I am a final-year PhD candidate at Stanford University in the Operations Research group within Department of Management Science and Engineering, where I am fortunate to be advised by Peter Glynn and Jose Blanchet. Previously, I received my bachelor degree in Mathematics from University of Science and Technology of China (USTC).
I am on the 2023-2024 academic job market.
Email: quyanlin [at] stanford.edu.
Broadly speaking, I am interested in analyzing modern complex systems where uncertainty presents, especially their efficiency and stability. These systems include not only stochastic models utilized in management science and engineering but also stochastic algorithms utilized in optimization and simulation. To fundamentally facilitate this analysis, I introduce a novel framework to perform quantitative Markov chain convergence analysis. Meanwhile, I also develop Markovian probability theorems to address important questions in stochastic simulation.
My interests were recently expanded into robust decision making under uncertainty after I developed a distributionally robust bid shading strategy for a major demand side platform that participates millions of online ad auctions per day.
“A new class of bounds for convergence of Markov chains to equilibrium”
“with application to stochastic gradient descent” [Slides] (2023 INFORMS Annual Meeting: SE08. APS Student Paper Competition)
“with application to stochastic fluid network” [Slides] (2023 INFORMS Annual Meeting: SC10. Ergodicity and Scaling Limits of Stochastic Networks)
“with application to priority queues” [Slides] (SNAPP Seminar: Lightning Talks on 12/11/2023)