Linwei Xin is an associate professor of Operations Management at the University of Chicago Booth School of Business. His primary research is on inventory and supply chain management: designing models and algorithms for organizations to effectively "match supply to demand" in various contexts with uncertainty. His research using asymptotic analysis to study stochastic inventory theory has been recognized with several INFORMS paper competition awards, including the Applied Probability Society Best Publication Award (2019), First Place in the George E. Nicholson Student Paper Competition (2015), Second Place in the Junior Faculty Interest Group Paper Competition (2015), and a finalist in the Manufacturing and Service Operations Management Student Paper Competition (2014). His work on implementing state-of-the-art multi-agent deep reinforcement learning techniques in Alibaba's inventory replenishment system was selected as a finalist for the INFORMS 2022 Daniel H. Wagner Prize, with more than 65% algorithm-adoption rate within Alibaba’s own supermarket brand Tmall Mart. His research with JD.com on dispatching algorithms for robots in intelligent warehouses was recognized as a finalist for the INFORMS 2021 Franz Edelman Award, with an estimate of billions of dollars in savings. His research has been published in journals such as Operations Research, Management Science, Mathematics of Operations Research, and INFORMS Journal on Applied Analytics.
Before joining Booth in 2017, Xin was an assistant professor in the College of Engineering at the University of Illinois at Urbana-Champaign. He earned a PhD in operations research from the Georgia Institute of Technology in 2015 and a bachelor's degree in mathematics from Zhejiang University in 2008.