I’m an assistant professor at MIT LIDS, CEE, & IDSS. In short, we build AI to analyze AI. I am fascinated by how to proactively use technology to make the world a better place. With rapid advances in AI, it is crucial to understand how automated decision making will reshape society, and how we can proactively shape that reshaping. To systematically analyze the knobs of intervention, my group builds learning-enabled algorithms for control and combinatorial problems that cope with the complexity, diversity, and scale of societal systems—with the aim of uncovering insights into how, why, and when to leverage automation. My domain focus is sustainable cities—a prototypical societal system—where automation has the potential to enable streets that are not only more efficient, but also more environmentally friendly, healthier, and safer for everyone. My work draws from reinforcement learning, deep learning, control, optimization, multi-agent systems, and stochastic modeling.

I previously completed a postdoc with the Microsoft Research Reinforcement Learning group and my PhD in EECS at UC Berkeley. I received a BS and MEng in EECS at MIT. I have also spent time at OpenAI, Waymo, Dropbox, Facebook, and several startups.

Recent news | Teaching | Group | Publications

Recent news

For an introduction to opportunities to proactively shape technology that excite me, see The Hidden Cause of Traffic Jams—and How to Solve Them (NOVA, 2022) and How Cities Can Reshape Cars (TEDxMIT, 2021).

For a recent research talk that goes into the foundational methodologies we are developing, see Cities as Robots: Scalability, Operations, and Robustness from the RSS Workshop on Learning from Diverse, Offline Data in NYC (June 2022).


Teaching

Courses
6.7950: Reinforcement Learning. Fall 2022, Spring 2021, Spring 2020. Previously 6.246.
6.231 Dynamic Programming and Reinforcement Learning. Spring 2022.
1.041/1.200 Transportation. Spring 2023, Fall 2021, Fall 2020, Fall 2019.
EE290O Multi-agent reinforcement learning for autonomous traffic. Fall 2018.

Outreach and Professional Education
These courses are intended for industry professionals and not MIT students.
Reinforcement Learning: Summer 2022, Winter 2021
Advanced Reinforcement Learning: Summer 2022, Summer 2021

Group

I’m looking for postdoctoral researchers and PhD students!

Interested in a Masters/PhD? I’ll be considering students primarily via CEE, SES (IDSS), and EECS. I am also open to advising students with the right fit through MEng, Transportation, and TPP. If you are interested in working with me on impact-driven machine learning & control research for mobility, infrastructure, and sustainability, apply to any of these programs and list me in your application. A few special areas of interest include: deep (reinforcement) learning for combinatorial optimization, learning for control of multi-agent systems, and applied (reinforcement) learning for multi-agent mobility.

Interested in a postdoc? Please email me directly with [Postdoc starting 2023] in the title, and include a CV, short research statement, and 3 reference contacts. I’m particularly interested in hiring postdoctoral researchers with a strong background in machine learning, control, operations research, or public policy (!) who would like to expand their research to include high-impact applications in robotics warehouses, large-scale mobility systems, and/or wind farms (!).

I greatly value diversity and inclusion in STEM through teaching, mentorship, and outreach. We are a highly collaborative and welcoming lab with diverse backgrounds that come together to make the whole stronger than the sum of its parts. I strongly believe that diversity in perspective is necessary to solve the world’s most challenging problems.

PhD students
Zhongxia “Zee” Yan
Vindula Jayawardana
Sirui Li
Jiaqi “Vicky” Zhang (co-advised with Prof. Caroline Uhler)
Cameron Hickert
Wenbin Ouyang
Jung Hoon Cho
Mehul Damani (co-advised with Prof. Dylan Hadfield-Menell)

Masters students
Edgar Ramirez Sanchez
Jason Teno (LGO)
Ao Qu (co-advised with Prof. Jinhua Zhao)

UROPs & Interns
Catherine Tang
Kevin Liu
Sunera Avinash (University of Moratuwa)
Edouard Labarthe (École Polytechnique)
Anirudh Valiveru
Jiaxin He (Vanderbilt)
Baptiste Freydt (ETH Zurich)

Articles in review / preparation

A Statistical Framework for Autonomous Vehicle Safety Using Pre-crash Scenarios
Edouard Labarthe, Cathy Wu

Learning Surrogates for Diverse Emission Models
Edgar Sanchez*, Catherine Tang*, Vindula Jayawardana, Cathy Wu

Mitigating The Braess’s Paradox in A Closed System Using Reinforcement Learning
Dingyi Zhuang*, Yuzhu Huang*, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu
( pre-print )

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
Cameron Hickert, Sirui Li, Cathy Wu
IEEE Transactions on Robotics (T-RO), conditionally accepted.
( pre-print )

Generalization of Reinforcement Learning in Multi-robot Coordination
Zhongxia Yan, Cathy Wu

Model-free Learning of Multi-objective Corridor Clearance in Mixed Autonomy
Dajiang Suo*, Vindula Jayawardana*, Cathy Wu

Publications

What is a Typical Signalized Intersection in a City? A Pipeline for Intersection Data Imputation from OpenStreetMap
Ao Qu*, Anirudh Valiveru*, Catherine Tang, Vindula Jayawardana, Baptiste Freydt, Cathy Wu
Transportation Research Board (TRB) Annual Meeting, 2023.

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
Vindula Jayawardana, Catherine Tang, Sirui Li, Dajiang Suo, Cathy Wu
Advances in Neural Information Processing Systems (NeurIPS), 2022.
( paper / website )

The Braess Paradox in Dynamic Traffic
Dingyi Zhuang*, Yuzhu Huang*, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2022.
( pre-print )

Unified Automatic Control of Vehicular Systems with Reinforcement Learning
Zhongxia Yan, Abdul R. Kreidieh, Eugene Vinitsky, Alexandre M. Bayen, Cathy Wu
IEEE Transactions on Automation Science and Engineering (T-ASE), 2022.
Additionally selected for presentation at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
( paper / journal / website / github )
Interview: On the Future of Our RoadsThe Robot Brains Podcast

Learning Eco-Driving Strategies at Signalized Intersections
Vindula Jayawardana, Cathy Wu
European Control Conference (ECC), 2022.
Also, IEEE International Conference on Robotics and Automation (ICRA), 2022. Robotics for Climate Change Workshop. Invited flash talk.
( paper / website / poster )
Press: On the road to cleaner, greener, and faster drivingMIT News. Home page feature.

Reinforcement Learning for Empirical Supervision Scaling of Autonomous Vehicles
Cameron Hickert, Cathy Wu.
IEEE International Conference on Robotics and Automation (ICRA), 2022. Workshop Safe and Reliable Autonomy Under Uncertainty.

Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles
Thomas Krendl Gilbert, Aaron Snoswell, Michael Dennis, Rowan McAllister, Cathy Wu
IEEE International Conference on Robotics and Automation (ICRA), 2022. Fresh Perspectives on Autonomous Driving Workshop.
( paper )

Learning to Delegate for Large-scale Vehicle Routing
Sirui Li*, Zhongxia Yan*, Cathy Wu
Advances in Neural Information Processing Systems (NeurIPS), 2021. Spotlight (<3%).
Also, International Conference on Machine Learning (ICML), 2021. Workshop on Subset Selection in Machine Learning.
( paper / website / OpenReview / poster / github )
Press: Machine learning speeds up vehicle routingMIT News

Learning to Dissipate Traffic Jams with Piecewise Constant Control
Mayuri Sridhar, Cathy Wu
Conference on Neural Information Processing Systems (NeurIPS), 2021. Workshop on Tackling Climate Change with Machine Learning, 2021.
( paper / slides / talk / proceedings )

Piecewise Constant Policies for Human-Compatible Congestion Mitigation
Mayuri Sridhar, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2021.
( proceedings )

Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre M. Bayen
IEEE Transactions on Robotics (T-RO), 2021.
( paper / journal / videos / website / github )
Interview: Simulating the Future of Traffic with RL w/ Cathy WuTWIML AI Podcast
Interview: The Future of Mixed-Autonomy Traffic with Alexandre BayenTWIML AI Podcast
Press: Science, Wired, O’Reilly (Chinese version), Berkeley College of Engineering, abc News, Berkeley Lab, India Times, and Russian Forbes

Reinforcement Learning for Mixed Autonomy Intersections
Zhongxia Yan, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2021.
( paper / proceedings / github )

Mixed Autonomous Supervision in Traffic Signal Control
Vindula Jayawardana, Anna Landler, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2021.
( proceedings )

Block simplex signal recovery: a method comparison and an application to routing
Cathy Wu, Alexei Pozdnoukhov, Alexandre M. Bayen
IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2019.
( journal / github )

Variance Reduction for Policy Gradient Using Action-Dependent Factorized Baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel
International Conference on Learning Representations (ICLR), 2018. Oral (2%).
Also, Conference on Neural Information Processing Systems (NeurIPS), 2017. Deep Reinforcement Learning Symposium. Contributed talk.
( paper / OpenReview )

Stabilizing traffic with autonomous vehicles
Cathy Wu, Alexandre M. Bayen, Ankur Mehta
International Conference on Robotics and Automation (ICRA), 2018.
( proceedings / talk video )

Learning and Optimization for Mixed Autonomy Systems – A Mobility Context
Cathy Wu
Thesis. PhD, Electrical Engineering and Computer Sciences, UC Berkeley, 2018.
( thesis / summary )
Award: First Place 2019 IEEE ITSS Best Ph.D. Dissertation Award (news)
Award: 2018 Milton Pikarsky Memorial Award from the Council of University Transportation Centers (CUTC) for the best Doctoral dissertation in the field of science and technology in transportation studies (news).

Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion
Eugene Vinitsky, Kanaad Parvate, Abdul R. Kreidieh, Cathy Wu, Alexandre M. Bayen
IEEE Intelligent Transportation Systems Conference (ITSC), 2018.
( paper / proceedings )

Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning
Aboudy Kreidieh, Cathy Wu, Alexandre M. Bayen
IEEE Intelligent Transportation Systems Conference (ITSC), 2018.
( paper / proceedings )

Benchmarks for reinforcement learning in mixed-autonomy traffic
Eugene Vinitsky*, Aboudy Kreidieh*, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu, Fangyu Wu, Richard Liaw, Eric Liang, Alexandre M. Bayen.
Conference on Robot Learning (CoRL), 2018.
( paper / proceedings )
Press: Watch just a few self-driving cars stop traffic jamsScience

Emergent behaviors in mixed-autonomy traffic
Cathy Wu, Aboudy Kreidieh, Eugene Vinitsky, Alexandre M. Bayen
Conference on Robot Learning (CoRL), 2017.
( paper / proceedings )
Press: Autonomous Vehicles: The Answer to Our Growing Traffic WoesWired

Multi-lane Reduction: A Stochastic Single-lane Model for Lane Changing
Cathy Wu, Eugene Vinitsky, Abdul R. Kreidieh, Alexandre M. Bayen
IEEE Intelligent Transportation Systems Conference (ITSC), 2017.
( proceedings )

Framework for Control and Deep Reinforcement Learning in Traffic
Cathy Wu, Kanaad Parvate, Nishant Kheterpal, Leah Dickstein, Ankur Mehta, Eugene Vinitsky, Alexandre M. Bayen
IEEE Intelligent Transportation Systems Conference (ITSC), 2017.
( proceedings )

Clustering for Set Partitioning with a Case Study in Ridesharing
Cathy Wu, Ece Kamar, Eric Horvitz
IEEE Intelligent Transportation Systems Conference (ITSC), 2016. Best paper award.
( paper / proceedings )

Optimizing the diamond lane: A more tractable carpool problem and algorithms
Cathy Wu, K. Shankari, Ece Kamar, Randy Katz, David Culler, Christos Papadimitriou, Eric Horvitz, Alexandre M. Bayen
IEEE Intelligent Transportation Systems Conference (ITSC), 2016.
( paper / proceedings )

Cellpath: fusion of cellular and traffic sensor data for route flow estimation via convex optimization
Cathy Wu, Jerome Thai, Steve Yadlowsky, Alexei Pozdnoukhov, Alexandre M. Bayen
Transportation Research: Part C, 2015.
International Symposium on Transportation and Traffic Theory (ISTTT), 2015. Oral (14%).
( paper / journal / proceedings / github (system) / github (algorithm) )

Convex programming on the l1-ball and on the simplex via isotonic regression
Jerome Thai, Cathy Wu, Alexei Pozdnoukhov, Alexandre M. Bayen
Conference on Decision and Control (CDC), 2015.
( paper / proceedings )

Link Density Inference from Cellular Infrastructure
Steve Yadlowsky, Jerome Thai, Cathy Wu, Alexei Pozdnoukhov, Alexandre M. Bayen
Transportation Research Record (TRR), 2015.
Transportation Research Board (TRB) Annual Meeting, 2015.
( paper / journal / proceedings )


Selected other writing

It’s Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process.
Brent Hecht, Lauren Wilcox, Jeffrey P. Bigham, Johannes Schöning, Ehsan Hoque, Jason Ernst, Yonatan Bisk, Luigi De Russis, Lana Yarosh, Bushra Anjum, Danish Contractor, Cathy Wu
ACM Future of Computing Academy Blog, 2018.
( article )

Traffic Jammin’: Making automated transportation a reality
Cathy Wu
Berkeley Science Review, 2016.
( article )

Automating us: The entanglement of people and machines
Daniel Aranki*, Roel Dobbe*, Jaime F. Fisac*, Cathy Wu*
Berkeley Science Review, 2015.
( article )

How to Lead a Technical Reading Group
Cathy Wu, Oct 2012.
( paper )