ASSISTANT PROFESSOR IN ARTIFICIAL INTELLIGENCE (AI)
EDUCATION
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, 2012
RESEARCH AREAS
• Urban science
• Deep learning
• Choice modeling
• Urban mobility
• Network analysis
BIO
Shenhao Wang is an assistant professor and the director of the Urban AI Laboratory at the University of Florida. As an urban and computer scientist, he develops novel AI approaches to focus on three research themes: (1) resilient and equitable urban systems, (2) travel behavior, and (3) design and planning automation with generative AI.
The first theme treats cities as an interrelated system. By integrating network theory and deep learning, it quantifies the spatiotemporal dynamics between people and places, thus facilitating the design of resilient and equitable cities. The second theme focuses on the individual decisions by integrating discrete choice models and deep learning with wide urban applications to travel modes, residential locations, and urban activities. The third theme focuses on automating the process of urban design, planning, and engineering with generative AI, such as generating land use, mobility, and building footprint patterns. His research has been funded by Department of Energy (DOE), Singapore-MIT Alliance for Research and Technology (SMART), and industrial partners. Dr. Wang completed his interdisciplinary Ph.D. in Computer and Urban Science at Massachusetts Institute of Technology in 2020. He received a Bachelor of Economics from Peking University (2014) and Bachelor of Architecture from Tsinghua University (2012), as well as a Master of Science in Transportation, and Master of City Planning from MIT (2017).
PUBLICATIONS
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.
