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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.

Jose Blanchet
Jose H. BlanchetProfessor of Management Science & Engineering

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 profile

A personal path

From Oaxaca to Stanford—with probability along the way.

I grew up in Oaxaca, Mexico, where a high-school course first drew me to probability and statistics. Because applied mathematics was not available locally, I moved to Mexico City to study at ITAM, earning degrees in applied mathematics and actuarial science.

ITAM also gave me the beginning of my favorite personal story: I met my wife, Citlalli (“Lalli”), in Algebra I. That may not sound especially romantic, but we have always thought it was a good love story. We later came to the Bay Area for graduate school—me at Stanford and Lalli at Berkeley. The longer version is still best told in person.

People

Current students

Researchers working across probability, optimization, machine learning, and stochastic systems.

View the full group & alumni

Research

Three connected areas

We study fundamental questions and build practical tools across stochastic modeling, robust learning, and rare events.

01

Limit Theorems

Asymptotic theory, stochastic approximation, and the probabilistic structure behind complex systems.

02

Modeling

Data-driven models for learning, operations, finance, and decision-making under uncertainty.

03

Risk & Extremes

Rare-event analysis, robust methods, simulation, and reliable decisions in high-consequence settings.

Recent work

Latest publications

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2026

Extending Subsampling to Sequential Stopping

arXiv

2026

There and Back Again: Bidirectional Diffusion Bridges for Multimodality Translation

arXiv

2026

Sobolev Regularized Score Difference Estimation in Diffusion Models

arXiv

Browse all 349 publications