Zhe is an assistant professor of business analytics and economics of digitization/information technology. His research interests are on the societal and broader impacts of information technology phenomena. Two example phenomena are the growth of data-driven decision-making and the sharing economy. In those phenomena, he has worked on fairness and bias—auditing prediction algorithms for bias—and studied how the sharing economy affects the urban economy or manufacturer strategies. Zhe has presented at top conferences in information systems (CIST, WISE), economics (NBER), and computer science (KDD). In his research, he uses a variety of methods including applied microeconomics and machine learning. Other research interests include technological change and machine learning in public policy.
During his graduate degree, Zhe also spent some time working in data science at Data Science for Social Good, DataKind, and on fairness and bias methods in AI at Facebook. Zhe completed his Ph.D. in Information Systems and Management at Carnegie Mellon University in the Heinz College, and Bachelor's degrees in Economics and in Statistics at Stanford University.
Identifying Significant Predictive Bias in Classifiers
Business Models in the Sharing Economy: Manufacturing durables in the presence of peer-to-peer rental markets
Ridesharing, Spatial Frictions, and Urban Consumption Patterns
Selected Awards & Honors
- Runner-up for ACM SIGMIS Doctoral Dissertation Award 2019
- Best paper finalist (POMS 2017 Supply Chain Management)