AI-Empowered Spatiotemporal Mobility Intelligence
Build transferable foundation and generative models that fuse GPS, survey, semantic, and demographic signals to synthesize realistic activity-location behavior, even in data-limited settings.
Research Directions
Three connected themes organize my research: generating realistic mobility behavior from sparse data, operationalizing it in city-scale digital twins, and translating it into trustworthy policy and infrastructure decisions.
Urban Mobility Digital Twins
Develop interactive urban simulation systems that combine AI agents and transportation networks for scenario testing, intervention analysis, and real-world planning support at metropolitan scale.
Policy, Infrastructure, and Trustworthy Mobility AI
Translate mobility AI into decision-ready tools for electrification policy, disaster logistics, regulation-aware autonomous systems, and robust multimodal transportation intelligence.