Weibin Mo

Assistant Professor
Quantitative Methods

Education

Ph.D. in Statistics, University of North Carolina at Chapel Hill, 2021
B.B.A. in Business Administration, B.S. in Mathematics, Nankai University, 2016

Weibin Mo is an Assistant Professor of Management in Department of Quantitative Methods. 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

MGMT 30500 (Spring 2023)
MGMT 69000 - (Fall 2022)
In this course, we cover selective concepts in causal inference and cutting-edge research topics. In most causal models, minimal parametric assumptions or identification restrictions based on the targeted causal estimands are made, with the remaining distributional assumptions for the observed and unobserved variables left as unspecified/unrestricted. To understand the properties of causal estimates, the semi-parametric inference framework is also introduced for model-based, assumption-lean and model-free analyses.

Contact

mo63@purdue.edu
Phone: (765) 494-4855
Office: KRAN 711

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