Timezone: »
Author Information
Sait Cakmak (Georgia Institute of Technology)
Raul Astudillo (Cornell University)
I am a Postdoctoral Scholar in the Department of Computing and Mathematical Sciences at Caltech, hosted by Professor Yisong Yue. I earned my Ph.D. in Operations Research and Information Engineering from Cornell University, where I worked with Professor Peter Frazier. Before that, I completed the undergraduate program in Mathematics offered jointly by the University of Guanajuato and the Center for Research in Mathematics. In 2021, I was a Visiting Researcher at Meta within the Adaptive Experimentation team led by Eytan Bakshy. My research interests lie at the intersection between operations research and machine learning, with a focus on algorithms for efficient sequential decision-making under uncertainty. More specifically, my work combines rigorous decision-theoretic foundations with sophisticated machine learning tools to develop modern adaptive experimentation methods. These methods have found application in cellular agriculture, materials design, and protein engineering.
Peter Frazier (Cornell / Uber)
Peter Frazier is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University, and a Staff Data Scientist at Uber. He received a Ph.D. in Operations Research and Financial Engineering from Princeton University in 2009. His research is at the intersection of machine learning and operations research, focusing on Bayesian optimization, multi-armed bandits, active learning, and Bayesian nonparametric statistics. He is an associate editor for Operations Research, ACM TOMACS, and IISE Transactions, and is the recipient of an AFOSR Young Investigator Award and an NSF CAREER Award.
Enlu Zhou (Georgia Institute of Technology)
More from the Same Authors
-
2023 : Provably-Convergent Bayesian Source Seeking for Multimodal Fields using Mobile Agents »
Vivek Mishra · Raul Astudillo · Peter Frazier · Fumin Zhang -
2023 Poster: Bayesian Risk-Averse Q-Learning with Streaming Observations »
Yuhao Wang · Enlu Zhou -
2022 Poster: Bayesian Risk Markov Decision Processes »
Yifan Lin · Yuxuan Ren · Enlu Zhou -
2021 Poster: Constrained Two-step Look-Ahead Bayesian Optimization »
Yunxiang Zhang · Xiangyu Zhang · Peter Frazier -
2021 Poster: Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs »
Raul Astudillo · Daniel Jiang · Maximilian Balandat · Eytan Bakshy · Peter Frazier -
2021 Poster: Bayesian Optimization of Function Networks »
Raul Astudillo · Peter Frazier -
2019 Poster: Towards Understanding the Importance of Shortcut Connections in Residual Networks »
Tianyi Liu · Minshuo Chen · Mo Zhou · Simon Du · Enlu Zhou · Tuo Zhao -
2019 Poster: Practical Two-Step Lookahead Bayesian Optimization »
Jian Wu · Peter Frazier -
2018 Poster: Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization »
Tianyi Liu · Shiyang Li · Jianping Shi · Enlu Zhou · Tuo Zhao -
2017 : Multi-Attribute Bayesian Optimization under Utility Uncertainty »
Raul Astudillo -
2017 : Invited talk: Knowledge Gradient Methods for Bayesian Optimization »
Peter Frazier -
2017 Poster: Multi-Information Source Optimization »
Matthias Poloczek · Jialei Wang · Peter Frazier -
2017 Spotlight: Multi-Information Source Optimization »
Matthias Poloczek · Jialei Wang · Peter Frazier -
2017 Poster: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier -
2017 Oral: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier