LANS Seminar

Seminar Title: Harnessing Big Data and Game Theory for Emerging Transportation Modeling

Speaker: Dr. Xuan (Sharon) Di, Associate Professor, Department of Civil Engineering and Engineering Mechanics at Columbia University, On a committee for the Smart Cities Center in the Data Science Institute.

Date/Time: February 21, 2024/ 10:30 AM-11 :30 AM
Location: See Meeting URL on the cels-seminars website which will require an Argonne login.

Description: My research aims to employ innovative solutions to advance our understanding of the equilibrating and evolving states of the future transportation ecosystem, and the interventions needed to guide the ecosystem toward a social optimum. 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), leveraging the core concepts of game theory, control, and machine learning.

Bio: 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 transportation systems engineering and data science, spanning the areas of game theory, AI, and optimization.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 here.Dr. Di serves as the Associate Editor for 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 received a number of awards including 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), NSF CAREER (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 (2014), Outstanding Presentation Award from INFORMS (2013), the Best Paper Award (2014) and Best Graduate Student Scholarship (2013) from Institute of Transportation Engineers (ITE). 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, and a visiting professor at Ecole Polytechnique in 2023-2024. Di received a PhD from University of Minnesota, Twin Cities, and was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute (UMTRI).