Dr. Xuan (Sharon) Di, Columbia University
Associate Professor, Civil Engineering and Engineering Mechanics
"AI for Urban Transportation Digital Twin"
Transportation digital twins have become increasingly popular tools to improve traffic efficiency and safety. However, the majority of effort nowadays is focused on the “eyes” of the digital twin, which is object detection using computer vision. I believe the key to empowering the intelligence of a transportation digital twin lies in its “brain,” namely, how to utilize the information extracted from various sensors to infer traffic dynamics evolution and devise optimal control and management strategies with real-time feedback to guide the transportation ecosystem toward a social optimum.
My research aims to employ tools including machine learning and game theory to develop an urban transportation digital twin, leveraging data collected from the NSF PAWR COSMOS city-scale wireless testbed being deployed in West Harlem next to the Columbia campus. In this talk, I will primarily focus on two solutions: (1) scientific machine learning that leverages both domain knowledge and available data, and (2) mean field game that bridges the gap between micro- and macroscopic behaviors of multi-agent dynamical systems. In the first topic, physics-informed deep learning will be introduced and applied to traffic state estimation and uncertainty quantification. In the second topic, I will introduce how to model behaviors of new actors (e.g., a large number of autonomous vehicles) in a transportation system and their interaction with existing actors (e.g., human-driven vehicles).
Dr. Xuan (Sharon) Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York and serves on a committee for the Smart Cities Center in the Data Science Institute. Dr. Di directs the DitecT (Data and innovative technology-driven Transportation) Lab. Her field of research is transportation systems engineering, with a focus on reshaping technological innovation for social good. Her research lies at the intersection of game theory and machine learning. She specializes in autonomous vehicle control in mixed traffic, cyber physical transportation systems, multi-modal mobility optimization, and transportation and health, using innovative tools including physics-informed deep learning and mean-field games. Details about DitecT Lab and her research can be found in the following link: http://sharondi-columbia.wixsite.com/ditectlab.
Dr. Di received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities. Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute (UMTRI). Dr. Di received a number of awards including NSF CAREER, International Data Corporation’s Smart Cities North America Awards (2023), Best Paper Award from ACM SIGKDD Workshop on Urban Computing (2022), Amazon AWS Machine Learning Research Award (2020), Transportation Data Analytics Contest Winner from Transportation Research Board (TRB) (2017), the Dafermos Best Paper Award Honorable Mention from the TRB Network Modeling Committee (2017), Chan Wui & Yunyin Rising Star Workshop Fellowship for Early Career Professionals from TRB, Outstanding Presentation Award from INFORMS (2016), the Best Paper Award (2014) and Best Graduate Student Scholarship (2013) from North-Central Section Institute of Transportation Engineers (ITE). She serves as the Associate Editor for the journals including Transportation Science, Transportation Research Part B, and IEEE on ITS, and won the Transportation Science Meritorious Service Award from INFORMS in 2022. She was a participant in a long program on Mathematical Challenges and Opportunities for Autonomous Vehicles at the Institute for Pure & Applied Mathematics (IPAM) in UCLA in 2020. She has been a visiting research fellow at Bielefeld University’s Center for Interdisciplinary Research (ZiF) in Germany since 2021.