I am a 5th-year Ph.D. Candidate in the Department of Information and Decision Sciences at Carlson School of Management, University of Minnesota. My research focuses on algorithmic and economic aspects of IT-enabled platforms in the areas of recommender systems and healthcare markets. As part of algorithmic explorations, I combine machine learning, deep learning and simulation to understand and improve recommender systems performance in the settings where traditional modeling assumptions do not hold due to the real-world bias, privacy, and item life length considerations. To explore the economic impact of IT platforms, I combine randomized controlled trials, quasi-natural experiments, econometric analyses, and machine learning to study the effects of personalized recommendations on product category expansion as well as the effects of telehealth adoption. My studies have won Best Paper Award at ZEW Conference 2021 and Best Student Paper Award at INFORMS Workshop on Data Science 2020.
Prior to graduate school, I obtained my master's degree at Chinese Academy of Sciences, and bachelor’s degree at Renmin University of China. I have worked as research intern at companies such as Best Buy, JD.com, and Tencent. My internship relevant research have been published on top computer science conferences including WSDM and CIKM.