{"id":51055,"date":"2025-01-21T11:52:02","date_gmt":"2025-01-21T16:52:02","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=51055"},"modified":"2025-02-03T09:37:54","modified_gmt":"2025-02-03T14:37:54","slug":"ams-weekly-seminar-raul-astudio","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-raul-astudio\/","title":{"rendered":"AMS Weekly Seminar | Raul Astudillo"},"content":{"rendered":"

Location:\u00a0<\/strong>Gilman 50<\/p>\n

When:<\/strong>\u00a0February 6th at 1:30 p.m.<\/p>\n

Title:\u00a0<\/strong>Beyond the Black Box: Structure-Aware Bayesian Optimization for Efficient and Scalable Experimentation<\/span><\/p>\n

Abstract:\u00a0<\/strong>Experimentation is fundamental to scientific progress. However, the complexity of modern experimentation tasks surpasses the capabilities of trial-and-error approaches guided by human intuition. AI-driven frameworks for adaptive experimentation have emerged as powerful alternatives. Among them, Bayesian optimization\u2014a machine learning framework for optimizing expensive-to-evaluate objective functions\u2014has become one of the most successful, with applications ranging from hyperparameter tuning of deep neural networks to robot control.<\/p>\n

Traditionally, Bayesian optimization methods have been employed as black-box optimizers. While this approach has achieved significant success, it often falls short in complex applications such as materials design and drug discovery. In this talk, I will discuss how exploiting composite and network-like structures inherent to many experimentation tasks can dramatically improve the efficiency and scalability of Bayesian optimization methods, enabling breakthroughs across various fields. I will conclude by discussing several exciting directions for future work in the broader field of AI-driven decision-making and discovery.<\/p>\n

<\/div>\n
<\/div>\n
Bio:<\/strong>\u00a0<\/strong>Raul Astudillo is a Postdoctoral Scholar in the Department of Computing and Mathematical Sciences at Caltech, hosted by Professor Yisong Yue. His research focuses on developing algorithms for intelligent decision-making in complex, data-intensive environments, with applications in materials design, biotechnology, and other scientific domains. His contributions have been recognized with Rising Star honors in Management Science and Engineering (Stanford University) and Data Science (UChicago, UC San Diego). Raul earned a Ph.D. in Operations Research and Information Engineering from Cornell University, where he was advised by Professor Peter Frazier, and a <\/span>B.Sc<\/span>. in Mathematics from the University of Guanajuato and the Mexican Center for Research in Mathematics. During his Ph.D., he was also a Visiting Researcher with Meta\u2019s Adaptive Experimentation team.<\/span><\/div>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/93287142219?pwd=z9fqWnRMzmzS0SGijRiie5yN3kHRSZ.1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Location:\u00a0Gilman 50 When:\u00a0February 6th at 1:30 p.m. Title:\u00a0Beyond the Black Box: Structure-Aware Bayesian Optimization for Efficient and Scalable Experimentation Abstract:\u00a0Experimentation is fundamental to scientific progress. 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