Peter Frazier received a B.S. in Physics and Engineering/Applied Science from the California Institute of Technology in 2000, after which he spent several years in industry as a software engineer, working for two different start-up companies and for the Teradata division of NCR. In 2005, he entered graduate school in the Department of Operations Research & Financial Engineering at Princeton University, and received an M.A. in 2007 and a Ph.D. in 2009. He joined the faculty at Cornell in 2009 as an Assistant Professor in the School of Operations Research & Information Engineering. His research is in sequential decision¬making under uncertainty, optimal methods for collecting information, and machine learning, focusing on applications in simulation, e-commerce, medicine, and biology. He is the recipient of a CAREER Award from the National Science Foundation and a Young Investigator Award from the Air Force Office of Scientific Research. He is currently on sabbatical leave at Uber, where he is the technical lead for data science on UberPool.
Professor Frazier works in sequential decision-making under uncertainty and machine learning, focusing on problems where information is acquired over time. Behaving optimally in such problems is also known as optimal learning. He works on applications in simulation, e-commerce, medicine, and biology. Within simulation, he views the design of simulation optimization algorithms as an optimal learning problem, and is developing new simulation optimization algorithms with optimal average-case performance. This work uses Bayesian statistics and dynamic programming to make better decisions about which simulations we should perform, to solve simulation optimization problems more quickly.
Professor Frazier teaches courses in simulation and statistics, including ORIE 5582 (Monte Carlo Methods for Financial Engineering), ORIE 6580 (Simulation), and ORIE 3120 (Industrial Data and Systems Analysis, co-taught with Peter Jackson). He also teaches ORIE 3800 (Information Systems and Analysis) and ORIE 6750 (Optimal Learning), which teach students how to use methods from operations research to analyze and improve how we acquire information, and to understand how the dynamics of information acquisition influence society. Professor Frazier won the Sonny Yau '72 Excellence in Teaching Award in both 2011 and 2014, and in 2011 the MEng team that he advised won the Silent Hoist and Crane Award for their work with Walmart.com.
Professor Frazier is an associate editor for Operations Research, ACM Transactions on Modeling and Computer Simulation, and IIE Transactions. He is active within INFORMS societies, currently serving as the Secretary of the INFORMS Simulation Society, and having co-organized the Applied Probability Cluster for the 2014 INFORMS Annual Meeting. He is also active within the machine learning community, serving on program committees for NIPS, ICML, and AISTATS.
- 2008. "A Knowledge-Gradient Policy for Sequential Information Collection." SIAM Journal on Control and Optimization 47 (5): 2410-2439. .
- 2009. "The Knowledge-Gradient Policy for Correlated Normal Rewards." Journal on Computing (INFORMS) 21: 599-613. .
- 2012. "The Knowledge-Gradient Algorithm for a General Class of Online Learning Problems." Operations Research 60 (1): 180-195. .
- 2014. "Incentivizing Exploration." Paper presented at 15th ACM Conference on Economics and Computation, June. .
- 2016. "Coupled bisection for root ordering." Operations Research Letters 44 (2): 165-169. .
Selected Awards and Honors
- Best Paper Award, "Incentivizing Exploration" (15th ACM Conference on Economics and Computation) 2014
- Finalist, Junior Faculty Interest Group (JFIG) Paper Competition, for P.I. Frazier, " A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection" (INFORMS) 2013
- Computing Society Student Paper Prize for J. Xie, P.I. Frazier, "Sequential Bayes-Optimal Policies for Multiple Comparisons with a Control" (INFORMS) 2013
- CAREER Award, for the proposal "Methodology for Optimization via Simulation: Bayesian Methods, Frequentist Guarantees, and Applications to Cardiovascular Medicine" (National Science Foundation) 2012
- Young Investigators Research Program Award (Air Force Office of Scientific Research) 2011
- BS (Physics, Engineering and Applied Sciences), California Institute of Technology, 2000
- MA (Operations Research and Financial Engineering), Princeton University, 2007
- Ph D (Operations Research and Financial Engineering), Princeton University, 2009