I’m an associate professor at MIT in LIDS, CEE, & IDSS. My group studies machine learning for control and optimization in mobility. Broadly, I am interested in AI for Engineering––developing innovative tools that empower engineers to navigate and manage the increasing complexity of modern systems.
I am the recipient of a NSF Career Award aimed at advancing learning for generalization in large-scale cyber-physical systems. My work is supported by NSF, Amazon Robotics, Mathworks, MIT Mobility Initiative, MIT Energy Initiative, US DOT, Microsoft Research, Cintra, and Symbotic research grants.
Service highlights: Board of Governors for IEEE ITSS; TRB ACP50 Standing Committee Member; Inaugural Chair of the REproducible Research In Transportation Engineering (RERITE) Working Group (see tutorial); Program Co-chair for RLC 2025; Associate Editor (or equivalent) for ICML, NeurIPS, ICRA, & TRC (starting Fall 2025).
Previously, I 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.
Research highlights
- Hybridizing machine learning and model-based methods for optimization. Pure ML unfortunately often fails to solve the hard optimization problems prevalent in mobility systems. Pure model-based methods are unfortunately not well suited for the growing complexity of our systems. We integrate model-free and model-based methods to achieve the best of both worlds. We consider combinatorial optimization and control problems. Representative works include learning-guided large neighborhood search, learning-guided separator selection for branch-and-cut, model-based transfer learning, and multi-residual task learning.
- Optimization for safe and sustainable mobility. Project Greenwave leverages reinforcement learning to inform transportation decarbonization by mitigating carbon intensity of urban driving. In the interest of near-term deployment, we study how real-time driving advisories can guide human drivers to achieve the same traffic-optimizing behavior of automated vehicles. We also analyze how to optimize traffic operations to proactively manage traffic safety.
- Multi-agent coordination. We devise methods that are versatile, robust, efficient, and suitable for up to 1000 agents. Applications include cooperative driving and warehouse automation.
News | Teaching | Group | Papers | Service
News
For a recent research talk, see my September 2024 Keynote on Learning-guided Optimization for Mobility at TRC-30, the 30th year Anniversary Conference for the Transportation Research: Part C Journal.
For a gentle introduction to how I think about advancing sociotechnical systems, see The Hidden Cause of Traffic Jams—and How to Solve Them (NOVA, 2022) and How Cities Can Reshape Cars (TEDxMIT, 2021).
- Nov 2024: Our NeurIPS 2024 article on Model-based Transfer Learning for Contextual Reinforcement Learning is featured by MIT News! Our new contextual RL method is up to 40x more sample-efficient by strategically selecting training tasks that generalize best.
- Sep 2024: We delivered a hands-on tutorial on Reproducibility in Transportation Research at ITSC 2024! Reproducibility is big challenge in the field, and this tutorial is a step forward.
- Sep 2024: I am honored to give a Keynote at TRC-30, the 30th year anniversary conference of the Transportation Research: Part C (TR-C) journal. I spoke on Learning-guided Optimization for Mobility, focusing on how AI can facilitate the design of next-generation mobility systems.
- Aug 2024: I had tremendous fun speaking on the Keynote Panel on Applications of Reinforcement Learning at the inaugural RLC 2024.
- Feb 2024: Our ICLR 2024 article on New AI model could streamline operations in a robotic warehouse is featured on the MIT front page! We introduce a new neural network architecture that captures complex relationships across time and space in the warehouse for hundreds of robot paths, and amortizes the computation for efficiency.
- Dec 2023: Our NeurIPS 2023 article on AI for accelerating problem-solving in complex scenarios is featured on the MIT front page! We introduce a machine learning algorithm to intelligently configure mixed-integer linear programming (MILP) separators, which accelerates both open source and commercial MILP solvers.
- Nov 2023: I shared some thoughts on Generative AI for Transportation (YouTube) at MIT’s Generative AI: Shaping the Future Symposium. (MIT News coverage)
- May 2023: Our T-RO 2023 article on increasing safety and reliability of autonomous vehicles was featured on MIT News! We analyzed how cooperative intelligence and ideas from air traffic control can drastically improve reliability of autonomous vehicles.
- Mar 2023: Thrilled to be awarded an NSF CAREER Award to study generalization for advancing large-scale cyber-physical systems.
(For more, see: earlier news.)
Teaching
Courses
6.7920 Reinforcement Learning. Fall 2024, Fall 2023, Fall 2022, Spring 2021, Spring 2020. (Previously 6.7950/6.246.)
1.041/1.200 Transportation. Spring 2024, Spring 2023, Fall 2021, Fall 2020, Fall 2019.
6.231 Dynamic Programming and Reinforcement Learning. Spring 2022.
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 2024, Summer 2023, Summer 2022, Winter 2021
Advanced Reinforcement Learning: Summer 2024, Summer 2023, Summer 2022, Summer 2021
Recent awards to lab members
Shreyaa Raghavan is selected for a 2024 DDETFP Graduate Fellowship.
Shreyaa Raghavan is selected as a 2024-2025 Accenture Fellow.
Cathy Wu receives 2023 NSF CAREER Award.
Sirui Li is selected as a 2023-2024 Amazon Robotics Fellow.
Jung-Hoon Cho receives the 2022-2026 Kwanjeong Educational Foundation (KEF) Scholarship for graduate studies.
Zee Yan is selected for a 2020 DDETFP Graduate Fellowship.
Group
If you are interested in working with us, please see how to apply here.
Research Scientists and Postdocs
Zhengbing He
Yining Ma
PhD students
Vindula Jayawardana
Sirui Li (next position: Research Software Engineer, Microsoft Research)
Cameron Hickert
Wenbin Ouyang
Jung Hoon Cho
Han Zheng
Shreyaa Raghavan
Edgar Ramirez Sanchez
Tsung-Han “Hank” Lin
Masters students
Ao Qu (co-advised with Prof. Jinhua Zhao)
Andrea Garcia
Natalie Huang
UROPs, Interns, and Visiting Students
Tianyue Zhou
Anirudh Chari
Annika Vivekananthan
Athena J Wang
Catherine H Tang
Jinhee Won
Ruth Lu
Stephen Andrews
Monica Chan
Basil Liu
PhD Alumni
Zhongxia “Zee” Yan (PhD ’24, next position: Member of Technical Staff, Anthropic)
Articles in review
Structure Detection for Contextual Reinforcement Learning
Tianyue Zhou*, Jung-Hoon Cho*, Cathy Wu
A Survey on Large Language Model-empowered Autonomous Driving
Yuxuan Zhu, Shiyi Wang, Wenqing Zhong, Nianchen Shen, Yunqi Li, Siqi Wang, Zhiheng Li, Cathy Wu, Li Li
( pre-print )
Formalizing Task Similarity for Zero-shot Generalization in Reinforcement Learning
Jung-Hoon Cho, Sirui Li, Siqi Du, Roy Dong, Cathy Wu
Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation
M. Umar B. Niazi, Jung-Hoon Cho, Munther A. Dahleh, Roy Dong, Cathy Wu
( pre-print )
Learning to Segment for Capacitated Vehicle Routing Problems
Wenbin Ouyang, Sirui Li, Yining Ma, Cathy Wu
Preliminary work accepted to the 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII) [33% acceptance]
Can Synthetic Data Support Effective Training for Learning-based Solvers on Real-world Routing?
Wenbin Ouyang, Sirui Li, Yining Ma, Cathy Wu
Learning to Prune: Fast Feasible Trip Generation for High-capacity Ridepooling
Youngseo Kim, Sirui Li, Hins Hu, Wenbin Ouyang, Samitha Samaranayake, Cathy Wu
Preliminary work accepted to the 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII) [33% acceptance]
Probability-Aware Parking Selection
Cameron Hickert, Sirui Li, Zhengbing He, Cathy Wu
The Nah Bandit: Modeling User Non-Compliance in Recommendation Systems
Tianyue Zhou, Jung-Hoon Cho, Cathy Wu
( pre-print )
NeuralMOVES: Extracting and Learning Surrogates for Diverse Vehicle Emission Models
Edgar Ramirez Sanchez, Catherine Tang, Yaosheng Xu, Nrithya Renganathan, Vindula Jayawardana, Cathy Wu
( pre-print )
Multi-agent Scheduling of Intersection Crossings for Cooperative Autonomous Driving
Zhongxia Yan, Han Zheng, Cathy Wu
Temporal Transfer Learning for Traffic Optimization with Coarse-Grained Advisory Autonomy
Jung-Hoon Cho, Sirui Li, Jeongyun Kim, Cathy Wu
( pre-print )
Selective journal & conference papers
Since joining MIT
Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale
Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Edgar Sanchez, Catherine Tang, Mark Taylor, Blaine Leonard, Cathy Wu
Transportation Research: Part C, 2025.
( pre-print / website )
Media: A simple driving trick could make a big dent in cars’ carbon emissions – NewScientist
Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Cathy Wu, Katherine Driggs-Campbell
IEEE Intelligent Transportation Systems Magazine (ITSM), 2025.
( pre-print )
Reinforcement Learning for Robust Advisories Under Driving Compliance Errors
Jeongyun Kim, Jung-Hoon Cho, Cathy Wu
IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2025.
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job Shop Scheduling
Sirui Li, Wenbin Ouyang, Yining Ma, and Cathy Wu
International Conference on Learning Representations (ICLR), 2025. [32% acceptance]
( OpenReview )
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, Cathy Wu
International Conference on Learning Representations (ICLR), 2025. [32% acceptance]
( OpenReview / documentation / github )
Towards Foundation Models for Mixed Integer Linear Programming
Sirui Li, Janardhan Kulkarni, Ishai Menache, Cathy Wu, Beibin Li
International Conference on Learning Representations (ICLR), 2025. [32% acceptance]
( OpenReview / paper )
Revisiting the Correlation between Simulated and Field-Observed Conflicts Using Large-Scale Traffic Reconstruction
Accident Analysis & Prevention (AAP), 2025.
Ao Qu, Cathy Wu
( journal )
Robust Reinforcement Learning Strategies with Evolving Curriculum for Efficient Bus Operations in Smart Cities
Yuhan Tang, Ao Qu, Xuan Jiang, Baichuan Mo, Shangqing Cao, Joseph Rodriguez, Haris N Koutsopoulos, Cathy Wu, Jinhua Zhao
Smart Cities, 2024.
( journal )
Model-Based Transfer Learning for Contextual Reinforcement Learning
Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, and Cathy Wu
Advances in Neural Information Processing Systems (NeurIPS), 2024. [25.8% acceptance]
( paper / website / github / OpenReview )
Media: MIT researchers develop an efficient way to train more reliable AI agents – MIT News. SciTech Daily. CO/AI. ScienceDaily. The Brighter Side of News.
Cooperative Advisory Residual Policies for Congestion Mitigation
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
ACM Journal on Autonomous Transportation Systems, 2024.
( paper )
The League of Robot Runners Competition: Goals, Designs, and Implementation
Shao-Hung Chan, Zhe Chen, Teng Guo, Han Zhang, Yue Zhang, Daniel Harabor, Sven Koenig, Cathy Wu, Jingjin Yu
International Conference on Automated Planning and Scheduling (ICAPS) Demonstration Track, 2024. [21.6% acceptance]
( OpenReview / website )
Hybrid System Stability Analysis of Multi-Lane Mixed-Autonomy Traffic
Sirui Li, Roy Dong, Cathy Wu
IEEE Transactions on Robotics (T-RO), 2024.
( paper )
Neural Neighborhood Search for Multi-agent Path Finding
Zhongxia Yan, Cathy Wu
International Conference on Learning Representations (ICLR), 2024. [31% acceptance]
( OpenReview / github )
Media: New AI model could streamline operations in a robotic warehouse – MIT News. Home page feature. Sourcing Journal, The Robot Report, SciTechDaily.
Multi-agent Path Finding for Cooperative Autonomous Driving
Zhongxia Yan, Han Zheng, Cathy Wu
International Conference on Robotics and Automation (ICRA), 2024.
( paper )
Generalizing Eco-Lagrangian Control via Multi-residual Task Learning
Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid, and Kentaro Oguchi
International Conference on Robotics and Automation (ICRA), 2024.
( paper / website / video / poster / slides )
Learning to Configure Separators in Branch-and-Cut
Sirui Li*, Wenbin Ouyang*, Max B. Paulus, Cathy Wu
Advances in Neural Information Processing Systems (NeurIPS), 2023. [26% acceptance]
( OpenReview / paper / website )
Media: AI accelerates problem-solving in complex scenarios – MIT News. Home page feature.
Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective
Dajiang Suo*, Vindula Jayawardana*, Cathy Wu
IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2023.
( paper / journal )
Integrated Analysis of Coarse-grained Control for Traffic Flow Stability
Sirui Li, Roy Dong, Cathy Wu
IEEE Transactions on Control of Network Systems (T-CNS), 2023.
( paper / journal )
Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
Cameron Hickert, Sirui Li, Cathy Wu
IEEE Transactions on Robotics (T-RO), 2023.
( paper / journal )
Media: Exploring new methods for increasing safety and reliability of autonomous vehicles – MIT News
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. [26% acceptance]
( paper / website / OpenReview / video / slides / poster )
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 Roads – The Robot Brains Podcast
Learning to Delegate for Large-scale Vehicle Routing
Sirui Li*, Zhongxia Yan*, Cathy Wu
Advances in Neural Information Processing Systems (NeurIPS), 2021. [26% acceptance] Spotlight (<3%).
Also presented at International Conference on Machine Learning (ICML), 2021. Workshop on Subset Selection in Machine Learning.
( paper / website / OpenReview / poster / github )
Media: Machine learning speeds up vehicle routing – MIT News, ACM TechNews
Before joining MIT
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 Wu – TWIML AI Podcast
Interview: The Future of Mixed-Autonomy Traffic with Alexandre Bayen – TWIML AI Podcast
Media: 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 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. [25% acceptance rate] Oral (2%).
Also presented at Conference on Neural Information Processing Systems (NeurIPS), 2017. Deep Reinforcement Learning Symposium. Contributed talk.
( paper / OpenReview )
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. [31% acceptance rate]
( paper / proceedings )
Media: Watch just a few self-driving cars stop traffic jams – Science
Emergent behaviors in mixed-autonomy traffic
Cathy Wu, Aboudy Kreidieh, Eugene Vinitsky, Alexandre M. Bayen
Conference on Robot Learning (CoRL), 2017. [29% acceptance rate]
( paper / proceedings )
Media: Autonomous Vehicles: The Answer to Our Growing Traffic Woes – Wired
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.
Additionally selected for presentation at the International Symposium on Transportation and Traffic Theory (ISTTT), 2015. [25% acceptance rate] Oral (14%).
( paper / journal / proceedings / github (system) / github (algorithm) )
More papers
Scalability of Platoon-based Coordination for Mixed Autonomy Intersections
Zhongxia Yan, Cathy Wu
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.
Multi-agent Path Finding for Mixed Autonomy Traffic Coordination
Han Zheng, Zhongxia Yan, Cathy Wu
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.
A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets
Cameron Hickert, Zhongxia Yan, and Cathy Wu
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.
( pre-print )
Expert with Clustering: Hierarchical Online Preference Learning Framework
Tianyue Zhou, Jung-Hoon Cho, Babak Rahimi Ardabili, Hamed Tabkhi, Cathy Wu
Conference on Learning for Dynamics and Control (L4DC), 2024.
( pre-print )
Incentive Design for Eco-driving in Urban Transportation Networks
M. Umar B. Niazi, Jung-Hoon Cho, Munther A. Dahleh, Roy Dong, Cathy Wu
European Control Conference (ECC), 2024.
( pre-print )
Multi-Behavior Learning For Socially Compatible Autonomous Driving
Sanjula Jayawardana, Vindula Jayawardana, Kaneeka Vidanage, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2023.
( proceedings )
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Autonomy
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
IEEE Intelligent Transportation Systems Conference (ITSC), 2023.
( paper / proceedings )
Stabilization Guarantees of Human-Compatible Control via Lyapunov Analysis
Sirui Li, Roy Dong, Cathy Wu
European Control Conference (ECC), 2023.
( proceedings )
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 Braess Paradox in Dynamic Traffic
Dingyi Zhuang*, Yuzhu Huang*, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2022.
( paper / proceedings )
Learning Eco-Driving Strategies at Signalized Intersections
Vindula Jayawardana, Cathy Wu
European Control Conference (ECC), 2022.
Also presented at IEEE International Conference on Robotics and Automation (ICRA), 2022. Robotics for Climate Change Workshop. Invited flash talk.
( paper / website / poster )
Media: On the road to cleaner, greener, and faster driving – MIT News. Home page feature.. TechCrunch. ScienceDaily. The Loh Down on Science Podcast.
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 / github )
Mixed Autonomous Supervision in Traffic Signal Control
Vindula Jayawardana, Anna Landler, Cathy Wu
IEEE Intelligent Transportation Systems Conference (ITSC), 2021.
( proceedings )
Stabilizing traffic with autonomous vehicles
Cathy Wu, Alexandre M. Bayen, Ankur Mehta
International Conference on Robotics and Automation (ICRA), 2018.
( proceedings / talk video )
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 )
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 honorable mention.
( 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 )
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.
Additionally selected for presentation at the Transportation Research Board (TRB) Annual Meeting, 2015.
( paper / journal / proceedings )
Theses
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).
Selected other writing
Reproducibility in transportation research: a hands-on tutorial
Cathy Wu, Bidisha Ghosh, Zuduo Zheng, and Irene Martínez
IEEE International Conference on Intelligent Transportation Systems (ITSC), 2024.
( website )
Steering Innovation for Autonomous Vehicles towards Societally Beneficial Outcomes
Thomas Krendl Gilbert, Cathy Wu, and Michael Dennis
Day One Project, Federation of American Scientists. June 2021.
( policy memo / summary )
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 )
Service
Board of Governors, IEEE ITSS, 2023-2026
Competition Chair, League of Robot Runners (LoRR), 2022-2025
Program Chair, RLC, 2025
Guest Editor, ETRR Special Issue on Reproducible Research in Transportation, 2025
Associate Editor, ICRA, 2025
Area Chair, NeurIPS, 2024
Organizing Committee, NAE CAFOE, 2024
Area Chair, ICML, 2024
Reviewer for JMLR, TR-C, T-ITS, NeurIPS, ICML, ITSC, ISTTT, CoRL, ICRA, CDC, Nature, Science Advances, etc.