, “you need to get outside of your research sweet spot and broaden your approach.”
Translating Numbers into Networks
By Raleigh McElvery
Math is the strongest of all languages. That’s what Sathwik Chadaga’s undergraduate research advisor told him when he was first learning to code, and today it’s a maxim that underpins his graduate research at LIDS. As Sathwik explains: “Math can be used to explain almost anything. No one can argue with a theory if you prove it using math.” A member of Eytan Modiano’s lab, Sathwik is using mathematical theorems to improve the algorithms that predict widespread outages across power grids and communication networks.
Growing up in the hills of southwestern India, Sathwik enjoyed hands-on, applied projects from a young age. He did most of his learning through play, designing miniature catapults, building DC motors from scratch, and dissecting remote control cars to repurpose their components.
He went on to earn bachelor’s and master’s degrees in electrical engineering from the Indian Institute of Technology, Madras (IIT Madras) in just five years. During that time, he joined a lab at the forefront of communications and networking research, run by IIT Madras professor R. David Koilpillai.
Sathwik’s master’s project, which was in collaboration with UC San Diego professor Nambi Seshadri, was specifically aimed at improving data rates. For example, when you log onto a Zoom meeting, the video from your computer’s camera must be converted from digital to analog signals that are wirelessly transmitted to a server, before being directed to your colleagues’ devices. But there’s a limit (called the Nyquist Limit) to the speed at which bits can be converted into
continuous signals without causing interference between neighboring bits. This interference can degrade the quality of the communication signal, ultimately reducing the video quality. Sathwik was working to develop algorithms that could send data faster than the Nyquist Limit without trading speed for quality.
In the summers when he wasn’t at IIT Madras, Sathwik completed research internships in both academia and industry—including Texas Instruments (TI), who went on to hire him after he graduated. He served as a systems engineer there for a year, helping to design chips for antennas.
Although he enjoyed his time at TI, this experience ultimately reinforced his desire to return to academia and earn a PhD. “I wanted to continue to push the boundaries of knowledge, and a PhD seemed like the best way to do that,” he says.
The Modiano lab, he explains, hit just the right “sweet spot” between theory and application. “We model the real world using mathematical tools to solve long-standing problems in communications and networking,” he says. “The close-knit LIDS community also makes it easy to form research collaborations that quickly take our projects out of the theoretical and into the applied realm.”
Sathwik is only in his second year of graduate school and must complete (another) master’s thesis before moving on to his PhD project. He’s still selecting a focus for the latter, although it will almost certainly involve devising new algorithms in the area of communication networks.
“The close-knit LIDS community also makes it easy to form research collaborations that quickly take our projects out of the theoretical and into the applied realm.”
In the meantime, he’s taking a hiatus from communications networks for his master’s project to focus on another type of network: power grids. The US operates from just three power grids that provide electricity to consumers nation-wide. However, even a seemingly insignificant event like a branch falling onto a power line could cause electrical failures across an entire city by overloading the remaining wires in the system.
Sathwik’s job is to analyze these outage patterns and determine which power lines are most likely to fail and therefore require extra reinforcement. Traditional models designed to make these predictions were built from scratch and based on the laws of physics — rather than real data — making them slow and computationally-expensive. Data from real-life power outages are hard to come by, but the Modiano lab has found a way to make synthetic data with the help of simulators, which they then use to train their models. Sathwik
is currently applying his machine learning skills to tweak an existing influence model created by his fellow LIDS collaborators Xinyu Wu and Dan Wu, in order to help it run even faster.
Designing efficient models is imperative, Sathwik says, especially as we come to rely more on renewable energy sources. Solar generators and wind turbines, for example, are at the mercy of ever-changing weather conditions; their capacity to produce power varies based on cloud cover, turbulence, and many other factors. Sathwik’s alterations will help the Modiano lab’s predictive model adjust rapidly to these transient conditions — and perhaps even forecast outages across other types of networks in addition to power networks, such as communications networks or social networks.
Outside of doing research, Sathwik is an active member of the LIDS and MIT communities. He
serves as the IT chair of his graduate dorm committee as well as a member of Sangam, the Association of Indian Students at MIT. He’s also on the LIDS Social Committee, helping to organize weekly coffee hours and larger events including ice skating outings and movie nights. In addition, he helps to organize the weekly LIDS & Stats Tea Talks, which provide MIT students with the chance to share their research in an informal setting and receive audience feedback.
“The Tea Talks are just another way in which LIDS encourages collaboration,” Sathwik says. “Although my main research focus is communications and networking, LIDS has really helped me expand my skills in another realm of
communications: science communications. That is, explaining my work to others outside my field. After all, being able to share your ideas with others is a critical part of the scientific process.”
Sathwik looks forward to continuing to build and share his ideas with colleagues at LIDS and beyond in his time at MIT.
The Body as a Dynamical System
By Grace Chua
LIDS research on systems ranges widely: from governing fleets of autonomous vehicles, to monitoring the state of electrical power grids.
But what about the human body? Where does the human body, with its heartbeats and peristalsis, its hormone cascades and feedback loops, and its myriad emotional states, fit into the universe of LIDS work and applications?
Alumna Rose Faghih would say that the body is a dynamical system. It changes over time, and various aspects of the body, such as the electrical impulses of the central nervous system, can be represented mathematically. Even the body’s chemical processes, such as the way adrenaline affects and regulates bodily functions, can be represented this way.
Rose, now an associate professor of biomedical engineering at New York University’s Tandon School of Engineering, applies that understanding to complex biomedical challenges. Currently, she uses system theory to design algorithms that use nearly-imperceptible changes in a person’s skin conductance to tell a story about what is happening inside the brain.
Skin conductance is affected by sweat, which is an important indicator of stress or excitement: imagine speaking in front of a large audience, or standing at the edge of a cliff looking out over a scenic vista. Small electrodes attached to a smartwatch can monitor these changes, and Rose’s algorithms can correlate them to different brain states in real time.
Rose calls this the MINDWATCH, or ‘Multimodal Intelligent Noninvasive brain state Decoder for Wearable AdapTive Closed-loop arcHitectures’. Ultimately, the technology will serve as a way to not only monitor a wearer’s mental state, but to also respond automatically with nudges that improve a person’s performance and quality of life. For example, in a stressful situation, a smartwatch could play relaxing music or prompt its wearer to do some deep breathing exercises.
For her work, Rose was named to Technology Review’s Innovators Under 35 (TR35) list in 2020. The same year, she received a National Science Foundation CAREER Award, and the IEEE Women in Engineering magazine named her a ‘Woman to Watch’.
Applying her work to the human body was not part of Rose’s original plan. Growing up as the daughter of economics and engineering professors in the historic city of Shiraz, in Iran, Rose loved mathematics puzzles, robotics, and coding. She attended a specialized high school for Development of Exceptional Talents focusing on math and physics and dreamed of becoming an engineering professor, but health sciences were not her cup of tea at the time. “My last biology class in high school involved a lot of memorization,” she quipped.
Rose attended the University of Maryland for electrical engineering. But during an undergraduate research experience one summer, she observed how her project—designing better sensors to detect breast cancer—could make a real impact on people’s lives. Keen to pursue research
at the intersection of electrical engineering and medical applications, she came to LIDS to work with Professors Munther Dahleh and Emery Brown. While at LIDS, her PhD research worked out how to trace levels of blood cortisol, a key stress hormone, back over time to the brain hypothalamic events that caused the cortisol release.
One muscle she developed at MIT was collaboration. Few LIDS researchers work with human subjects, so at LIDS and then during a post-doctoral stint at MIT, Rose collaborated extensively with surgeons, physicians and other healthcare and biomedical professionals at Massachusetts General Hospital and elsewhere to gain the skills, knowledge, and access that her research needs.
Rose’s advisors also encouraged and empowered her to be an independent thinker —something that has stood her in good stead when setting up her own lab. And of course, LIDS’ signature emphasis on fundamentals has left its mark on how she works. “It’s colored the way I look at problems. I try to come up with the simplest solution to a complex problem,” Faghih said.
After five years as an assistant professor at the University of Houston, Rose moved back to the East Coast in January 2022 to be closer to family and has been based in New York City since. In her spare time, she explores the city, walking along the Hudson River and venturing out to different neighborhoods; she also writes poetry (and has published two collections!) and creates digital art.
“Can we go beyond the typical 15-minute doctor visits to get to the autonomous healthcare system of the future?”
Today, what keeps her busy is expanding the MINDWATCH work to measure biochemical signals rapidly and in real time. For example, hormones such as melatonin and cortisol are typically measured in the lab from a blood sample, and even that produces only a ‘snapshot’ of hormone levels at a single time point. If we can monitor such biochemical signals over time and combine them with an understanding of electrical signals and human physiology, perhaps we can tell a clearer story of what is happening inside our brains – and even change the script.
Eventually, Rose envisions real-time, autonomous and non-invasive monitoring of physical and mental health that catches diseases and disorders at early stages, by tracking people’s health states in the real-world settings where they live and work. This could make healthcare much more accessible in rural and underserved settings where people may not be able to travel regularly for repeat
doctor visits. “Can we go beyond the typical 15-minute doctor visits to get to the autonomous healthcare system of the future?” Rose ponders. It’s a vision that could make healthcare better for us all.
Between Two Universes
By Raleigh McElvery
When Mohammad Javad Khojasteh arrived at LIDS in 2020 to begin his postdoc appointment, he was introduced to an entirely new universe. The domain he knew best could be explained by “classical” physics that predicts the behavior of ordinary objects with near-perfect accuracy (think Newton’s three laws of motion). But this new universe was governed by bizarre laws that can produce unpredictable results while operating at scales typically smaller than an atom.
“The rules of quantum mechanics are counterintuitive and seem very strange when you first start to learn them,” Mohammad says. “But the more you know, the clearer it becomes that the underlying logic is extremely elegant.”
As a member of Professor Moe Win’s lab, called the Wireless Information and Network Sciences Laboratory, or WINS Lab, Mohammad’s job is to straddle both the classical and quantum realms, in order to improve state-of-the-art communication, sensing, and computational capabilities.
Growing up in Iran, Mohammad knew he wanted to be a scientist from an early age. In high school, he became captivated by physics in particular. He was a first-generation college graduate, earning a dual bachelor’s degree in electrical engineering
and math from Sharif University of Technology, before completing his PhD in electrical and computer engineering at University of California, San Diego (UCSD). There, he worked at the intersection of robotics and machine learning, developing tools to protect against cyber threats as well as learning-enabled planning algorithms for autonomous robots to operate safely in changing real-world scenarios. After graduating from UCSD in 2019, he remembers calling home to share the good news: “Mom, I’m officially a doctor now.”
After a stint at the California Institute of Technology (Caltech), where Mohammad collaborated with NASA researchers to develop planning and control algorithms to improve off-road autonomous driving and build robots for life detection missions on other planets, Mohammad moved across the country to Cambridge, Massachusetts to join LIDS and the WINS Lab.
“LIDS has always been at the center of the decision making and information science field,” he says. “As an undergraduate and later as a PhD student, I remember reading papers and textbooks by LIDS professors, so getting the chance to collaborate with these renowned researchers during my postdoc has been really exciting. LIDS is such an interesting and vibrant environment.”
“But the more you know, the clearer it becomes that the underlying logic is extremely elegant.”
Until this point, Mohammad had focused primarily on classical systems such as autonomous vehicles, although he’d always maintained a keen interest in quantum systems. In the WINS Lab, he could finally focus on both pursuits in tandem.
There’s a quantum revolution on the horizon, he explains, that will transform the way devices perform sensing, computation, and communications tasks. Problems that take classical computers years to solve will be child’s play for the large-scale quantum computers slated to come online in the next few decades. For example, these new-wave quantum computers will allow biologists and chemists to better simulate molecular interactions to design new drugs — and even help engineers to design better batteries. These machines will also leverage the laws of quantum physics to advance medical research and clinical care.
In Mohammad’s words: “This quantum revolution will change lives and help us to better understand the world around us.”
Because he was still so new to the field of quantum mechanics when he arrived in the WINS Lab, Mohammad began by reading and discussing related papers with his lab mates to get up to speed. In the meantime, he started working on a project related to classical systems, helping robots to navigate while keeping their locations secret to thwart potential security breaches.
As Mohammad began to master the rules of the quantum universe, he took on a second project that has since become his main endeavor, aimed at developing data-driven techniques to control the basic units of information that feed quantum computers.
While classical computers store information as electrical pulses that represent 1s and 0s called “bits,” quantum computers use quantum bits or “qubits,” which might be typically subatomic particles. Based on their unique quantum mechanical properties, qubits can represent additional values besides just 0 or 1: They can also represent both 0 and 1 at the same time in different weights (a phenomenon known as superposition that can lead to computational advantages). However, because the dynamics of quantum systems are so difficult to predict, controlling the state of these qubits is no easy feat. While traditional approaches rely on manually designed models, Mohammad’s method utilizes a hierarchical design that layers exploratory control, quantum tomography, Hamiltonian learning, and data-driven control techniques to more precisely tune the dynamics of these qubits, allowing quantum computers to operate more efficiently.
“I’ve learned so much from Professor Win,” Mohammad says. “There are very few research groups with a foot in both classical and quantum physics, so working in his lab has been an amazing opportunity.”
With roughly a year left in his postdoc appointment, Mohammad has begun considering his next career steps. He plans to apply to research scientist jobs in industry, as well as faculty positions. Becoming a professor would allow him to continue teaching, which he’s enjoyed immensely during his time at LIDS. In addition to serving as a teaching assistant, he has also volunteered for MIT’s Summer Research Program (MSRP), which empowers students from historically underrepresented groups in science to become researchers. Mohammad mentored one
MSRP student for over a year, and the two even co-authored a study together.
Whether he pursues a career in academia or industry, Mohammad aims to continue performing fundamental research in quantum systems. His interdisciplinary background in physics, mathematics, engineering, robotics, machine learning, and quantum mechanics has endowed him with a multifaceted perspective, which he applies to every research problem he encounters.
“I’m somebody who enjoys crossing the imaginary boundaries between the fields and trying different methods to address a research question,” he says. “Everyone at LIDS also really values this interdisciplinary approach, which gives them a broad vision to conduct really interesting research and solve important problems.”
Sound Bites: Christina Fucarino
What do you do at LIDS?
I’m the assistant to the LIDS director, and I do a wide spectrum of things here at the lab. I support the director by coordinating his calendar and meetings. I work on space placements for incoming students and visitors. I work with student committees and admin staff to help plan all of the major and minor lab events throughout the year — from teas and lunches to major conferences. I’m there to step into any role if it’s needed.
Where did you work before coming to LIDS?
I worked at my local YMCA in the Cultural Arts Department. That position was somewhat similar to my current position at LIDS, but in addition to supporting the director I worked primarily with the children’s offerings — coordinating and helping to run children’s theater classes. I’d say I’m using the same kinds of skills just in a very different environment.
What brought you to LIDS and MIT?
How has your experience been so far?
I moved to Massachusetts early in 2022. What brought me to MIT and LIDS specifically is that I enjoyed my time in the not-for-profit area, and I thought that higher education would have a similar kind of vibe. I’m happy that my assumption was right! In my time here I’ve found that the nonprofit mindset runs through higher education as well — especially by having a huge sense of community. There’s definitely that at MIT, and I think LIDS more specifically. Since LIDS is a smaller lab, you don’t feel like you’re in this massive institution, more like you’re in a community. At LIDS in particular there is a strong community and I really like that I get to see people I know (or am getting to know!) every single day.
What kind of things do you like to do when you’re not at work?
I love attending live theater and supporting the performing arts. Coming from New York—I lived close to the city—I frequented Broadway growing up and I continue to enjoy this. Here in Boston I’ve been to Shakespeare in the Park, which was lovely, and this summer we’ll be going to a special screening of Star Wars accompanied by the Boston Symphony Orchestra. I also really enjoy going to the ballet. I’ve never been to the opera, but it’s on my list of things I’d like to do.
Are there any fun facts about yourself you’d like to share?
I’m an avid baker. I think during the pandemic many people found baking to be a comforting activity during a time of uncertainty, and I was definitely one of those people. I hadn’t really baked at this level before the pandemic. My dad is a professional chef so there was always that in me, to enjoy making food, but of course I had to go my own way. I’ve found baking really fun. My favorite challenge was to take a bunch of different chocolate chip cookie recipes and make them all to see which we thought was best. The winner was one of Bon Apetit’s recipes. I think what made it the top choice was that they include brown butter, which changed my life as a baker.
LIDS GRADUATION CELEBRATION
Congratulations to the amazing class of 2022!
PhDs received by:
Anish Agarwal
Paolo Bertolotti
Bomin Jiang
Eren Kizildag
Suhas Kowshik
Dongchan Lee
Zhenyu Liu
Emily Meigs
Dheeraj Nagaraj
Hanzhang Qin
Micah Smith
Igor Spasojevic
Will Stephenson
Ezra Tal
Jennifer Tang
Brian Trippe
Nuri Denizcan Vanli
Manxi Wu
Chulhee (Charlie) Yun
Jingzhao Zhang
SMs or MEngs received by:
Sarah Alnegheimish SM
Abdullah Alomar SM
Pourya Habib Zadeh SM
Fernando Herrera Arias MEng
Brice Huang SM
Nathan Hughes SM
Zeyu Jia SM
Arpan Kaphle MEng
John Keszler SM
Haochuan Li SM
Yi-Lun Liao SM
Kerri Lu MEng
Mubarik Mohamoud MEng
Saba Nejad SM
Quang Nguyen SM
Amir Nouripour SM
Nassim Oufattole MEng
Premila Rowles SM
George Stefanakis MEng
William Wang SM
Jerrod Wigmore SM
Zhuofan Xie MEng
Annie Yun MEng
Xinyi Zhang SM
LIDS Awards & Honors
Awards
Professor Saurabh Amin received the Samuel M. Seegal Prize from the MIT School of Engineering for inspiring students in pursuing and achieving excellence. Professor Amin also received the 2022 Earll M. Murman Award for Excellence in Undergraduate Advising from MIT.
Student Juncal Arbelaiz, supervised by Professor Ali Jadbabaie, won a Schmidt Science Fellowship. Arbelaiz was also selected as a finalist for the Young Author Prize at the IFAC Conference on Networked Systems.
Doctor Audun Botterud received the Impact Argonne Award for organizing the workshop “Informing Storage Solutions to Decarbonize Electricity,” convened by Argonne National Laboratory.
Professor Guy Bresler and collaborators received Best Paper Runner Up at the Conference on Learning Theory 2021.
Professor Luca Carlone received the American Institute of Aeronautics and Astronautics MIT chapter’s Excellence in Undergraduate Advising Award, 2022.
Staff member Rachel Cohen received an MIT Excellence Award—Outstanding Contributor. Excellence Awards are among the highest honors given to MIT staff.
Student Yuzhou Gu won the Best Student Paper Award at the 2021 Conference on Learning Theory. The paper was co-authored with his supervisor, LIDS professor Yury Polyanskiy, and collaborators.
At the IEEE/RSJ International Conference on Intelligent Robots and Systems 2021, students Nathan Hughes and Samuel Ubellacker, together with Professor Luca Carlone and collaborator received the Best Student Paper Award as well as the Best Paper Award on Mobile Manipulation finalist distinction.
Student Sagar Indurkhya won the Best Paper Award at the 20th IEEE Conference on Cognitive Informatics and Cognitive Computing, which was co-authored with his advisor, Professor Robert Berwick.
Student Héctor Javier Vázquez Martinez (supervised by Professor Robert Berwick) received the Harold Hazen Teaching Award from the Department of EECS in 2021. Martinez also received a Charles & Jennifer Johnson MEng Thesis Award from the Department of EECS in 2022.
Professor Etyan Modiano received the American Institute of Aeronautics and Astronautics MIT chapter’s Excellence in Undergraduate Teaching Award, 2022.
Student Gregory M. Pailet (supervised by Professor Robert Berwick) received the J. Francis Reintjes Excellence in 6A Industrial Practice Award from the Department of EECS.
Student Dennis Shen, supervised by Professor Devavrat Shah, received the 2021 INFORMS
George B. Dantzig Dissertation Award, Honorable Mention for his thesis.
Professor David Simchi-Levi and his collaborators received several awards including: second place in the INFORMS 2022 Innovative Applications in Analytics Award, the 2021 Operations Research Best Operation Management Paper Award, and a finalist paper for the 2022 Revenue Management and Pricing (RMP) Practice Award. Additionally, Professor SimchiLevi won the 2021 Roddy Martin Polaris Transform Award.
Student Soumya Sudhakar (supervised by Professor Sertac Karaman) was named a 2021-22 Accenture Fellow.
Professor Moe Win received the Everett Moore Baker Award for Excellence in Undergraduate Teaching from MIT.
Honors
Professor Guy Bresler was granted tenure by the Department of Electrical Engineering and Computer Science, effective July 1, 2022.
Professor Tamara Broderick was awarded membership in 2021 COPSS Leadership Academy.
Professor Etyan Modiano received the Richard Cockburn Maclaurin Professorship in Aeronautics and Astronautics, effective July 1, 2022.
Professor Asu Ozdaglar was elected an IFAC Fellow.
Professor Yury Polyanskiy was promoted to Full Professor, effective July 1, 2022.
Professor Devavrat Shah was named a Fellow of the IEEE.
Professor Caroline Uhler was promoted to Full Professor, effective July 1, 2022.
LIDS Community
The 2021-2022 academic year saw the LIDS community slowly returning to in-person activities as the Covid-19 pandemic evolved.
It was wonderful to start seeing friends and colleagues in person again, and the LIDS community navigated frequent changes in policies and parameters to prioritize health and safety while maintaining a vibrant and connected community through our signature activities.
Our students and postdocs continued to play a key role in organizing these activities. In addition to the LIDS Student Conference (which you can read about in a separate page) the different committees organized social events including weekly snacks and an ice skating event, as well as weekly LIDS & Stats Tea Talks, a popular series of informal research presentations.
Our thanks to all of the students, faculty, and staff who made these a success! We’d like to thank here, in particular, the student and postdoc organizers:
LIDS Social Committee
Sathwik Chadaga
Chirag Rao
Nick Jones
LIDS & Stats Tea Talks Committee
Tanay Wakhare
Jerrod Wigmore
Yunzong Xu
Sathwik Chadaga
LIDS Seminars
2021-2022
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.
Jitendra Malik University of California, Berkeley Department of Electrical Engineering and Computer Sciences
Rediet Abebe
University of California, Berkeley Department of Electrical Engineering and Computer Sciences
Nihar Shah
Carnegie Mellon University Machine Learning Department Computer Science Department
Max Welling University of Amsterdam Informatics Institute
Doina Precup McGill University School of Computer Science
Aaron Ames California Institute of Technology
Department of Mechanical and Civil Engineering
The annual LIDS Student Conference is a studentorganized, 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 2022 Student Conference marks 27 years of this signature lab event.
Student Speakers
Abdullah Alomar
Moise Blanchard
Julia Briden
Sarah Cen
Charles Dawson
Vassilis Digalakis Jr
Kevin Doherty
Raaz Dwivedi
Peter Fisher
Rabab Haider
Andy Haupt
Zeyu Jia
Nicholas Johnson
Eren Can Kizildag
Nitya Mani
Xiang Meng
Sung Min (Sam) Park
Manon Revel
Austin Iglesias Saragih
Will Stephenson
Prem Talwai
M. Taha Toghani
Vishrant Tripathi
Yunzong Xu
Runyu Zhang
Zijie Zhou
Feng Zhu
Plenary Speakers
Yuejie Chi
Carnegie Mellon University
Na Li
Harvard University
Angelia Nedich
Arizona State University
Adam Wierman
California Institute of Technology
Student Conference Chairs
Bai Liu
Chirag Rao
Maryann Rui
Soumya Sudhakar
Zijie Zhou
NEW LIDS FACULTY
Tamara Broderick
Tamara is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. In addition to being a member of LIDS, she is a member of the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Previously, she received an AB in Mathematics from Princeton University (2007), a Master of
Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in Physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning.
Navid Azizan
Navid joined MIT in Fall 2021, where he is the Esther & Harold E. Edgerton Assistant Professor. He holds dual appointments in the MIT Institute for Data, Systems, and Society (where he is also a core member of the Statistics and Data Science Center) and the Department of Mechanical Engineering (in Controls, Instrumentation, and Robotics). Prior to joining MIT, he obtained his PhD in Computing and Mathematical Sciences from the California Institute of Technology, co-advised by Babak Hassibi and Adam Wierman, in 2020. Following this, he completed a postdoc at Stanford. He was also a research scientist intern at Google DeepMind in 2019. His research interests broadly lie in machine learning, optimization, systems and control, and network science. His research group focuses on developing theoretical foundations and practical methodologies for enabling large-scale intelligent systems, with an emphasis on principled learning and optimization algorithms, with applications in autonomous systems and societal networks.