Weibin Mo
My name is Weibin Mo (莫伟斌). I am an Assistant Professor of Management in Quantitative Methods area at Purdue Mitchell E. Daniels, Jr. School of Business. 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.
Contact Information
Email: harrymok at purdue dot edu
Phone: +1 (765) 494-4855
Office: KRAN 711
Address: 403 Mitch Daniels Blvd, West Lafayette, IN 47907, United States
Websites: Google Scholar, Linkedin, Github
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. 2020Student Paper Competition Winner
Statistical Learning and Data Science Section, American Statistical Association, Aug. 2020Cambanis-Hoeffding-Nicholson Award
Department of Statistics and Operations Research, the University of North Carolina at Chapel Hill, Dec. 2017