I am interested in developing computational tools for reliable and complex decision-making in critical societal systems, such as our urban systems. This includes creating new data-enabled systems which help experts plan more intelligent and sustainable societal systems, scalable learning algorithms, and foundational work towards bridging theory and practice in machine learning.

I will be a postdoc with Microsoft Research AI, starting in November. I will join the MIT faculty in 2019, where I will be part of the Institute for Data, Systems, & Society (IDSS), the Department of Civil and Environmental Engineering (CEE), and the Laboratory for Information & Decision Systems (LIDS).

I recently completed my PhD in EECS at UC Berkeley, working at the intersection of machine learning, optimization, and mobility. My PhD research focused on mixed autonomy systems in mobility, which studies the complex integration of automation such as self-driving cars into existing urban systems. I was advised by Alexandre Bayen, and was part of the Berkeley AI Research Lab (BAIR), California Partners for Advanced Transportation Technology (PATH), the Berkeley Real-time Intelligent Secure Explainable Systems Lab (RISELab), and Berkeley DeepDrive (BDD). Before graduate school, 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, Microsoft Research, the Google X Self-Driving Car Team (now Waymo), Dropbox, Facebook, and several startups.

Journal publications

[5] Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre Bayen
IEEE Transactions on Robotics (T-RO). In review.
arXiv / videos / github

[4] 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 Transactions on Automation Science and Engineering (T-ASE). In review.

[3] 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). In review.

[2] Link Density Inference from Cellular Infrastructure
Steve Yadlowsky, Jerome Thai, Cathy Wu, Alexei Pozdnoukhov, Alexandre Bayen
Transportation Research Record (TRR), 2015.
journal / pdf

[1] 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.
journal / pdf / github (system) / github (algorithm)


Conference publications

[12] 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. To appear.

[11] Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion
IEEE Intelligent Transportation Systems Conference (ITSC), 2018. To appear.

[10] 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% acceptance).
Deep Reinforcement Learning Symposium (NIPS), 2017. Contributed talk.
arXiv / OpenReview

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

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

[7] 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.

[6] 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.

[5] 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. [pdf]

[4] 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. [pdf]

[3] 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. [pdf]

[2] 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
International Symposium on Transportation and Traffic Theory (ISTTT), 2015.
proceedings / pdf

[1] Link Density Inference from Cellular Infrastructure
Steve Yadlowsky, Jerome Thai, Cathy Wu, Alexei Pozdnoukhov, Alexandre Bayen
Transportation Research Board (TRB) Annual Meeting, 2015.
pdf


General audience articles

[3] 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]

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

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


In the press

[3] ITS Announced Award Winners. Berkeley ITS, Apr 2017 [press release]

[2] Cathy Wu Selected as Rising Star. Berkeley ITS, Sept 2016 [press release]

[1] AT&T will improve your morning commute. AT&T press release, Sept 2015 [press release]


Selected talks

Reinforcement Learning for Mixed Autonomy Traffic
O’Reilly Artificial Intelligence Conference. September 2018. Invited talk.

Mixed-Autonomy Mobility: Scalable Learning and Optimization
Microsoft Research AI, 2018.
BAIR/CPAR/BDD (Berkeley Artificial Intelligence Research, CITRIS People & Robots and Berkeley DeepDrive) Seminar, 2018.

Simulation and HPC for Mixed-Autonomy Mobility
International Supercomputing Conference (ISC), 2018. Distinguished talk.

Variance Reduction for Scalable Learning and Mixed-Autonomy Traffic
Google Brain, 2018. Invited talk.

Reinforcement Learning for Mixed-Autonomy Traffic
UC Berkeley RISElab Retreat, 2018. Invited talk.

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
OpenAI, 2017.

Flow: Computational Framework for Deep RL and Traffic Microsimulation
SidewalkLabs, Alphabet, 2017. Invited talk.

Reinforcement Learning for Mixed-Autonomy Traffic
IEEE General Meeting, UC Berkeley, 2017. Invited talk. [slides]

Reinforcement Learning for Mixed-Autonomy Traffic
NSF Foundations Of Resilient CybEr-physical Systems (FORCES), Annual Meeting, UC Berkeley, 2017.

Mixed-Autonomy Traffic
IEEE Leaders Summit, 2017. Invited talk. [slides]

Towards Energy Minimization of Mixed-Autonomy Traffic via Control Theory and Reinforcement Learning
California Partners for Advanced Transportation Technology (PATH), UC Berkeley, 2016.

Cellpath: State Estimation of Traffic Networks via Convex Optimization
Distributed Robotics Lab, CSAIL, MIT, 2016.
Resilient Infrastructure Networks Lab, CEE, MIT, 2016.

Randomization for Ridesharing
Uber, 2015.

Cellpath: State Estimation of Static Traffic Networks via Convex Optimization
Women in Computer Science & Electrical Engineering (WISCE), Berkeley-Stanford Meetup, 2015. Invited talk. [slides]

Convex optimization for traffic assignment
AT&T, 2014.
Verizon, 2014.


*equal contribution