Shenhao Wang is a Postdoctoral Associate at MIT Urban Mobility Lab. His research interest is at the interaction of machine learning, decision-making theories, and urban transportation applications. His recent work examines how to extract economic information from the “black box” deep neural networks, improving the interpretability of DNNs and generating mutual insights between deep learning and classical choice modeling. He also works on decision-making under uncertainty with joint machine learning and individual behavioral perspectives. Particularly, he uses classical prospect theory to understand how people respond to the uncertainty in autonomous vehicles (AVs) and uses a robust machine learning framework to reduce the analytical uncertainty for AVs. Currently, he is working on the social justice of transportation policies, integration of urban systems through the machine learning framework, and demand analysis with unstructured data. Shenhao Wang obtained an interdepartmental Ph.D. in Computer and Urban Science from MIT in 2019. He has B.A. in Economics from Peking University and B.A. in architecture and law from Tsinghua University, Master of Science in Transportation, and Master of City Planning from MIT.
Research Projects: SMART, MIT Energy Initiative
Research Interests: Machine Learning, Decision-Making Theory, Transport Policy, Automated Vehicle Analytics, Chinese Urbanization and Motorization