About Me
I’m an Assistant Professor in the Operations Department of the Kellogg School of Management at Northwestern University. From 2023-2024, I was a Postdoctoral Scientist in the Supply Chain Optimization Technologies group at Amazon, supervised by Garrett van Ryzin. I obtained my PhD in 2023 in Management Science and Engineering at Stanford University, where I was fortunate to be advised by Peter Glynn.
Email: lin.fan [at] kellogg.northwestern.edu
My research interests are at the interface of applied probability and data-driven operations research, with specializations in (1) sequential decision-making under uncertainty, and (2) statistical inference for stochastic processes arising in operations settings.
For sequential decision-making problems, I am interested in better understanding the trade-off between optimizing for average performance vs ensuring reliability/safety. My goal here is to design algorithms with good practical performance (as measured by regret), for which we more fully understand operational risks. Along this direction, I have been focused on the distribution (and especially the distributional tail properties) of regret, which provides new perspectives on algorithm design different from the conventional focus on minimizing regret in expectation.
Another thread of my work is in developing statistically and computationally efficient tools to better quantify uncertainty in stochastic system performance. I am particularly interested in flexible/robust statistical formulations of stochastic process models arising in, for example, queueing systems, where the focus has traditionally been on simpler, analytically tractable formulations. My work here also intersects with stochastic simulation and time series analysis.