Professor Jose Blanchet is a faculty member both in the Department of Industrial Engineering and Operations Research, and the Department of Statistics at Columbia University. Prior to joining Columbia in 2008, Professor Blanchet was a faculty member in the Department of Statistics at Harvard University. He holds a Ph.D. in Management Science and Engineering from Stanford University; he graduated in 2004.
Professor Blanchet's research interest expands across various areas of applied probability and statistics. His work on Monte Carlo methods for rare events has received several publication awards, including the biennial 2009 Best Publication Award given by the Institute for Operations Research and Management Science (INFORMS) Applied Probability Society (APS), among others. In addition, he received the 2010 Erlang Prize, given every two years by INFORMS APS, and the Presidential Early Career Award for Scientists and Engineers from President Obama, also in 2010.
Professor Blanchet's research at the interface of optimal transport, distributionally robust optimization and statistics, has been able to unify many of the most popular machine learning algorithms and regularization under a fundamental principle called Robust Wasserstein Profile Inference. These techniques are currently being applied to various financial settings, including portfolio optimization and risk management.
Professor Blanchet serves in the editorial board of some of the main journals in stochastic operations research, including: Advances in Applied Probability, Journal of Applied Probability, Mathematics of Operations Research, QUESTA, and Stochastic Systems.