About
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.
Research
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 and algorithms in the realm of general state-space Markov chains.
MC Convergence Trilogy
Deep Learning for Computing Convergence Rates of Markov Chains
with Jose Blanchet and Peter Glynn, submitted, [arXiv]
- The first general-purpose algorithm to bound the convergence
Computable Bounds on Convergence of Markov Chains in Wasserstein Distance
with Jose Blanchet and Peter Glynn, submitted, [arXiv]
- The theoretical foundation of the above algorithm
- Applied Probability Society Best Student Paper Prize, 2023
- Applied Probability Society Conference Best Poster Award, 2023
Estimating the Convergence Rate to Equilibrium of a Markov Chain via Simulation
with Jose Blanchet and Peter Glynn, preprint
- Another sample-based algorithm to bound the 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 \(n,t\rightarrow\infty\)
On a New Characterization of Harris Recurrence for Markov Chains and Processes
with Peter Glynn, Mathematics, 2023
Bias of Markov Chain Sample Quantile
with Peter Glynn, preprint
Uniform Edgeworth Expansion for Markov Chains
with Peter Glynn, preprint
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?
Teaching
Winner of the 2024 Centennial Teaching Assistant Award
Teaching assistant for the following MS&E courses at Stanford
Ragnarök
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?
Currently, we have \(t_{\text{mix}}\geq26\). A better bound is coming soon.
