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Raul Astudillo

Cornell University

I am a Ph.D. candidate in the School of Operations Research and Information Engineering at Cornell University, where I am fortunate to be advised by Professor Peter Frazier. Prior to coming to Cornell, I completed the undergraduate program in Mathematics offered jointly by the University of Guanajuato and the Center for Research in Mathematics.

My current research focuses on the design and analysis of Bayesian optimization algorithms for problems with a nested structure, which arise in calibration of expensive simulators, multi-objective optimization with preference information, and risk-averse simulation optimization.

Interests

  • Bayesian Optimization
  • Preference Learning
  • Simulation Optimization
  • Adaptive Experimentation
  • Optimal Learning

Education

  • Ph.D., Operations Research and Information Engineering, 2021 (Expected)

    Cornell University

  • B.S., Mathematics, 2016

    University of Guanajuato & Center for Research in Mathematics (Mexico)

Publications

Raul Astudillo, Daniel R Jiang, Maximilian Balandat, Peter I Frazier, Eytan Bakshy (2021). Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. Advances in Neural Information Processing Systems.

Raul Astudillo, Peter I Frazier (2021). Bayesian Optimization of Function Networks. Advances in Neural Information Processing Systems.

Sait Cakmak, Raul Astudillo, Peter I Frazier, Enlu Zhou (2020). Bayesian Optimization of Risk Measures. Advances in Neural Information Processing Systems.

PDF

Raul Astudillo, Peter I Frazier (2020). Multi-Attribute Bayesian Optimization With Interactive Preference Learning. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics.

PDF

Raul Astudillo, Peter I Frazier (2019). Bayesian Optimization of Composite Functions. Proceedings of the 36th International Conference on Machine Learning.

PDF

See all publications

Talks

Bayesian Optimization of Function Networks
Mar 4, 2021 9:45 AM — 10:00 AM Virtual
Bayesian Optimization of Function Networks
Nov 10, 2020 12:30 PM — 12:45 PM Virtual
Bayesian Optimization of Composite Functions
Dec 8, 2019 3:30 PM — 5:20 PM Gaylord National Resort & Conference Center
Bayesian Optimization of Composite Functions With Application to Computationally Expensive Inverse Problems
Jul 4, 2019 4:00 PM — 4:30 PM University of Grenoble Alpes
Bayesian Optimization of Composite Functions
Jun 12, 2019 5:05 PM — 5:10 PM Room 101, Long Beach Convention & Entertainment Center
See all talks

Industry Experience

 
 
 
 
 

Visiting Researcher

Facebook

Oct 2020 – Mar 2021 Menlo Park, CA
Developed novel Bayesian optimization algorithms for problems with unknown evaluation costs.
 
 
 
 
 

Research Intern

Facebook

Jun 2020 – Sep 2020 Menlo Park, CA
Developed novel Bayesian optimization algorithms for problems with unknown evaluation costs.
 
 
 
 
 

Research Intern

ExxonMobil Upstream Research Company

Jul 2019 – Aug 2019 Houston, TX
Developed novel Bayesian optimization algorithms for improving reservoir development planning under geological uncertainty.
 
 
 
 
 

Research Intern

ExxonMobil Upstream Research Company

May 2018 – Aug 2018 Houston, TX
Developed novel Bayesian optimization algorithms for improving reservoir development planning under geological uncertainty.