Stanford University · Management Science & Engineering
Probability, learning, and decisions under uncertainty.
The Blanchet Lab develops mathematical and computational tools for reliable decisions in complex stochastic systems.
About the lab
Research grounded in probability and built for consequential decisions.
Jose Blanchet is a professor in Stanford University’s Department of Management Science and Engineering and an Amazon Scholar. His research spans applied probability, Monte Carlo methods, distributionally robust optimization, and machine learning.
The lab brings together probability, optimization, and data to study systems where uncertainty is central—not incidental.
Stanford profilePeople
Current students
Researchers working across probability, optimization, machine learning, and stochastic systems.
Co-advised with Peter Glynn
Co-advised with Vasilis Syrgkanis
Co-advised with Renyuan Xu
Co-advised with Renyuan Xu
Co-advised with Amir Dembo
Research
Three connected areas
We study fundamental questions and build practical tools across stochastic modeling, robust learning, and rare events.
Limit Theorems
Asymptotic theory, stochastic approximation, and the probabilistic structure behind complex systems.
Modeling
Data-driven models for learning, operations, finance, and decision-making under uncertainty.
Risk & Extremes
Rare-event analysis, robust methods, simulation, and reliable decisions in high-consequence settings.
Recent work
Latest publications
Extending Subsampling to Sequential Stopping
arXiv
There and Back Again: Bidirectional Diffusion Bridges for Multimodality Translation
arXiv
Sobolev Regularized Score Difference Estimation in Diffusion Models
arXiv