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Yuexing Li (李跃星)

Assistant Professor
Johns Hopkins Carey Business School

Welcome to my website! I am an Assistant Professor of Operations Management and Business Analytics at Johns Hopkins Carey Business School. I obtained my Ph. D. degree in Business Administration in the field of Operations Management at the Fuqua School of Business, Duke University. 

Before joining Duke, I obtained a master's degree from Cornell University, a master's degree from Yale University, and two bachelor's degrees from Peking University in Beijing, China.

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Decision Making

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I am broadly interested in designing and analyzing data-driven algorithms to facilitate decision making under uncertainty. 


I leverage and develop many statistical/machine learning methods, including deep learning, and combine them with dynamic pricing, inventory control, and optimization to solve complex operational problems in the big data era. My work advances the theoretical foundation of various data-driven analytics and significantly improves the performance in real applications across multiple industries, including grocery retailing, energy, and digital platforms. 


Ph.D. in Business Administration

Duke University

M.Eng. in Operations Research

Cornell University

M.S. in Biomedical Engineering

Yale University

B.S. in Chemistry

B.A. in Economics

Peking University


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with P.L. Jackson & J.A. Muckstadt (2019), Management Science, 65(2):794-818

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with N.B. Keskin & J.-S. Song (2022), Management Science, 68(3): 1938-1958

- Special Issue on Data-driven Prescriptive Analytics

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Photograph Courtesy of Solume

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with N.B. Keskin & N. Sunar, under revision at Operations Research

- Winner, INFORMS Data Mining Best Paper Competition, 2020

- Spotlight Track, INFORMS 2021 RM&P Conference

- Winner, INFORMS Service Science Best Paper Competition, 2022

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Deep Learning for Visual Advertising on Digital Platforms:
Asymptotically Optimal Image Selection

with N.B. Keskin, S. Liu, & J.-S. Song, to be submitted soon

with Y. Deng & J.-S. Song, submitted to Management Science 

A Unified Parsimonious Model for Structural Demand Estimation
Accounting for Stockout and Substitution

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