About Me

I’m a fourth-year Ph.D. candidate in Operations Research at Cornell University, advised by Prof. Huseyin Topaloglu. I am also fortunate to work with Prof. Paat Rusmevichientong at University of Southern California. Before that, I obtained B.S. in Mathematics and Applied Mathematics from Peking University in 2021. 

I have a broad interest in modeling stochastic optimization problems and designing provably good algorithms to solve them, with a particular focus on applications in revenue management and pricing.

Publications

Revenue Management with Calendar-Aware and Dependent Demands: Asymptotically Tight Fluid Approximations Weiyuan Li, Paat Rusmevichientong and Huseyin Topaloglu, Operations Research, forthcoming.

Working Papers

History-dependent fluid approximations and performance guarantees for revenue management with Markov-modulated demands  Weiyuan Li, Paat Rusmevichientong, Huseyin Topaloglu and Jingwei Zhang, submitted.

Teaching

ORIE 3120:  Practical Tools for Operations Research, Machine Learning and Data Science, Cornell University, Spring 2022 (TA).

ORIE 6700:  Statistical Principles, Cornell University, Fall 2022 (TA).

ORIE 3510:  Introduction to Engineering Stochastic Processes I, Cornell University, Spring 2023 (TA).

ORIE 5530:  Modeling Under Uncertainty, Cornell Tech, Fall 2023 (TA).

ORIE 5132:  Pricing Analytics and Revenue Management, Cornell Tech, Spring 2024 and Spring 2025 (TA).

News

"Revenue Management with Calendar-Aware and Dependent Demands: A Tight Fluid Approximation" presentations at Informs Annual Meeting and Kellogg's OM Rookiepalooza, October 2024.

"Revenue Management with Calendar-Aware and Dependent Demands: A Tight Fluid Approximation" presentation at INFORMS Revenue Management and Pricing Section Conference, July 2024.

"Revenue Management with Calendar-Aware and Dependent Demands" Poster at  Cornell Young Researchers Workshop, presentation at ORIE Industry and Data Science Summit, October 2023.