Yanlin Qu        

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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). My email is quyanlin at stanford dot edu.


Broadly speaking, I am interested in analyzing modern complex systems where uncertainty plays an essential role in performance analysis and decision making. These systems include not only stochastic models utilized in operations research (OR) and management science (MS) but also stochastic algorithms utilized in optimization and simulation. To fundamentality facilitate this analysis, I develop novel theories in the realm of general state space Markov chains. My interests have recently expanded into online advertising as I collaborate with Yahoo DSP to develop a distributionally robust bid shading policy.

MC Convergence Trilogy

Computable Bounds on Convergence of Markov Chains in Wasserstein Distance

with Jose Blanchet and Peter Glynn, submitted, [arXiv]

  • Applied Probability Society Best Student Paper Prize, 2023
  • Applied Probability Society Conference Best Poster Award, 2023
  • Analytical framework to bound convergence

Estimating the Convergence Rate to Equilibrium of a Markov Chain via Simulation

with Jose Blanchet and Peter Glynn, preprint

  • Computational framework to bound convergence

Strong Limit Interchange Property of a Sequence of Markov Processes

with Jose Blanchet and Peter Glynn, work in progress

  • General framework to verify \(X_n(t)\Rightarrow X_\infty(\infty)\) as \(n,t\rightarrow\infty\)

Bias of Markov Chain Sample Quantile

with Peter Glynn, preprint

  • How biased are sample quantiles generated by MCMC?

Uniform Edgeworth Expansion for Markov Chains

with Peter Glynn, preprint

  • Parameteric expansion with uniformly small remainder

On a New Characterization of Harris Recurrence for Markov Chains and Processes

with Peter Glynn, Mathematics, 2023

  • Single random time that leads to Harris Recurrence

Double Distributionally Robust Bid Shading for First Price Auctions

with Ravi Kant, Yan Chen, Brendan Kitts, San Gultekin, Aaron Flores, Jose Blanchet, submitted, manuscript available upon request

  • How should we bid if we are uncertain about multiple factors?


Winner of the 2024 Centennial Teaching Assistant Award

Teaching assistant for the following MS&E courses at Stanford

100-199, 200-299, 300-399

Undergraduate, Intermediate, Graduate

220: Probabilistic Analysis

Autumn 2019, Summer 2022

323: Stochastic Simulation

Autumn 2020, Winter 2024

324: Stochastic Methods in Engineering

Spring 2021, Spring 2022, Winter 2023, Spring 2024


This is a fun project related to Markov chain, stochastic simulation, and cube. Perfect order can be reached in twenty moves (God's number). What about complete chaos? How hard is it to scramble Rubik’s Cube?

Sunset Panorama