Raul Astudillo Contact and citizenship information Address: Rhodes Hall 288, Cornell University, Ithaca, NY 14850 Email: ra598@cornell.edu Website: https://raulastudillo.netlify.com/ Citizenship: Mexico Education 2016-Present Cornell University, USA. Ph.D. in Operations Research and Information Engineering { Expected graduation date: May 2022 { Advisor: Peter I. Frazier { Minors: Computer Science and Statistics 2011-2016 University of Guanajuato, Mexico. B.S. in Mathematics. GPA: 9.7/10 { Highest GPA of the class 2011-2016 Research interests Bayesian Optimization, Preference Learning, Simulation Optimization, Adaptive Experimentation, Optimal Learning Publications and working papers 1. R. Astudillo, B. Sha, and P.I. Frazier, "Mixed integer linear programming under preference uncertainty " (A preliminary version of this work was fnalist at the 2020 INFORMS Undergraduate Operations Research Prize Competition), Working paper. 2. Z. Lin, R. Astudillo, P.I. Frazier, and E. Bakshy, "Eÿcient preference exploration for multi-objective Bayesian optimization", Submitted. 3. R. Astudillo, and P.I. Frazier, "Thinking inside the box: A tutorial on grey-box Bayesian optimization", Advanced Tutorial at the Winter Simulation Conference, 2021. 4. R. Astudillo, D.R. Jiang, M. Balandat, P.I. Frazier, and E. Bakshy, "Multi-step budgeted Bayesian optimization with unknown evaluation costs", Advances in Neural Information Processing Systems, 2021. 5. R. Astudillo and P.I. Frazier, "Bayesian optimization of function networks", Advances in Neural Information Processing Systems, 2021. 6. S. Cakmak, R. Astudillo, P.I. Frazier and E. Zhou, "Bayesian optimization of risk measures", Advances in Neural Information Processing Systems, 2020. 7. R. Astudillo and P.I. Frazier, "Multi-attribute Bayesian optimization with interactive preference learning", International Conference on Artifcial Intelligence and Statistics, 2020. 8. R. Astudillo and P.I. Frazier, "Bayesian Optimization of composite functions", International Conference on Machine Learning, 2019. 9. R. Astudillo and P.I. Frazier, "Multi-attribute Bayesian optimization under utility uncertainty", NIPS Workshop on Bayesian Optimization, 2017. Selected presentations Oct 2021 "Grey-box Bayesian optimization", Young Researchers Workshop, Cornell University’s School of ORIE, Ithaca, NY. Mar 2021 "Bayesian optimization of function networks", SIAM Conference on Computational Science and Engineering, Virtual. Feb 2020 "Interactive Bayesian optimization with uncertain preferences", Facebook Adaptive Experimentation Workshop, New York City, NY. Jul 2019 "Bayesian optimization of composite functions with application to computationally expensive inverse Problems", Applied Inverse Problems Conference, Grenoble, France. Jun 2019 "Bayesian optimization of composite functions", International Conference on Machine Learning, Long Beach, CA. May 2019 "Bayesian optimization of composite functions", 2nd Uber Science Symposium, San Francisco, CA. Nov 2018 "A utility uncertainty approach to multi-attribute Bayesian optimization", INFORMS Annual Meeting, Phoenix, AZ. Dec 2017 "Multi-attribute Bayesian optimization under utility uncertainty", NIPS Workshop on Bayesian Optimization, Long Beach, CA. (contributed poster) Selected graduate coursework { Applied Stochastic Processes { Bayesian Machine Learning { Mathematical Programming { Statistical Learning Theory { Bayesian Statistics and Data Analysis { Advanced Machine Learning { Numerical Methods for Data Science { Optimal Learning Industry experience Oct 2020 Facebook, Menlo Park, CA. -Mar 2021 Visiting Researcher { Developed novel non-myopic Bayesian optimization algorithms for problems with unknown evaluation costs and implemented them on Facebook’s adaptive experimentation pipeline Jun-Sep 2020 Facebook, Menlo Park, CA. Intern { Developed novel non-myopic Bayesian optimization algorithms for problems with unknown evaluation costs { Mentor: Daniel R. Jiang Jul-Aug 2019 ExxonMobil Upstream Research Company, Houston, TX. Intern { Developed novel Bayesian optimization algorithms for improving reservoir development planning under geological uncertainty { Mentors: Liz Curry and Xiao-Hui Wu Jun-Aug 2018 ExxonMobil Upstream Research Company, Houston, TX. Intern { Developed novel Bayesian optimization algorithms for improving reservoir development planning under geological uncertainty { Mentors: Damian Burch and Xiao-Hui Wu Teaching experience Cornell University, USA. Instructor Summer 2021 Engineering Stochastic Processes Undergraduate Cornell University, USA. Teaching Assistant Fall 2018 Statistical Principles Graduate Spring 2017 Engineering Stochastic Processes Undergraduate Fall 2016 Basic Probability and Statistics Undergraduate Center for Research in Mathematics (CIMAT), Mexico. Teaching Assistant Fall 2015 Measure Theory and Probability Graduate University of Guanajuato, Mexico. Teaching Assistant Spring 2015 Complex Analysis Undergraduate Fall 2014 Elementary Number Theory Undergraduate Selected awards 2021 Outstanding Reviewer Award 2015 Second Prize -XXII International Mathematics Competition for University Students (IMC), Blagoevgrad, Bulgaria. 2014 Third Prize -XXII International Mathematics Competition for University Students (IMC), Blagoevgrad, Bulgaria. 2014 Orgullo UG Academic Excellence Award -University of Guanajuato. 2012-2016 Academic Excellence Fellowship -Center for Research in Mathematics. Computer skills Development MATLAB, Python, R Tools Git, LATEX, Microsoft Oÿce Languages English (profcient), Spanish (native)