Rapidly developing mobile, social, and sensor networks are accumulating massive volumes of geospatial and temporal data. Space-time modeling on these data is a fundamental problem in building decision support systems for various human and environmental applications. In the real world, such spatio-temporal data show high heterogeneity over space and non-stationarity over time, which makes the prediction task especially challenging. My research focuses on enhancing the robustness of space-time models utilizing adaptive AI techniques such as meta learning. Making robust predictions lays a foundation for not only inclusive spatial planning, but also emergency response to adverse events, including traffic accidents, COVID pandemic, and natural disasters. The robust space-time models will contribute to more inclusive and adaptive communities in a changing environment, which aligns well with the Sustainable Development Goals by the United Nations.

Dr. Zhaonan Wang is a postdoctoral researcher at the Department of Geography and GIScience, University of Illinois Urbana-Champaign (UIUC). He leads the Geospatial AI team at CyberGIS Center UIUC, and is also a member of the Geospatial AI team of NSF I-GUIDE. His research interests lie in the interdisciplinary area between AI and GIScience, with a focus on spatial networks and network dynamics. He did his PhD at the Center for Spatial Information Science, the University of Tokyo. His research studies have been not only published at the top-tier AI and data science conferences, but also in close collaboration with stakeholders, including Tokyo Fire Department, Toyota Motor Cooperation.