Date: Friday, October 18, 2024
Time: 10:30 am - 11:30 am
Location: DC 1304
Title: Probabilistic Inference and Decision-Making with Foundation Models for Bayesian Optimization
Abstract: Probabilistic inference is a compelling framework for capturing our belief about an unknown given observations. Central in this paradigm are probabilistic models and approximate inference methods. The former models one’s prior belief and encodes the data, while the latter produces posterior distributions based on the former. Large-scale neural networks and foundation models present exciting opportunities to enhance probabilistic inference. However, their sheer size makes leveraging them in probabilistic modeling challenging. In this talk, I will discuss my recent work in developing efficient probabilistic models with large foundation models to improve decision-making in Bayesian optimization for materials discovery.
Bio: Agustinus Kristiadi is a postdoctoral fellow at the Vector Institute, working primarily with Alán Aspuru-Guzik and Pascal Poupart. He obtained his PhD from the University of Tuebingen in Germany, advised by Philipp Hennig and Matthias Hein. His research interests are in probabilistic inference with large-scale neural networks, decision-making under uncertainty, and their applications in broader scientific fields such as chemistry. His work has been recognized in the form of best PhD thesis award and multiple spotlight papers from flagship machine learning conferences. His contributions to the scientific society includementoring underrepresented students in Canada under the IBET PhD Project and actively contributing to the open-source community.