I study intelligent infrastructure: advancing artificial intelligence (AI) for next-generation infrastructure systems. My long-term goal is to contribute methodology that bolsters society’s capacity to make forward-looking decisions. I am deeply curious about the role of computation–particularly reinforcement learning, deep learning, and stochastic modeling–for designing future cities, multi-agent systems, and other complex dynamical systems. My research is motivated by three overlapping questions: How should we integrate AI and automation into society? How can we scalably analyze large collections of dynamical systems? And, motivated by climate change and pervasive costs of congestion, how soon can we leverage AI and automation for more sustainable cities?

I am the Gilbert W. Winslow (1937) Career Development Assistant Professor at MIT, where I am part of the Laboratory for Information & Decision Systems (LIDS), the Department of Civil and Environmental Engineering (CEE), and the Institute for Data, Systems, & Society (IDSS).

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

Recent news | Teaching | Group | Publications

Recent news

For my TEDxMIT talk, see How Cities Can Reshape Cars.
For a recent research talk, see Mixed Autonomy Traffic: A Reinforcement Learning Perspective (Simons Institute). Also, see: Research overview.

  • December 2021: I had a fun interview with the MIT Schwarzman College of Computing on our new project with Amazon, where we’ll be tackling algorithmic challenges of coordinating hundreds of robots — with the ultimate goal of safely integrating automation and AI into our world.
  • December 2021: Our NeurIPS 2021 Spotlight Talk on machine learning for speeding up vehicle routing was featured by MIT News! We devise a strategy which accelerates the best algorithmic solvers by 10-100x for large sets of cities.
  • December 2021: I am honored to be part of this year’s TEDxMIT, speaking about how cars are becoming smart enough to meet the needs of cities, rather than the other way around.
  • September 2021: Congratulations to Sirui Li and Zee Yan for our work on learning for large-scale vehicle routing, accepted at NeurIPS 2021 for a Spotlight Talk (<3%).
  • May 2021: My work on deep reinforcement learning for analyzing systems of mixed traffic of autonomous and human drivers has been accepted for publication in the IEEE Transactions on Robotics.
  • February 2021: Excited to be profiled by MIT: Examining the world through signals and systems.
  • April 2020: I had a fun conversation with Sam Charrington on the TWIML AI Podcast about Simulating the Future of Traffic with RL.
  • January 2020: I was immensely honored to testify on the future of AI in transportation to the National Academy’s Transportation Research Board (TRB) at the Executive Committee Policy Session on Artificial Intelligence in Transportation.
  • October 2019: I am honored to be awarded the First Place 2019 IEEE ITSS Best Ph.D. Dissertation Award, for my thesis titled “Learning and Optimization for Mixed Autonomy Systems – A Mobility Context.” More here.
  • July 2019: I joined MIT as an assistant professor.
  • March 2019: I am honored to be inducted into the Microsoft Location Summit Hall of Fame, for receiving the First Place Award for my recent talk on “Integrating Autonomy into Urban Systems.”
  • December 2018: I am honored to receive the 2018 Milton Pikarsky Memorial Award from the Council of University Transportation Centers (CUTC) for my PhD dissertation. More here.
  • (For more, see: earlier news.)


Teaching

Courses
6.231 Dynamic Programming and Reinforcement Learning. Spring 2022.
6.246 Reinforcement Learning. Spring 2021, Spring 2020.
1.041/1.200 Transportation. Fall 2021, Fall 2020, Fall 2019.
EE290O Multi-agent reinforcement learning for autonomous traffic. Fall 2018.
CS189 Machine Learning. Content Development TA. Fall 2017.
CS170 Algorithms. Head TA. Spring 2016.

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

Group

PhD students
Zhongxia “Zee” Yan
Vindula Jayawardana
Sirui Li
Jiaqi “Vicky” Zhang (co-advised with Caroline Uhler)

Masters students
Cameron Hickert
Arpan Kaphle

UROPs
Anna Landler
Grace L. Chang
Ammar Fayad

Articles in review / preparation

Unified Automatic Control of Vehicular Systems with Reinforcement Learning
Zhongxia Yan, Abdul Rahman Kreidieh, Eugene Vinitsky, Alexandre M. Bayen, and Cathy Wu
In review.

Reinforcement Learning for Eco-Lagrangian Control at Intersections
Vindula Jayawardana, Cathy Wu
In review.

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
Cameron Hickert, Sirui Li, and Cathy Wu
In review.
( pre-print )

Scalability of Safe Supervision of Autonomous Vehicles in Mixed Traffic
Cameron Hickert and Cathy Wu
In review.

Path Clearance for Emergency Vehicles Under Mixed Autonomy: An Opportunity for Low Market Penetration CAVs
Dajiang Suo, Vindula Jayawardana, Cathy Wu
In review.

Towards Proxy Metrics for the Broader Impacts of Autonomous Vehicles
Thomas Krendl Gilbert, Michael Dennis, Rowan McAllister, Cathy Wu

User-friendly Interpretations in Reinforcement Learning
Vindula Jayawardana, Cameron Hickert, Tsui-Wei Weng, Sijia Liu, Lam M. Nguyen, Cathy Wu

Publications

Learning to Delegate for Large-scale Vehicle Routing
Sirui Li*, Zhongxia Yan*, Cathy Wu
NeurIPS, 2021. Spotlight (<3%).
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
NeurIPS 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 )

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

Mixed Autonomous Supervision in Traffic Signal Control
Vindula Jayawardana, Anna Landler, 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 Bayen
IEEE Transactions on Robotics (T-RO), 2021.
( paper / journal / videos / website / github )
Press: Simulating the Future of Traffic with RL w/ Cathy WuTWIML AI Podcast
Press: The Future of Mixed-Autonomy Traffic with Alexandre BayenTWIML AI Podcast
Also Science, Wired, O’Reilly (Chinese version), Berkeley College of Engineering, abc News, Berkeley Lab, India Times, and Russian Forbes

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

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%).
Deep Reinforcement Learning Symposium (NIPS), 2017. Contributed talk.
( paper / OpenReview )

Stabilizing traffic with autonomous vehicles
Cathy Wu, Alexandre 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 Rahman Kreidieh, Cathy Wu, Alexandre 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 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, and 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 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 Kreidieh, Alexandre 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 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 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 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 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 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*, and Cathy Wu*
Berkeley Science Review, 2015.
( article )

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