City lights seen through a rain-covered window at dusk

Yanlin Qu

An Applied Probabilist

About.

I am a tenure-track Assistant Professor in the School of Data Science at The Chinese University of Hong Kong, Shenzhen.

Prior to this, I was a Postdoctoral Research Scholar in the Decision, Risk, and Operations Division at Columbia Business School, working with Assaf Zeevi and Hongseok Namkoong.

I earned my PhD in Management Science and Engineering from Stanford University, where I had the privilege of being advised by Peter Glynn and Jose Blanchet. I completed my bachelor's degree in Mathematics at the University of Science and Technology of China.

CURRICULUM VITAE GOOGLE SCHOLAR LINKEDIN ACADEMIC FAMILY TREE

Research.

As an applied probabilist specializing in stochastic modeling and simulation, I use stochastic methods to explore the synergy between Operations Research (OR) and Machine Learning (ML), leveraging ML tools to scale up OR methodologies while applying OR principles to understand and improve ML algorithms.

PUBLICATIONS & PREPRINTS13 WORKS

  1. A Harris Recurrent Continuous-time Markov Process without Wide-sense Regenerative Structure

    Y. Qu and Peter Glynn, arXiv

  2. A Broader View of Thompson Sampling

    Y. Qu, Hongseok Namkoong, and Assaf Zeevi, arXiv

  3. What Does Thompson Sampling Optimize?

    Y. Qu, Hongseok Namkoong, and Assaf Zeevi, ICML 2026

  4. Deep Learning for Markov Chains: Lyapunov Functions, Poisson's Equation, and Stationary Distributions

    Y. Qu, Jose Blanchet, and Peter Glynn, Queueing Systems, arXiv, 2026

    • Special Issue: 40 Years of QUESTA
    • NeurIPS 2025 Workshop MLxOR

  5. Computable Bounds on Convergence of Markov Chains in Wasserstein Distance via Contractive Drift

    Y. Qu, Jose Blanchet, and Peter Glynn, Annals of Applied Probability, arXiv, 2025

    • Applied Probability Society Best Student Paper Prize, 2023
    • Applied Probability Society Conference Best Poster Award, 2023

  6. Deep Learning for Computing Convergence Rates of Markov Chains

    Y. Qu, Jose Blanchet, and Peter Glynn, NeurIPS 2024 (spotlight)

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

    Peter Glynn and Y. Qu, Mathematics, 2023

  8. Rubik's Cube Scrambling Requires at Least 26 Random Moves

    Y. Qu, Tomas Rokicki, and Hillary Yang, arXiv, slides

  9. Double Distributionally Robust Bid Shading for First Price Auctions

    Y. Qu, Ravi Kant, Yan Chen, Brendan Kitts, San Gultekin, Aaron Flores, and Jose Blanchet, arXiv, slides

  10. Strong Limit Interchange Property of a Sequence of Markov Processes

    Y. Qu, Jose Blanchet, and Peter Glynn, preprint

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

    Y. Qu, Jose Blanchet, and Peter Glynn, preprint

  12. Uniform Edgeworth Expansions for Markov Chains with Applications to MCMC Sample Quantiles.

    Y. Qu and Peter Glynn, preprint

  13. Markov Chain Convergence Analysis: From Pen and Paper to Deep Learning

    Y. Qu, Stanford University

Teaching.

CUHK-Shenzhen

Instructor
STA4001HHonours Stochastic Processes

Stanford University

Teaching Assistant

Contact.

School of Data Science
The Chinese University of Hong Kong, Shenzhen

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