Bio


Jose Blanchet is a Professor of Management Science and Engineering (MS&E) at Stanford. Prior to joining MS&E, he was a professor at Columbia (Industrial Engineering and Operations Research, and Statistics, 2008-2017), and before that he taught at Harvard (Statistics, 2004-2008). Jose is a recipient of the 2010 Erlang Prize and several best publication awards in areas such as applied probability, simulation, operations management, and revenue management. He also received a Presidential Early Career Award for Scientists and Engineers in 2010. He worked as an analyst in Protego Financial Advisors, a leading investment bank in Mexico. He has research interests in applied probability and Monte Carlo methods. He is the Area Editor of Stochastic Models in Mathematics of Operations Research. He has served on the editorial board of Advances in Applied Probability, Bernoulli, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Queueing Systems: Theory and Applications, and Stochastic Systems, among others.

Honors & Awards


  • Biennial Best Publication Award in Applied Probability Award, INFORMS Applied Probability Society (2023)
  • Outstanding Simulation Publication Award, INFORMS Simulation Society (2021)
  • Revenue Management and Pricing Section Best Publication Award, INFORMS Revenue Management and Pricing Section (2021)
  • Best Contributed Theory Paper Award for the Winter Simulation Conference, INFORMS Simulation Society (2019)
  • Best OM Paper in Operations Research, INFORMS MSOM Society (2019)
  • William H. Keck Scholar, Stanford University (2017)
  • Professional Merit Award, ITAM, Mexico (2012)
  • Distinguished Alumni Scholar Lecture, Stanford University (2012)
  • Kavli Fellow, Kavli Foundation and National Academies (2013)
  • Biennial Best Publication in Applied Probability Award, INFORMS Applied Probability Society (2009)
  • Erlang Prize, INFORMS Applied Probability Society (2010)
  • NSF Career Award, NSF (2008)
  • Presidential Early Career Awards for Scientists and Engineers, White House (2009)

Boards, Advisory Committees, Professional Organizations


  • Area Editor, Mathematics of Operations Research (2020 - Present)
  • President, INFORMS Applied Probability Society (2020 - 2022)

Professional Education


  • PhD, Stanford University, Management Science and Engineering (2004)
  • MS, Stanford University, Engineering Economic Systems and Operations Research (2002)
  • Licenciado, Instituto Technologico Autonomo de Mexico, Applied Mathematics (2000)
  • Licenciado, Instituto Technologico Autonomo de Mexico, Actuarial Science (2000)

2023-24 Courses


Stanford Advisees


All Publications


  • Modeling shortest paths in polymeric networks using spatial branching processes JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS Zhang, Z., Mohanty, S., Blanchet, J., Cai, W. 2024; 187
  • Sample-Path Large Deviations for Unbounded Additive Functionals of the Reflected Random Walk MATHEMATICS OF OPERATIONS RESEARCH Bazhba, M., Blanchet, J., Rhee, C., Zwart, B. 2024
  • Is a high-throughput experimental dataset large enough to accurately estimate a statistic? JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS Zhou, Y., Lin, S., Zhang, X., Wu, H., Blanchet, J., Suo, Z., Lu, T. 2024; 183
  • Towards optimal running timesfor optimal transport OPERATIONS RESEARCH LETTERS Blanchet, J., Jambulapati, A., Kent, C., Sidford, A. 2024; 52
  • Unbiased Optimal Stopping via the MUSE STOCHASTIC PROCESSES AND THEIR APPLICATIONS Zhou, Z., Wang, G., Blanchet, J. H., Glynn, P. W. 2023; 166
  • Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press. Health care management science Shi, Y., Mahdian, S., Blanchet, J., Glynn, P., Shin, A. Y., Scheinker, D. 2023

    Abstract

    Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.

    View details for DOI 10.1007/s10729-023-09649-0

    View details for PubMedID 37665543

  • Neural network accelerated process design of polycrystalline microstructures MATERIALS TODAY COMMUNICATIONS Lin, J., Hasan, M., Acar, P., Blanchet, J., Tarokh, V. 2023; 36
  • Distributionally Robust Batch Contextual Bandits MANAGEMENT SCIENCE Si, N., Zhang, F., Zhou, Z., Blanchet, J. 2023
  • Delay-Adaptive Learning in Generalized Linear Contextual Bandits MATHEMATICS OF OPERATIONS RESEARCH Blanchet, J., Xu, R., Zhou, Z. 2023
  • Dropout Training is Distributionally Robust Optimal JOURNAL OF MACHINE LEARNING RESEARCH Blanchet, J., Kang, Y., Olea, J., Nguyen, V., Zhang, X. 2023; 24
  • Some open problems in exact simulation of stochastic differential equations QUEUEING SYSTEMS Blanchet, J. H. 2022
  • Article High-throughput experiments for rare-event rupture of materials MATTER Zhou, Y., Zhang, X., Yang, M., Pan, Y., Du, Z., Blanchet, J., Suo, Z., Lu, T. 2022; 5 (2): 654-665
  • Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities OPERATIONS RESEARCH Blanchet, J. H., Reiman, M., Shah, V., Wein, L. M., Wu, L. 2022
  • Distributionally Robust Q-Learning Liu, Z., Bai, Q., Blanchet, J., Dong, P., Xu, W., Zhou, Z., Zhou, Z., Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022
  • HUMAN IMPERCEPTIBLE ATTACKS AND APPLICATIONS TO IMPROVE FAIRNESS Hua, X., Xu, H., Blanchet, J., Nguyen, V., IEEE IEEE. 2022: 2641-2652
  • No Weighted-Regret Learning in Adversarial Bandits with Delays JOURNAL OF MACHINE LEARNING RESEARCH Bistritz, I., Zhou, Z., Chen, X., Bambos, N., Blanchet, J. 2022; 23
  • Optimal Transport-Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes MATHEMATICS OF OPERATIONS RESEARCH Blanchet, J., Murthy, K., Zhang, F. 2021
  • Sample Out-of-Sample Inference Based on Wasserstein Distance OPERATIONS RESEARCH Blanchet, J., Kang, Y. 2021; 69 (3): 985-1013
  • Confidence regions in Wasserstein distributionally robust estimation BIOMETRIKA Blanchet, J., Murthy, K., Si, N. 2021
  • EXACT SIMULATION FOR MULTIVARIATE ITO DIFFUSIONS ADVANCES IN APPLIED PROBABILITY Blanchet, J., Zhang, F. 2020; 52 (4): 1003–34
  • SAMPLE PATH LARGE DEVIATIONS FOR LEVY PROCESSES AND RANDOM WALKS WITH WEIBULL INCREMENTS ANNALS OF APPLIED PROBABILITY Bazhba, M., Blanchet, J., Rhee, C., Zwart, B. 2020; 30 (6): 2695–2739

    View details for DOI 10.1214/20-AAP1570

    View details for Web of Science ID 000598914200006

  • On distributionally robust extreme value analysis EXTREMES Blanchet, J., He, F., Murthy, K. 2020; 23 (2): 317–47
  • Rates of Convergence to Stationarity for Reflected Brownian Motion MATHEMATICS OF OPERATIONS RESEARCH Blanchet, J., Chen, X. 2020; 45 (2): 660–81
  • A CLASS OF OPTIMAL TRANSPORT REGULARIZED FORMULATIONS WITH APPLICATIONS TO WASSERSTEIN GANS Mahdian, S., Blanchet, J. H., Glynn, P. W., IEEE IEEE. 2020: 433-444
  • EXACT SAMPLING FOR SOME MULTI-DIMENSIONAL QUEUEING MODELS WITH RENEWAL INPUT ADVANCES IN APPLIED PROBABILITY Blanchet, J., Pei, Y., Sigman, K. 2019; 51 (4): 1179–1208
  • Queue length asymptotics for the multiple-server queue with heavy-tailed Weibull service times QUEUEING SYSTEMS Bazhba, M., Blanchet, J., Rhee, C., Zwart, B. 2019
  • Optimal uncertainty size in distributionally robust inverse covariance estimation OPERATIONS RESEARCH LETTERS Blanchet, J., Si, N. 2019; 47 (6070): 618–21
  • On logarithmically optimal exact simulation of max-stable and related random fields on a compact set BERNOULLI Liu, Z., Blanchet, J. H., Dieker, A. B., Mikosch, T. 2019; 25 (4A): 2949–81

    View details for DOI 10.3150/18-BEJ1076

    View details for Web of Science ID 000485825100018

  • SAMPLE PATH LARGE DEVIATIONS FOR LEVY PROCESSES AND RANDOM WALKS WITH REGULARLY VARYING INCREMENTS ANNALS OF PROBABILITY Rhee, C., Blanchet, J., Zwart, B. 2019; 47 (6): 3551–3605

    View details for DOI 10.1214/18-AOP1319

    View details for Web of Science ID 000500553400003

  • ROBUST WASSERSTEIN PROFILE INFERENCE AND APPLICATIONS TO MACHINE LEARNING JOURNAL OF APPLIED PROBABILITY Blanchet, J., Kang, Y., Murthy, K. 2019; 56 (3): 830–57
  • Rare-Event Simulation for Distribution Networks OPERATIONS RESEARCH Blanchet, J., Li, J., Nakayama, M. K. 2019; 67 (5): 1383–96
  • Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Random Walks and Compound Poisson Processes MATHEMATICS OF OPERATIONS RESEARCH Chen, B., Blanchet, J., Rhee, C., Zwart, B. 2019; 44 (3): 919–42
  • Quantifying Distributional Model Risk via Optimal Transport MATHEMATICS OF OPERATIONS RESEARCH Blanchet, J., Murthy, K. 2019; 44 (2): 565–600
  • Perfect Sampling of Generalized Jackson Networks MATHEMATICS OF OPERATIONS RESEARCH Blanchet, J., Chen, X. 2019; 44 (2): 693–714
  • EXACT SAMPLING OF THE INFINITE HORIZON MAXIMUM OF A RANDOM WALK OVER A NONLINEAR BOUNDARY JOURNAL OF APPLIED PROBABILITY Blanchet, J., Dong, J., Liu, Z. 2019; 56 (1): 116–38

    View details for DOI 10.1017/jpr.2019.9

    View details for Web of Science ID 000475365600008

  • Robust Actuarial Risk Analysis NORTH AMERICAN ACTUARIAL JOURNAL Blanchet, J., Lam, H., Tang, Q., Yuan, Z. 2019; 23 (1): 33–63
  • Queueing Theory-Based Perspective of the Kinetics of "Channeled" Enzyme Cascade Reactions ACS CATALYSIS Tsitkov, S., Pesenti, T., Palacci, H., Blanchet, J., Hess, H. 2018; 8 (11): 10721–31
  • Perfect sampling of GI/GI/c queues QUEUEING SYSTEMS Blanchet, J., Dong, J., Pei, Y. 2018; 90 (1-2): 1–33
  • EXACT SIMULATION OF MULTIDIMENSIONAL REFLECTED BROWNIAN MOTION JOURNAL OF APPLIED PROBABILITY Blanchet, J., Murthy, K. 2018; 55 (1): 137–56
  • ANALYSIS OF A STOCHASTIC APPROXIMATION ALGORITHM FOR COMPUTING QUASI-STATIONARY DISTRIBUTIONS ADVANCES IN APPLIED PROBABILITY Blanchet, J., Glynn, P., Zheng, S. 2016; 48 (3): 792-811
  • Affine Point Processes: Approximation and Efficient Simulation MATHEMATICS OF OPERATIONS RESEARCH Zhang, X., Blanchet, J., Giesecke, K., Glynn, P. W. 2015; 40 (4): 797-819
  • UNBIASED MONTE CARLO COMPUTATION OF SMOOTH FUNCTIONS OF EXPECTATIONS VIA TAYLOR EXPANSIONS Blanchet, J. H., Chen, N., Glynn, P. W., IEEE IEEE. 2015: 360–67
  • Large deviations for the empirical mean of an queue QUEUEING SYSTEMS Blanchet, J., Glynn, P., Meyn, S. 2013; 73 (4): 425-446
  • Empirical Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary Distributions EVOLVE 2012 International Conference Blanchet, J., Glynn, P., Zheng, S. SPRINGER-VERLAG BERLIN. 2013: 19–37
  • On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION Blanchet, J., Glynn, P., Leder, K. 2012; 22 (3)
  • ON THE TRANSITION FROM HEAVY TRAFFIC TO HEAVY TAILS FOR THE M/G/1 QUEUE: THE REGULARLY VARYING CASE ANNALS OF APPLIED PROBABILITY Olvera-Cravioto, M., Blanchet, J., Glynn, P. 2011; 21 (2): 645-668

    View details for DOI 10.1214/10-AAP707

    View details for Web of Science ID 000289268100007

  • Asymptotic Robustness of Estimators in Rare-Event Simulation ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION L'Ecuyer, P., Blanchet, J. H., Tuffin, B., Glynn, P. W. 2010; 20 (1)
  • Rare event simulation for a slotted time M/G/s model Conference on 100 Years of Queueing - Erlang Centennials Blanchet, J., Glynn, P., Lam, H. SPRINGER. 2009: 33–57
  • RARE EVENT SIMULATION FOR A GENERALIZED HAWKES PROCESS Winter Simulation Conference 2009 Zhang, X., Glynn, P. W., Giesecke, K., Blanchet, J. IEEE. 2009: 1271–1278
  • EFFICIENT RARE EVENT SIMULATION OF CONTINUOUS TIME MARKOVIAN PERPETUITIES Winter Simulation Conference 2009 Blanchet, J., Glynn, P. IEEE. 2009: 405–412
  • Efficient Simulation of Light-Tailed Sums: an Old-Folk Song Sung to a Faster New Tune ... 8th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC 08) Blanchet, J. H., Leder, K., Glynn, P. W. SPRINGER-VERLAG BERLIN. 2009: 227–248
  • Efficient rare-event simulation for the maximum of heavy-tailed random walks ANNALS OF APPLIED PROBABILITY Blanchet, J., Glynn, P. 2008; 18 (4): 1351-1378

    View details for DOI 10.1214/07-AAP485

    View details for Web of Science ID 000258418800003

  • Uniform renewal theory with applications to expansions of random geometric sums ADVANCES IN APPLIED PROBABILITY Blanchet, J., Glynn, P. 2007; 39 (4): 1070-1097
  • Fluid heuristics, Lyapunov bounds and efficient importance sampling for a heavy-tailed G/G/1 queue QUEUEING SYSTEMS Blanchet, J., Glynn, P., Liu, C. 2007; 57 (2-3): 99-113
  • Efficient suboptimal rare-event simulation 2007 Winter Simulation Conference Zhang, X., Blanchet, J., Glynn, P. W. IEEE. 2007: 368–373
  • Complete corrected diffusion approximations for the maximum of a random walk ANNALS OF APPLIED PROBABILITY Blanchet, J., Glynn, P. 2006; 16 (2): 951-983