About

Jose H. Blanchet is a Professor of Management Science and Engineering at Stanford University and an Amazon Scholar. He holds a PhD in Management Science and Engineering from Stanford University, obtained in 2004, and an MS in Engineering Economic Systems and Operations Research from Stanford, earned in 2002. He also earned Licenciado degrees in Applied Mathematics and Actuarial Science from Instituto Technologico Autonomo de Mexico in 2000. Prior to his current position, he was a professor at Columbia University in the departments of Industrial Engineering and Operations Research, and Statistics from 2008 to 2017, and before that, he taught at Harvard University in Statistics from 2004 to 2008. Blanchet is a Fellow of the Institute of Mathematical Statistics and has received numerous awards, including the 2010 Erlang Prize and a Presidential Early Career Award for Scientists and Engineers in 2010. His research interests focus on applied probability and Monte Carlo methods. He currently leads a Department of Defense Multi-University Research Initiative on extreme events and serves as the Area Editor of Stochastic Models in Mathematics of Operations Research. He has served on the editorial boards of several prominent journals in his field.

Research topics

• Computer Science
• Mathematics
• Combinatorics
• Artificial Intelligence
• Physics
• Mathematical optimization
• Statistics
• Algorithm
• Machine Learning
• Geometry
• Statistical physics
• Discrete mathematics
• Mathematical analysis
• Finance
• Theoretical computer science
• Economics
• Econometrics

Selected publications

• Robust Assortment Optimization from Observational Data

  Open MIND · 2026-02-11

  preprintSenior author

  Assortment optimization is a fundamental challenge in modern retail and recommendation systems, where the goal is to select a subset of products that maximizes expected revenue under complex customer choice behaviors. While recent advances in data-driven methods have leveraged historical data to learn and optimize assortments, these approaches typically rely on strong assumptions -- namely, the stability of customer preferences and the correctness of the underlying choice models. However, such assumptions frequently break in real-world scenarios due to preference shifts and model misspecification, leading to poor generalization and revenue loss. Motivated by this limitation, we propose a robust framework for data-driven assortment optimization that accounts for potential distributional shifts in customer choice behavior. Our approach models potential preference shift from a nominal choice model that generates data and seeks to maximize worst-case expected revenue. We first establish the computational tractability of robust assortment planning when the nominal model is known, then advance to the data-driven setting, where we design statistically optimal algorithms that minimize the data requirements while maintaining robustness. Our theoretical analysis provides both upper bounds and matching lower bounds on the sample complexity, offering theoretical guarantees for robust generalization. Notably, we uncover and identify the notion of ``robust item-wise coverage'' as the minimal data requirement to enable sample-efficient robust assortment learning. Our work bridges the gap between robustness and statistical efficiency in assortment learning, contributing new insights and tools for reliable assortment optimization under uncertainty.

  DOI

• Statistical Inference in Causal Partial Identification with Smooth Densities

  Open MIND · 2026-02-24

  preprint

  Many causal quantities are only partially identifiable due to the inherent missingness of potential outcomes, and the associated partial identification (PI) sets can be obtained by solving an optimal transport (OT) problem. Covariates often provide additional information about the potential outcomes and thus yield tighter PI sets, which can be obtained via conditional optimal transport (COT). However, COT-based PI set estimators are susceptible to the curse of dimensionality in the covariates and outcomes, which precludes the asymptotic normality and hinders statistical inference. In this paper, we exploit smoothness in the marginal densities of covariates and potential outcomes and develop a wavelet-based primal method for COT with multivariate outcomes and covariates. Moreover, for quadratic cost functions, we establish a stability result for COT and prove asymptotic normality of the proposed estimator. This characterization of the asymptotic distribution enables valid statistical inference for the partial identification set. Empirically, we validate the estimation and inference performance of our approach through numerical experiments in comparison with existing benchmarks.

  DOI

• Viral Quasispecies Evolution as a Branching Random Walk on the Hypercube

  arXiv (Cornell University) · 2026-03-28

  articleOpen access1st authorCorresponding

  We study a continuous-time nearest-neighbor branching random walk on the $d$-dimensional $b$-ary hypercube $\{0,1,\dots,b-1\}^d$ as a model for viral quasispecies evolution under mutation and replication. Motivated by mutagenic antiviral treatments and evolutionary-safety questions, we analyze the first passage time to a fixed target genotype at Hamming distance $m$, corresponding to the first appearance of a prescribed collection of mutations. We derive sharp asymptotics for these first passage times, uniformly for $m\le d/L$ as $d\to\infty$ (where $L>0$ is a large constant), and identify a phase transition in first-passage scaling at $ρ=e$, where $ρ$ denotes the effective growth parameter. In the slow-branching regime $ρ\in(1,e)$ relevant to mutagenic treatment scenarios, the first passage time is asymptotically affine in the genome length $d$ and the target distance $m$. In particular, when replication is fixed and mutation exceeds branching, increasing the mutation rate can delay the first appearance of a prescribed genotype by order $d$, providing a quantitative perspective on evolutionary safety.

  PublisherOA PDF

• Distributionally Robust Regret Optimal LQR with Common Stage-Law Ambiguity

  arXiv (Cornell University) · 2026-04-07

  preprintOpen accessSenior author

  We study, to our knowledge, the first tractable multistage ex-ante distributionally robust regret optimization (DRRO) formulation for stochastic control. We consider finite-horizon LQR under common stage-law ambiguity: disturbances are independent across time but share an unknown stage law whose mean and covariance lie in a Gelbrich ball around nominal parameters. Unlike the single-stage quadratic case, the nominal certainty-equivalent (CE) controller is generally not regret-optimal, because reuse of the stage law makes past disturbances informative for future decisions. Despite the general NP-hardness of DRRO, we show that over linear disturbance-feedback policies the resulting multistage DRRO-LQR problem admits an exact semidefinite programming reformulation. The optimal controller is the nominal certainty-equivalent LQR law plus a strictly causal empirical-mean correction. We also characterize worst-case distributions and show that those for the DRRO-optimal policy are nonunique. Numerical results show that, relative to the corresponding DRO controller under the same ambiguity set, DRRO is often substantially less conservative while preserving the intended regret guarantee, and that its correction coefficients empirically approach the certainty-equivalent feedforward coefficient.

  PublisherDOI

• When Should Humans Step In? Optimal Human Dispatching in AI-Assisted Decisions

  arXiv (Cornell University) · 2026-03-14

  articleOpen accessSenior author

  AI systems increasingly assist human decision making by producing preliminary assessments of complex inputs. However, such AI-generated assessments can often be noisy or systematically biased, raising a central question: how should costly human effort be allocated to correct AI outputs where it matters the most for the final decision? We propose a general decision-theoretic framework for human-AI collaboration in which AI assessments are treated as factor-level signals and human judgments as costly information that can be selectively acquired. We consider cases where the optimal selection problem reduces to maximizing a reward associated with each candidate subset of factors, and turn policy design into reward estimation. We develop estimation procedures under both nonparametric and linear models, covering contextual and non-contextual selection rules. In the linear setting, the optimal rule admits a closed-form expression with a clear interpretation in terms of factor importance and residual variance. We apply our framework to AI-assisted peer review. Our approach substantially outperforms LLM-only predictions and achieves performance comparable to full human review while using only 20-30% of the human information. Across different selection rules, we find that simpler rules derived under linear models can significantly reduce computational cost without harming final prediction performance. Our results highlight both the value of human intervention and the efficiency of principled dispatching.

  PublisherOA PDF

• Robust Assortment Optimization from Observational Data

  arXiv (Cornell University) · 2026-02-11

  articleOpen accessSenior author

  Assortment optimization is a fundamental challenge in modern retail and recommendation systems, where the goal is to select a subset of products that maximizes expected revenue under complex customer choice behaviors. While recent advances in data-driven methods have leveraged historical data to learn and optimize assortments, these approaches typically rely on strong assumptions -- namely, the stability of customer preferences and the correctness of the underlying choice models. However, such assumptions frequently break in real-world scenarios due to preference shifts and model misspecification, leading to poor generalization and revenue loss. Motivated by this limitation, we propose a robust framework for data-driven assortment optimization that accounts for potential distributional shifts in customer choice behavior. Our approach models potential preference shift from a nominal choice model that generates data and seeks to maximize worst-case expected revenue. We first establish the computational tractability of robust assortment planning when the nominal model is known, then advance to the data-driven setting, where we design statistically optimal algorithms that minimize the data requirements while maintaining robustness. Our theoretical analysis provides both upper bounds and matching lower bounds on the sample complexity, offering theoretical guarantees for robust generalization. Notably, we uncover and identify the notion of ``robust item-wise coverage'' as the minimal data requirement to enable sample-efficient robust assortment learning. Our work bridges the gap between robustness and statistical efficiency in assortment learning, contributing new insights and tools for reliable assortment optimization under uncertainty.

  PublisherOA PDF

• Distributionally Robust Regret Optimal LQR with Common Stage-Law Ambiguity

  ArXiv.org · 2026-04-07

  articleOpen accessSenior author

  We study, to our knowledge, the first tractable multistage ex-ante distributionally robust regret optimization (DRRO) formulation for stochastic control. We consider finite-horizon LQR under common stage-law ambiguity: disturbances are independent across time but share an unknown stage law whose mean and covariance lie in a Gelbrich ball around nominal parameters. Unlike the single-stage quadratic case, the nominal certainty-equivalent (CE) controller is generally not regret-optimal, because reuse of the stage law makes past disturbances informative for future decisions. Despite the general NP-hardness of DRRO, we show that over linear disturbance-feedback policies the resulting multistage DRRO-LQR problem admits an exact semidefinite programming reformulation. The optimal controller is the nominal certainty-equivalent LQR law plus a strictly causal empirical-mean correction. We also characterize worst-case distributions and show that those for the DRRO-optimal policy are nonunique. Numerical results show that, relative to the corresponding DRO controller under the same ambiguity set, DRRO is often substantially less conservative while preserving the intended regret guarantee, and that its correction coefficients empirically approach the certainty-equivalent feedforward coefficient.

  PublisherOA PDF

• Viral Quasispecies Evolution as a Branching Random Walk on the Hypercube

  arXiv (Cornell University) · 2026-03-28

  preprintOpen access1st authorCorresponding

  We study a continuous-time nearest-neighbor branching random walk on the $d$-dimensional $b$-ary hypercube $\{0,1,\dots,b-1\}^d$ as a model for viral quasispecies evolution under mutation and replication. Motivated by mutagenic antiviral treatments and evolutionary-safety questions, we analyze the first passage time to a fixed target genotype at Hamming distance $m$, corresponding to the first appearance of a prescribed collection of mutations. We derive sharp asymptotics for these first passage times, uniformly for $m\le d/L$ as $d\to\infty$ (where $L>0$ is a large constant), and identify a phase transition in first-passage scaling at $ρ=e$, where $ρ$ denotes the effective growth parameter. In the slow-branching regime $ρ\in(1,e)$ relevant to mutagenic treatment scenarios, the first passage time is asymptotically affine in the genome length $d$ and the target distance $m$. In particular, when replication is fixed and mutation exceeds branching, increasing the mutation rate can delay the first appearance of a prescribed genotype by order $d$, providing a quantitative perspective on evolutionary safety.

  PublisherDOI

• Optimal Quantum Speedups for Repeatedly Nested Expectation Estimation

  Open MIND · 2026-02-08

  preprintSenior author

  We study the estimation of repeatedly nested expectations (RNEs) with a constant horizon (number of nestings) using quantum computing. We propose a quantum algorithm that achieves $\varepsilon$-error with cost $\tilde O(\varepsilon^{-1})$, up to logarithmic factors. Standard lower bounds show this scaling is essentially optimal, yielding an almost quadratic speedup over the best classical algorithm. Our results extend prior quantum speedups for single nested expectations to repeated nesting, and therefore cover a broader range of applications, including optimal stopping. This extension requires a new derandomized variant of the classical randomized Multilevel Monte Carlo (rMLMC) algorithm. Careful de-randomization is key to overcoming a variable-time issue that typically increases quantized versions of classical randomized algorithms.

  DOI

• Optimal Quantum Speedups for Repeatedly Nested Expectation Estimation

  ArXiv.org · 2026-02-08

  articleOpen accessSenior author

  We study the estimation of repeatedly nested expectations (RNEs) with a constant horizon (number of nestings) using quantum computing. We propose a quantum algorithm that achieves $\varepsilon$-error with cost $\tilde O(\varepsilon^{-1})$, up to logarithmic factors. Standard lower bounds show this scaling is essentially optimal, yielding an almost quadratic speedup over the best classical algorithm. Our results extend prior quantum speedups for single nested expectations to repeated nesting, and therefore cover a broader range of applications, including optimal stopping. This extension requires a new derandomized variant of the classical randomized Multilevel Monte Carlo (rMLMC) algorithm. Careful de-randomization is key to overcoming a variable-time issue that typically increases quantized versions of classical randomized algorithms.

  PublisherOA PDF

Recent grants

• An Approach to Robust Performance Analysis Using Optimal Transport

  NSF · $240k · 2018–2021

• Collaborative Research: Modeling and Analyzing Extreme Risks in Insurance and Finance

  NSF · $138k · 2014–2017

• Collaborative Research: Perfect Simulation of Stochastic Networks

  NSF · $128k · 2015–2018

• Collaborative Research: CIF: Medium: Statistical and Algorithmic Foundations of Distributionally Robust Policy Learning

  NSF · $620k · 2023–2027

• Collaborative Proposal: Strong Stochastic Simulation of Stochastic Processes Theory and Applications

  NSF · $201k · 2018–2020

Frequent coauthors

• Peter W. Glynn

  66 shared

• Henry Lam

  29 shared

• Karthyek Murthy

  29 shared

• Bert Zwart

  29 shared

• Nian Si

  Hong Kong University of Science and Technology

  21 shared

• Viet Anh Nguyen

  20 shared

• Jingchen Liu

  Columbia University

  20 shared

• Yixi Shi

  Chinese Academy of Sciences

  19 shared

Education

• Ph.D., Statistics

  Harvard University

  2008

• B.S., Industrial Engineering and Operations Research, and Statistics

  Columbia University

  2017

Awards & honors

• 2010 Erlang Prize
• Presidential Early Career Award for Scientists and Engineers…
• Fellow of the Institute of Mathematical Statistics