About
Lin Fan is an Assistant Professor of Operations at the Kellogg School of Management, Northwestern University (2024–present). Previously, he was a Postdoctoral Scientist at Amazon's Supply Chain Optimization Technologies (2023–2024). He holds a PhD in Management Science and Engineering from Stanford University (2023), alongside MS degrees in Statistics (2017) and Mechanical Engineering (2015), and a BS in Mechanical Engineering from Georgia Tech (2012).
His research bridges applied probability and data-driven operations, focusing on multi-armed bandits, reinforcement learning, stochastic processes, and simulation. He has received awards including the George Nicholson Student Paper Competition (2nd place, 2022) and an NSF Graduate Fellowship.
His work addresses theoretical foundations and practical challenges in sequential decision-making systems, with applications to supply chain optimization and dynamic resource allocation. Ongoing research explores robustness in bandit algorithms and statistical inference for Markov processes.
