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Raul Astudillo

Caltech

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. During my Ph.D., I spent six months as a Visiting Researcher at Meta, collaborating with the Adaptive the Adaptive Experimentation team led by Eytan Bakshy. Before joining Cornell, I completed the undergraduate program in Mathematics offered jointly by the University of Guanajuato and the Center for Research in Mathematics.

My research interests lie at the intersection between operations research and machine learning. By integrating tools from both fields, I develop algorithms that enable intelligent decision-making in complex environments where information is costly to gather or process. My work has found application in areas such as cellular agriculture, materials design, and protein engineering.

Honored to be named a Rising Star in Management Science and Engineering by Stanford University, and a Rising Star in Data Science by the UChicago and UC San Diego.

I am on the 2024-2025 academic job market!

News

Our paper Practical Bayesian algorithm execution via posterior sampling (full paper coming soon) has been accepted to NeurIPS 2024!

Our paper Cost-aware Bayesian optimization via the Pandora’s box Gittins index has been accepted to NeurIPS 2024!

Honored to be recognized as a Rising Star in Data Science by UChicago and UC San Diego.

Congratulations to my mentee, Chu Xin Cheng, on being named a finalist in the 2024 INFORMS Undergraduate Operations Research Prize Competition!

Our preprint Active learning-assisted directed evolution is now available.