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, energy, 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 recently completed a postdoc with the Microsoft Research AI 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

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.)


6.246 Reinforcement Learning. Spring 2021, Spring 2020.
1.041/1.200 Transportation. 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 (Registration Open)

Selected publications

For full list, see publications.

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

Flow: A Modular Learning Framework for Autonomy in Traffic
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre Bayen
IEEE Transactions on Robotics (T-RO). In review.
pdf / arXiv / videos / github / project page

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.

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

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

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

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 / pdf / github (system) / github (algorithm)

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.