Lin Fan
Donald P. Jacobs Scholar
Assistant Professor of Operations
Lin Fan is an Assistant Professor of Operations at the Kellogg School of Management (since September 2024). Prior to joining Kellogg, he spent one year as a Postdoctoral Scientist at Amazon in Supply Chain Optimization Technologies. He received his PhD in Management Science and Engineering, an MS in Statistics, and an MS in Mechanical Engineering, all from Stanford University. His research interests lie broadly at the interface of applied probability and data-driven operations, with specializations in multi-armed bandits, reinforcement learning, statistical inference for stochastic processes, and stochastic simulation.
- applied probability + data-driven operations: multi-armed bandits
- reinforcement learning
- statistical inference for stochastic processes
- and stochastic simulation
- operations management
- sequential learning and decision-making under uncertainty
- stochastic models
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PhD, 2023, Operations Research, Stanford University
MS, 2017, Statistics, Stanford University
MS, 2015, Mechanical Engineering, Stanford University
BS, 2012, Mechanical Engineering, Georgia Institute of Technology, Highest Honors -
Assistant Professor, Operations, Kellogg School of Management, Northwestern University, 2024-present
Postdoctoral Scientist, Amazon - Supply Chain Optimization Technologies, 2023-2024 -
Stanford University Centennial Teaching Assistant Award
Second Place, George Nicholson Student Paper Competition (for the paper "The Fragility of Optimized Bandit Algorithms"), INFORMS
National Science Foundation Graduate Research Fellowship, 3 years
Emerging Areas in Operations Managements (OPNS-525-0)
This course studies novel, emerging topics and methods used in academic research of operations management. Content will depend on the expertise and interests of the instructor. Past content included statistical (machine) learning and sequential decision-making, such as bandit learning, balancing exploration/exploitation, and reinforcement learning, including methods for value function approximation and algorithms for efficient exploration.
Operations Management (OPNS-430-0)
1Ys: This course is typically waived through the admissions process or the equivalent course Operations Management (Turbo) (OPNS-438A) was completed during the Summer term. MMMs: This course is equivalent to the MMM core course Designing and Managing Business Processes (OPNS-440) Operations management is the management of business processes--that is, the management of the recurring activities of a firm. This course aims to familiarize students with the problems and issues confronting operations managers, and to provide the language, concepts, insights and tools to deal with these issues to gain competitive advantage through operations. We examine how different business strategies require different business processes and how different operational capabilities allow and support different strategies to gain competitive advantage. A process view of operations is used to analyze different key operational dimensions such as capacity management, cycle time management, supply chain and logistics management, and quality management. Finally, we connect to recent developments such as lean or world-class manufacturing, just-in-time operations, time-based competition and business re-engineering.
