AIM-SI Tutorial

AIM-SI Tutorial Series in AI: A Tutorial on AI-Guided Experimental Design and Bayesian Optimization - Geoff Pleiss

Join us for the next installment of the Faculty of Science AI-SI Summer Tutorial Series in AI: "A Tutorial on AI-Guided Experimental Design and Bayesian Optimization" by Dr. Geoff Pleiss from the UBC Department of Statistics.  These events are open to all Science faculty, postdoctoral fellows, and graduate students wishing to learn more about artificial intelligence and its potential applicability to their work. 

Abstract

This tutorial will cover how AI-driven methods for experimental design can accelerate scientific discovery. We will introduce techniques from Bayesian optimization, a framework that uses machine learning models to propose promising sequences of future experiments while simultaneously learning from prior experiments to make increasingly informed decisions. We will discuss recent successes in domains that necessitate efficient experimental exploration, including materials science, drug discovery, and engineering. The tutorial will also discuss accessible software tools and cutting-edge methods for incorporating knowledge from literature using AI. No prior AI experience required.

 

Please Register Here

These talks are an opportunity to hear from experts across the Faculty of Science who are working in different areas across the field of artificial intelligence.  More events will be scheduled in the coming year, so please keep your eye on the CAIDA Events Page.