Weibin Mo is an Assistant Professor of Management in Quantitative Methods area at Purdue School of Business. His research interests mainly focus on statistical methodologies in machine learning, personalized decision making, causal inference and semiparametric inference, and robust optimization. The major application areas of his research are precision medicine, inventory management, and assortment. 

Before joining Purdue, Weibin Mo has been working as an Applied Scientist on overstock inventory management at Supply Chain Optimization Technologies (SCOT), Amazon