My long-term goal is to develop algorithms which increase the capacity for society to make good decisions, amidst the complexities of the open world. I specially focus on studying the impact of autonomy, or advanced forms of automation, with the aim of informing its integration into society. I study several critical building blocks, including establishing an empirical science around societal decision making, scalable reinforcement learning for networked decision systems, and human-compatible machine learning. I draw inspiration from a myriad of decision contexts, such as mobility, climate change, economics, sustainability, and public policy.

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).

I completed a postdoc with the Microsoft Research Reinforcement Learning group. I completed my PhD in EECS at UC Berkeley, working at the intersection of machine learning, optimization, and mobility, and where I was advised by Alexandre Bayen. I received a BS and MEng in EECS from MIT, where I worked with Daniela Rus, Seth Teller, and Jim Glass. I have also spent time at OpenAI, Waymo, Dropbox, Facebook, and several startups.

Recent news | Teaching | Group | Publications

Recent news

For a recent research talk, see Mixed Autonomy Traffic: A Reinforcement Learning Perspective (Simons Institute). Also, see: Research overview.

  • 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.)


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. 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


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

Masters students
Cameron Hickert
Arpan Kaphle

Anna Landler
Grace L. Chang
Ammar Fayad

Articles in review / preparation

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.

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


Learning to Delegate for Large-scale Vehicle Routing
Sirui Li*, Zhongxia Yan*, Cathy Wu
NeurIPS, 2021. Spotlight (<3%).
ICML Workshop on Subset Selection in Machine Learning: From Theory to Applications, 2021.
arXiv / github

Learning to Dissipate Traffic Jams with Piecewise Constant Control
Mayuri Sridhar, Cathy Wu
NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2021.

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

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

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

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.
pdf / arXiv / videos / github / project page

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.

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.
arXiv / 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.
pdf / 3-page synopsis

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.
proceedings / pdf

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.
proceedings / pdf

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.
proceedings / pdf

Emergent behaviors in mixed-autonomy traffic
Cathy Wu, Aboudy Kreidieh, Eugene Vinitsky, Alexandre Bayen
Conference on Robot Learning (CoRL), 2017.
proceedings / pdf

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.

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.

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.
proceedings / pdf

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.
proceedings / pdf

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%).
journal / proceedings / pdf / 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.
proceedings / pdf

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.
journal / proceedings / pdf

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.
full article

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

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

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