Massachusetts Institute of Technology (MIT), Ph.D. Computer and Urban Science, 2020
Massachusetts Institute of Technology (MIT), M.S. Transportation and Master City Planning, 2017
Peking University, B.A. Economics, 2014
Tsinghua University, B.A. Architecture and Law, 2011


• Urban mobility
• Deep learning
• Choice modeling
• Network analysis


Shenhao Wang is an assistant professor and the director of the Urban Artificial Intelligence Laboratory at the University of Florida, and a research affiliate to Urban Mobility Lab and Media Lab at the Massachusetts Institute of Technology. He seeks to develop fundamental theory for urban science using artificial intelligence with three specific research themes. The first research theme is deep choice theory, which analyzes individual decision-making by integrating discrete choice models and deep learning with applications to individual behavioral analysis in urban mobility. His second research theme is to analyze the collective mobility networks by integrating classical network theory and graph neural networks to quantify risk and uncertainty, thus promoting resilient economic growth. His third research theme focuses on ethical urban AI, which seeks to enhance the transparency, accountability, and fairness of the urban machine intelligence for broad social impacts. His research has been funded by Department of Energy (DOE), Singapore-MIT Alliance for Research and Technology (SMART), and urban mobility companies. Dr. Wang completed his interdisciplinary Ph.D. in Computer and Urban Science at Massachusetts Institute of Technology in 2020. He received B.A. in Economics from Peking University (2014) and B.A. in architecture and law from Tsinghua University (2011), Master of Science in Transportation, and Master of City Planning from MIT (2017).


S. Wang, Q. Wang and J. Zhao*. “Deep neural networks for choice analysis: Extracting complete economic information for interpretation”, Transportation research part C: emerging technologies, 118: 102701
S. Wang and J. Zhao*. “Risk preference and adoption of autonomous vehicles.” Transportation Research Part A: Policy and Practice, 126, 215-229.
D. Zhuang, S. Wang*, H. Koutsopoulos, and J. Zhao, “Uncertainty quantification of sparse trip demand prediction with spatial-temporal graph neural networks”, (Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)
S. Cranenburgh*, S. Wang, A. Vij, F. Pereira, and J. Walker, “Choice modeling in an age of machine learning – discussion paper”, (Journal of Choice Modeling: 100340)
Y. Zheng, S. Wang*, and J. Zhao, “Equality of opportunity in travel demand prediction with deep neural networks and discrete choice models”, Transportation Research Part C: Emerging Technologies. 132: 103410.

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