Prof. Yanhua Li received two Ph.D. degrees in computer science from University of Minnesota at Twin Cities in 2013, and in electrical engineering from Beijing University of Posts and Telecommunications, Beijing in China in 2009, respectively. He joined Department of Computer Science at Worcester Polytechnic Institute (WPI) as an assistant professor since fall 2015. His research interests are artificial intelligence and data science, with applications in smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization. Recently, Dr. Li focuses on developing data-driven approaches to inversely learn and influence the decision-making strategies of urban travelers, who take public transits, taxis, sharing bikes, etc. Dr. Li is a recipient of NSF CAREER and CRII Awards. (http://www.wpi.edu/~yli15/)
Title: Decision Analysis from Human-Generated Spatial-Temporal Data
Abstract: With the fast development of mobile sensing and information technology, large volumes of human-generated spatio-temporal data (HSTD) are increasingly collected, including taxi GPS trajectories, passenger trip data from automated fare collection (AFC) devices on buses and trains, and working traces from the emerging gig-economy services, such as food delivery (DoorDash, Postmates), and everyday tasks (TaskRabbit). Such HSTD capture unique decision-making strategies of the “data generators” (e.g., gig-workers, taxi drivers). Harnessing HSTD to characterize unique decision-making strategies of human agents has transformative potential in many applications, including promoting individual well-being of gig-workers, and improving service quality and revenue of transportation service providers. In this talk, I will introduce a spatial-temporal imitation learning framework for inversely learning and “imitating” the decision-making strategies of human agents from their HSTD, and present our recent works on analyzing taxi drivers’ passenger-seeking strategies and public transit travelers’ route choice strategies. Moreover, I will discuss key design challenges in spatial-temporal imitation learning, and outline various future applications in targeted training, incentive, and planning mechanisms that enhance the well-being of urban dwellers and society in terms of income level, travel and living convenience.