Prof. LI Menglong
李夢龍教授
Assistant Professor
PhD - Operations Research (University of Illinois at Urbana-Champaign)
MSc - Mathematics (University of Pierre and Marie Curie)
BSc - Mathematics (Tsinghua University)

Research Areas

Inventory Management
Revenue Management
Data-Driven Decision Making
(Discrete) Convex Analysis

Biography

Prof. Menglong Li is an Assistant Professor of Management Sciences in the College of Business, City University of Hong Kong. Before joining CityU, he was a postdoctoral associate of MIT Institute for Data, Systems, and Society. He received a PhD degree in Operations Research from the University of Illinois at Urbana-Champaign, a MS degree in Mathematics from the University of Pierre and Marie Curie and a BS degree in Mathematics from the Tsinghua University. His research interests include inventory management, revenue management, (discrete) convex analysis, combinatorial optimization, approximation algorithms, and data-driven decision making.

Openings: I am looking for self-motivated Ph.D. students interested in operations research and optimization, with potential applications in inventory management and revenue management. Prospective students with strong mathematical backgrounds are welcome to email me a CV and transcript. Students majoring in mathematics are especially welcome.

 

Research Grant

PI: "S-Convexity and Market Equilibrium", General Research Fund - Hong Kong Research Grants Council, Amount: 493,647HKD (2024-2026), Menglong Li and Xin Chen

Publications

Bai, Xingyu; Chen, Xin; Li, Menglong; Stolyar, Alexander / Asymptotic Optimality of Semi-Open-Loop Policies in Markov Decision Processes with Large Lead Times. June 2023; In: Operations Research.
Chen, Xin; Li, Menglong / S-Convexity and Gross Substitutability. November 2022; In: Operations Research.
Chen, Xin; Li, Menglong / M-Convexity and its applications in operations. September 2021; In: Operations Research. Vol. 69, No. 5, pp. 1396-1408
Chen, Xin; Li, Menglong / Discrete Convex Analysis and Its Applications in Operations: A Survey. June 2021; In: Production and Operations Management. Vol. 30, No. 6, pp. 1904-1926