Peter I. Frazier
Associate Professor, Cornell University
School of Operations Research
and Information Engineering
Staff Data Scientist, Uber
I work at the intersection between operations research and machine learning.
Half of my time is spent working for Uber as a Staff Data Scientist, and the other half for Cornell as an Associate Professor. I am based in Ithaca, NY.
Bayesian Optimization for Problems with Exotic Structure
- Cornell Center for Applied Math Colloquium, Mar 2018.
Knowledge Gradient Methods for Bayesian Optimization
- NIPS Bayesian Optimization Workshop, Dec 2017.
- MIT, OR Center, Feb 2016.
Parallel Bayesian Global Optimization of Expensive Functions, for Metrics Optimization at Yelp
- Carnegie Mellon, Machine Learning Department, Feb 2015.
Optimal Learning for Molecular Discovery
- Cornell Center for Applied Math, Oct 2014.
- For others, see the Presentations page. For a complete list, see my CV.
J. Wu, M.U. Poloczek, A.G. Wilson, P.I. Frazier,
Bayesian Optimization with Gradients,
NIPS 2017 (oral presentation)
M.U. Poloczek, J. Wang, P.I. Frazier,
Multi-Information Source Optimization NIPS 2017 (spotlight presentation)
B. Chen, P.I. Frazier,
Dueling Bandits with Weak Regret,
Wu, J., Frazier
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization,
Frazier, D. Kempe, J. Kleinberg, R. Kleinberg,
Economics and Computation, 2014 (best paper award)
A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection,
Operations Research, 2014 (JFIG paper competition finalist)
J. Xie & Frazier,
Sequential Bayes-Optimal Policies for Multiple Comparisons with a Control,
Operations Research, 2013 (ICS student paper prize)
Frazier, W.B. Powell & S. Dayanik,
A Knowledge-Gradient Policy for Sequential Information Collection,
SIAM Journal on Control and Optimization, 2008
(DA society student paper competition finalist)
- For a full list see the
or my CV.