About
I am 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 (USTC).
I am on the 2025-2026 job market.
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.
A Broader View of Thompson Sampling
with Hongseok Namkoong and Assaf Zeevi, arXiv, slides
- To be submitted to Operations Research
- Job market paper
Deep Learning for Markov Chains: Lyapunov Functions, Poisson's Equation, and Stationary Distributions
with Jose Blanchet and Peter Glynn, arXiv
- Submitted to Special Issue: 40 Years of QUESTA
- NeurIPS 2025 Workshop MLxOR
Deep Learning for Computing Convergence Rates of Markov Chains
with Jose Blanchet and Peter Glynn, NeurIPS 2024 (spotlight), arXiv
Computable Bounds on Convergence of Markov Chains in Wasserstein Distance via Contractive Drift
with 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
Markov Chain Convergence Analysis:
From Pen and Paper to Deep Learning
PhD thesis, Stanford University
Rubik's Cube Scrambling Requires at Least 26 Random Moves
Double Distributionally Robust Bid Shading for First Price Auctions
with Ravi Kant, Yan Chen, Brendan Kitts, San Gultekin, Aaron Flores, Jose Blanchet, arXiv, slides
On a New Characterization of Harris Recurrence for Markov Chains and Processes
with Peter Glynn, Mathematics, 2023
Strong Limit Interchange Property of a Sequence of Markov Processes
with Jose Blanchet and Peter Glynn, preprint
Estimating the Convergence Rate to Equilibrium of a Markov Chain via Simulation
with Jose Blanchet and Peter Glynn, preprint
Bias of Markov Chain Sample Quantile
with Peter Glynn, preprint
Uniform Edgeworth Expansion for Markov Chains
with Peter Glynn, preprint
Teaching
I served as a teaching assistant for the following MS&E courses at Stanford (Centennial Teaching Assistant Award).
Contact
qu dot yanlin at columbia dot edu
