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Wu Earns IEEE ITS Best Dissertation Award

Congratulations to recent alumnus Cathy Wu (’18) on receiving the IEEE Intelligent Transportation Systems (ITS) Best Dissertation Award, which is awarded annually for the best doctoral dissertation in any ITS area that is innovative and relevant to practice. Graduate students from academic transportation centers across the U.S. were nominated for the award.

This award was established to encourage doctoral research that combined theory and practice, makes in-depth technical contributions, or is interdisciplinary in nature having the potential to contribute to the ITSS and broaden the ITS  topic areas from either the methodological or application perspectives.

“This is a great honor,” says Wu. “I feel very fortunate to have been able to study optimization challenges at very different layers of our complex transportation systems in the context of self-driving vehicles, from congestion to routing to questions touching on planning and policy.”

This award recognizes the outstanding dissertation, “Learning and Optimization for Mixed Anatomy Systems – A Mobility Context,” Wu prepared as part of her Electrical Engineering and Computer Science studies at the University of California, Berkeley, under advisor Professor Alexandre Bayen.

“This a great honor for Cathy, and I am so excited to see her pioneering work and dedication receive the recognition of the US DOT through the UTC program,” says Bayen. “The acknowledgement of her depth and breadth of research into the world of mixed autonomy and deep-reinforcement learning applications to cloud-based microsimulations by this Federal program is thrilling.”

Wu’s dissertation research focused on mixed autonomy systems in mobility, which studies the complex integration of automation such as self-driving cars into existing urban systems problems. Wu considered how will the gradual introduction of self-driving cars change urban mobility, and developed machine learning and optimization techniques to address three key challenges: 1) quantifying behavior in such complex systems, 2) addressing inherent sensing limitations, and 3) mitigating negative effects of automation. Wu demonstrates through the development of new learning and optimization methods how small changes in vehicles, sensors, and infrastructure can be harnessed for significant impact on urban mobility, and sheds light into the future study of mixed autonomy systems.

While at UC Berkeley, Wu took full advantage of available resources on campus and the surrounding area. She 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), and also spent time at OpenAIMicrosoft Research, the Google X Self-Driving Car Team (now Waymo), DropboxFacebook, and several startups. She received a BS and MEng in EECS from MIT.

“Being at UC Berkeley, I felt I had access to top experts in every area I could dream of, from behavior modeling to robotics to public policy to every flavor of optimization. It was such a unique opportunity,” says Wu. “And being so close to the self-driving car efforts happening around the Bay Area, discussing and working with the companies that are implementing this technology immensely rich and grounding.”

Previously, Wu has been recognized with the Council of University Transportation Centers Milton Pikarsky Memorial Award. a NSF graduate fellowship, the Berkeley Chancellor's fellowship, the NDSEG fellowship, and the Dwight David Eisenhower graduate fellowship, and awarded the 2016 IEEE ITSC Best Paper Award and the 2017 ITS Outstanding Graduate Student Award. Her leadership has also led her to the 2017 IEEE Leaders Summit, the 2016 Rising Star Workshop, and multiple NSF early-career investigator workshops on cyber-physical systems.

She currently an associate professor at MIT in the Department of Civil and Environmental Engineering and the Institute for Data, Systems, and Society.

Another student of Bayen’s earned the award in 2011, alum Daniel Work, for his work on Real-time Estimation of Distributed Parameters Systems: Application to traffic monitoring. He is currently an associate professor at Vanderbilt University.

“I think it’s an amazing honor to see the continued honor of UC Berkeley work being recognized, and from a different perspective,” says Wu.

The award was presented at the IEEE ITS Conference award ceremony Oct. 28, 2019 in Auckland, New Zealand.