I am a tenured Associate Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University. My research examines the economic and social impacts of technologies and identifies effective designs and policies for technology-enabled markets and platforms, using economic theories, structural modeling, statistical modeling, machine learning methods, and an understanding of the underlying technologies. I am an early proponent of the use of structural econometric models to study the design and policy questions in the field of information systems. 

My recent work focuses on the economics of artificial intelligence (AI) and machine learning (ML) (papers). I examine the use of AI and ML through an economic lens, with particular attention to agents' incentives and strategic behavior in settings where algorithmic decision-making plays a central role, as well as the broader social and economic consequences of these systems. Topics I have studied include algorithmic fairness, transparency, and collusion. I am among the first to bring economic and social perspectives to research on fair ML. 

In addition to my current focus on AI and ML, some of my earlier and ongoing work addresses questions related to digital platforms and online markets (papers). In the context of digital platforms, I study the economic forces that shape user behavior and generate actionable insights for platform design and management. My research on online marketplaces examines how firms can leverage data-driven strategies to optimize pricing, personalization, and user engagement.

Prior to joining Tepper, I was an assistant professor at the Ross School of Business, University of Michigan. I received my bachelor's degree from Tsinghua University and Ph.D. degree from Carnegie Mellon University.