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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. Before 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 research interests lie at the intersection between operations research and machine learning, with an emphasis on Bayesian methods for efficient sequential decision making. My dissertation focused on the design and analysis of Bayesian optimization algorithms for problems exhibiting a nested structure. These algorithms have found application in risk-averse simulation optimization of COVID-19 testing policies, multi-objective portfolio optimization with user preferences, and reinforcement-learning-based robot control.

In Fall 2022, I will join the Computing + Mathematical Sciences Department at Caltech as a postdoctoral researcher hosted by Yisong Yue.

Interests

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

Education

  • Ph.D., Operations Research and Information Engineering, May 2022 (Expected)

    Cornell University

  • B.S., Mathematics, 2016

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

Publications

(2021). Thinking Inside the Box: A Tutorial on Grey-Box Bayesian Optimization. Proceedings of the 2021 Winter Simulation Conference.

PDF

(2021). Bayesian Optimization of Function Networks. Advances in Neural Information Processing Systems.

(2020). Bayesian Optimization of Risk Measures. Advances in Neural Information Processing Systems.

PDF

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

PDF

Talks

Bayesian Optimization of Function Networks
Bayesian Optimization of Function Networks
Bayesian Optimization of Composite Functions
Bayesian Optimization of Composite Functions

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.