Shenhao Wang is an assistant professor and the director of the Urban AI Laboratory at the University of Florida. He investigates three research themes in intelligent individual decisions, spatiotemporal urban dynamics, and computational urban justice. The first theme focuses on the individual decisions by integrating discrete choice models and deep learning with wide urban applications in the choice of travel modes, residential locations, and urban activities. The second 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 sustainable urban systems. The third research theme focuses on the normative aspect of urban science by enhancing transparency, accountability, and fairness of the urban machine intelligence to achieve broad social impacts. With the theoretical innovations and practical impacts, the lab seeks to create a more sustainable, intelligent, and equitable urban future with artificial intelligence. 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 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).
Research Areas
• Urban science
• Deep learning
• Choice modeling
• Urban mobility
• Network analysis
Links
- Urban AI Lab: http://urbanailab.com/
- Google Scholar: https://scholar.google.com/citations?user=01AmQ8wAAAAJ&hl=en
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.
