Yizhou (“ee-joh”/毅洲) is an economist and an assistant professor at the University of Toronto.
He has over 10 years of experience in tech and finance. He specializes in designing and implementing advanced data-driven pricing, nudging, contracting, and matching systems. Before joining UofT, he led several data science projects at Twitter such as the launch of the new Communities product as well as group-level experimental evaluation of ranking algorithms. He also designed Lyft’s real-time accident prevention program and delivered 4% per-ride insurance savings via personalized nudges and rewards. Previously, he co-founded a data lab at Alibaba/Ant Group and started digital training for 2+ million e-commerce entrants, with 6% average revenue impact. At the same time, his dissertation on auto insurance monitoring was widely featured by the National Bureau of Economic Research (4x), the Federal Trade Commission, the Competition and Markets Authority in the U.K., and the Reserve Bank of Australia. Previously, he had three years of experience at Citigroup and J.P.Morgan Chase doing Technology investment banking.
At UofT, he is cross-appointed in the Rotman School of Business, the Department of Management at UTSC, and the Department of Economics. He holds a PhD in Business Economics and an MA in Economics from Harvard.
In his research, Yizhou studies how data and AI technologies generate useful information in imperfect markets, and the mechanisms through which they create and distribute economic value. He focuses on applications in insurance and digital platforms, combining economic theory, causal inference, and modern computation methods to extract insights from large datasets.