Events

Beyond When & Where: Paradigm Shift of Geospatial AI from Predictive, to Generative and Adaptive

Speaker: Zhaonan Wang

Location: 370 Jay Street

Date: Friday, November 7, 2025

Cities are intricate and ever-evolving networks. So are the acquired real-world data, involving multiple dimensions (e.g., space, time), of different natures (e.g., numerical, unstructured), and demonstrating various distributions given intrinsic (e.g., human, infrastructure) and extrinsic factors, like events. My overarching research goal is thereby to decipher the network dynamics in cities. To make sense of location data, there is an emerging thread known as Geospatial AI built upon recent advances in artificial intelligence. The existing paradigm of Geospatial AI mostly follows the canonical supervised learning in machine learning, i.e., about making predictions for a variable somewhere sometime. However, there is still a gap left between building a crystal ball to informing decision making, no matter for city residents or managers. There are more questions like - (1) how reliable the predictions are, especially at anomalous events (e.g., extreme weathers, traffic incidents); (2) what if there is a rain/accident happens, how the traffic would be like; (3) how individuals should plan trips that satisfy multiple dimensions of their needs - providing more actionable insights. In this talk, I’ll introduce my research with an aim to shape the field with deeper questions to support real-world decision making for cities.


About the Speaker
Dr. Zhaonan Wang is an Assistant Professor at NYU Shanghai and an associated faculty member with CUSP at NYU Tandon. He has an interdisciplinary background in geospatial, AI, and urban science, with an overarching research goal to understand network dynamics of cities and support decision making with intricate real-world data. His research works have been published on top-tier AI and data science venues, including AAAI, KDD, WWW, ICDE. Before joining NYU Shanghai, Zhaonan was a postdoctoral researcher at University of Illinois Urbana-Champaign and NSF I-GUIDE. He obtained his PhD in 2022 at the University of Tokyo, where he was awarded MEXT Scholar by Japanese Government, and received best resource paper runner-up at ACM CIKM 2021.