Blanchet Lab

People

A research community across institutions and generations.

Current researchers and alumni connected through work in probability, optimization, simulation, and learning under uncertainty.

Alumni information reviewed through September 5, 2026

Current group

Doctoral students

Current doctoral researchers in MS&E, ICME, and related Stanford programs.

Current group

Postdoctoral fellows

There are currently no postdoctoral fellows in the group.

Alumni

Doctoral alumni and researchers

Former doctoral researchers from Harvard, Columbia, and Stanford. Dates marked “c.” are approximate and remain under review.

Harvard Statistics2008

Jingchen Liu

Effective Modeling and Scientific Computation with Applications to Health Study, Astronomy, and Queueing Network

Co-advised with Xiao-Li Meng

Harvard Statistics2009

Chenxin Li

Estimation of Overflow Probabilities for Models with Heavy Tails and Complex Dependencies

Harvard Statistics2011

Henry Lam

Efficient Monte-Carlo Methods and Asymptotic Analysis for Stochastic Systems

Columbia IEOR2014

Shuheng Zheng

Stochastic Approximation Algorithms in the Estimation of Quasi-Stationary Distribution of Finite and General State Space Markov Chains

Columbia IEOR2013

Yixi Shi

Rare Events in Stochastic Systems: Modeling, Simulation Design, and Algorithm Analysis

Columbia IEOR2014

Xinyun Chen

Perfect Simulation, Sample-path Large Deviations, and Multiscale Modeling for Some Fundamental Queueing Systems

Columbia IEOR2014

Jing Dong

Studies in Stochastic Networks: Efficient Monte-Carlo Methods, Modeling and Asymptotic Analysis

Columbia IEOR2015

Juan Li

Stochastic Networks: Modeling, Simulation Design and Risk Control

Columbia IEOR2015

Aya Wallwater

Perfect Simulation and Deployment Strategies for Detection

Columbia Statistics2018

Christopher Dolan

Distributionally Robust Performance Analysis with Applications to Mine Valuation and Risk

Columbia Statistics2018

Yang Kang

Distributionally Robust Optimization and its Applications in Machine Learning

Columbia IEOR2018

Yanan Pei

Exact Simulation Techniques in Applied Probability and Stochastic Optimization

Columbia IEOR2018

Fei He

Distributional Robust Performance Analysis

Columbia IEOR2018

Zhipeng Liu

Exact Simulation Algorithms with Applications in Queueing Theory and Extreme Value Analysis

Columbia IEOR2021

Fengpei Li

Stochastic Methods in Optimization and Machine Learning

Co-advised with Henry Lam

Stanford Mathematics2021

Yue Hui

Mean Field Methods for Stochastic Control and Optimization Problems

Co-advised with Peter Glynn

Stanford MS&E2021

Teng Zhang

Efficient Simulation for Complex Systems

Co-advised with Peter Glynn

Stanford Mathematics2021

Zhengqing Zhou

Distributionally Robust Optimization and Its Applications in Mathematical Finance, Statistics, and Reinforcement Learning

Co-advised with Peter Glynn

Stanford ICME2022

Jin Xie

Optimal Transport and Healthcare Operations

Co-advised with Peter Glynn

Stanford Materials Science & Engineering2022

Vincent Dufour-Décieux

Generalizable Chemical Mechanism Extraction from Molecular Dynamics Simulations

Co-advised with Evan Reed

Stanford ICME2021

Carson Kent

Optimization in the Space of Measures: New Techniques from Optimal Transport

Stanford MS&E2022

Linjia Wu

Dynamic Stochastic Models for Experimentation and Matching

Co-advised with Ramesh Johari

Stanford MS&E2022

Nian Si

Essays on Trustworthy Data-Driven Decision Making

Stanford ICME2023

Huanzhong Xu

MCMC with Substitutions and Multi-Armed Bandits with Covariates: Theory and Applications

Stanford MS&E2023

Xuhui Zhang

Distributional Robustness and Minimax Optimality in Selected Problems

Stanford Computer Science2024

Xinru Hua

Algorithms for Robust Learning, Gradient Flows, and Diffusion Generation of Rare Events

Co-advised with Tengyu Ma

Stanford MS&E2024

Yanlin Qu

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

Co-advised with Peter Glynn

Stanford MS&E2025

Shengbo Wang

Learning and Optimal Control of Dynamic Stochastic Systems: Robustness and Scalability

Co-advised with Peter Glynn

Stanford MS&E2026

Kyriakos Lotidis

Robust Multi-Agent Learning for Collective Intelligence

Co-advised with Nicholas Bambos

Stanford MS&E2026

Sirui Lin

Beyond First-Order Limits: Higher-Order Corrections for Mean-Field Control and Wasserstein Projection Tests

Co-advised with Peter Glynn

Alumni

Former postdoctoral fellows

Former postdoctoral researchers hosted by the group.