Weibin Mo

My name is Weibin Mo (莫伟斌). I am Assistant Professor of Management in Department of Quantitative Methods, Daniels School of Business, Purdue University. My 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 my research are precision medicine, inventory management, and assortment. 

Before joining Purdue, I have been working as an Applied Scientist on overstock inventory management at Supply Chain Optimization Technologies (SCOT), Amazon. I obtained my Ph.D. in Statistics from Department of Statistics and Operations Research, the University of North Carolina at Chapel Hill. I finished my B.B.A. in Business Administration and B.S. in Mathematics at Nankai University. 

Honors and Awards

• Excellence in Teaching Assistance and Instruction Award
  Department of Statistics and Operations Research, the University of North Carolina at Chapel Hill, Dec. 2020

• Student Paper Competition Winner
  Statistical Learning and Data Science Section, American Statistical Association, Aug. 2020

• Cambanis-Hoeffding-Nicholson Award
  Department of Statistics and Operations Research, the University of North Carolina at Chapel Hill, Dec. 2017