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, where he is now an Associate Professor. 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 leave at Uber, where he is a Staff Data Scientist and Data Science Manager. At Uber, he worked on UberPOOL from 2015-17, and on broader pricing efforts from 2016-17. He now leads a data science team focused on pricing.
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.
- Scientific Computing
- Complex Systems, Network Science and Computation
- Data Science
- Statistics and Machine Learning
- Applied Probability
- Data Mining
- Information Technology Modeling
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 was named a Merrill Scholar Most Inspiring Cornell Professor in 2015.
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, having served as the Secretary of the INFORMS Simulation Society from 2014 to 2016, Proceedings Editor for the 2016 Winter Simulation Conference, and co-organizer of 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.
- Peter I. Frazier. Grey-Box Bayesian Optimization for AutoML & More. ICML AutoML Workshop, June 2019
- Peter Frazier. Bayesian Optimization Tutorial. INFORMS Tutorials, November 2018.
- Peter Frazier. Bayesian Optimization for Materials Design and Drug Discovery. Michigan State University, Science on the Edge Seminar Series, October 2018.
- Peter I. Frazier. Knowledge Gradient Methods for Bayesian Optimization. NIPS Bayesian Optimization Workshop, December 2017.
- Peter Frazier, David Kempe, Bobby Kleinberg & Jon Kleinberg. Incentivizing Exploration. MIT, OR Center, February 2016.
Selected Awards and Honors
- Cornell College of Engineering Research Excellence Award, 2020
- 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
- B.S.(Physics, Engineering and Applied Sciences),California Institute of Technology,2000
- M.A.(Operations Research and Financial Engineering),Princeton University,2007
- Ph.D.(Operations Research and Financial Engineering),Princeton University,2009