I study how information should be designed and used in AI systems and digital platforms: what to reveal, what to withhold, and how to turn technology-generated data into better operational and market decisions.

I focus on two domains: digital marketplaces, including crowdsourcing and e-commerce, and human–AI collaboration, including AI-assisted decision-making, learning from AI, and AI companions. 

I combine structural estimation, game-theoretic modeling, causal inference, and applied machine learning, often working with industry and public-sector partners.

My research has received the INFORMS TIMES Best Dissertation Award and finalist honors in multiple INFORMS and POMS competitions. I serve on the Editorial Review Board of Information Systems Research.

I received my Ph.D. from the Ross School of Business, University of Michigan, and my B.A. in Economics and B.S. in Statistics from Peking University.