Civil Engineering Departmental Seminar - On the Value Functions in Reinforcement Learning for Ridesharing

4:30pm - 5:30pm
Room 3598 (Lift 27/28)

On the Value Functions in Reinforcement Learning for Ridesharing

With the quickly growing volume and variety of transportation data, machine learning (ML) and artificial intelligence (AI) technologies present a great potential to arouse improved scientific understandings, transformative informed decisions, and innovative and proactive management solutions for future transportation systems. Meanwhile, transportation systems are complex and dynamic physical systems. They are often associated with very complicated and unique operation problems featuring nonconvexity, nonlinearity, high dimensionality, and prohibitive computational difficulties. The power of AI technology can only be fully unlocked when we well customize it with transportation domain knowledge. This presentation shares several of our research studies, which integrate ML/AI approaches with traffic flow analysis, optimization, game theories, and control technologies to address challenging issues in real-time traffic prediction, event detection, large-scale traffic management and control, involving emerging transportation technologies such as shared mobility and connected and autonomous vehicles.

講者/ 表演者:
Dr. Zhiwei Qin
Lyft , California

Dr. Tony Qin is Principal Scientist at Lyft, working on core problems in ridesharing marketplace optimization. Previously, he was Principal Research Scientist and Director of the Decision Intelligence group at DiDi AI Labs and Staff Scientist in supply chain and inventory optimization at Walmart Global E-commerce. Tony received his Ph.D. in Operations Research from Columbia University. His research interests span optimization and machine learning, with a particular focus in reinforcement learning and its applications in operational optimization, digital marketing, and smart transportation. He is Associate Editor of the ACM Journal on Autonomous Transportation Systems. He has published more than 40 papers in top-tier conferences and journals in machine learning and optimization. He has served as Area Chair/Senior PC of KDD, AAAI, and ECML-PKDD, and a referee of top journals.  He is an INFORMS Franz Edelman Award Finalist and Laureate in 2023, received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019 and was selected for the NeurIPS 2018 Best Demo Awards.  Tony holds more than 10 US patents in intelligent transportation, supply chain, and recommendation systems.