Linwei Xin is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. Prior to Cornell, he was an Associate Professor of Operations Management at the University of Chicago Booth School of Business. He specializes in inventory and supply chain management, where he designs cutting-edge models and algorithms that help organizations effectively balance supply and demand in various contexts with uncertainty. 

Xin’s current interests focus on AI for supply chains. Recent developments in generative AI and foundation models motivate a central belief behind his work: scaling laws will continue to improve AI systems as models, data, and computing resources grow (e.g., a bitter lesson for retail demand forecasting), but realizing their full value in supply chains requires combining scale with the structural knowledge developed in OR. Domain structure, in his view, is not a restriction on AI but a way to improve statistical efficiency, reduce computational burden, and make deployment more reliable (e.g., learning less is more, integrating DRL with inventory structure). At the same time, modern foundation models and deep-learning methods can substantially expand the range and scale of operational problems that classical methods can address. His research therefore explores how these two perspectives can complement each other to shape the next generation of intelligent, data-driven decision-making tools that make OR methods more accessible and easier to apply in practice.

A defining feature of Xin’s research is the close interplay between theory and practice. He takes a problem-driven approach, with many of his projects inspired by operational innovations at leading companies or developed through direct collaborations with industry partners. These collaborations have led to tangible, real-world impact. For example, his long-term collaboration with Alibaba Group on developing state-of-the-art deep reinforcement learning techniques for Alibaba’s inventory replenishment system has led to two INFORMS Daniel H. Wagner Prize finalist recognitions (2022, 2025) and achieved 100% algorithm adoption within Alibaba’s own supermarket brand Tmall, managing over one million SKU-warehouse pairs. His research on designing dispatching algorithms for robots in JD.com’s intelligent warehouses was recognized as a finalist for the INFORMS 2021 Franz Edelman Award.

Xin’s earlier research using asymptotic analysis to study stochastic inventory theory is renowned and has been recognized with several prestigious INFORMS paper competition awards, including First Place in the George E. Nicholson Student Paper Competition in 2015 and the Applied Probability Society Best Publication Award in 2019.

Xin currently serves as an associate editor for Operations Research, Management Science, Manufacturing & Service Operations Management, and Naval Research Logistics.

Xin earned his Ph.D. in Operations Research from the Georgia Institute of Technology in 2015 and a bachelor’s degree in Mathematics from Zhejiang University in 2008.