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AI-Driven Experimental Design for Accelerating Science and Engineering

Monday, March 6, 2023 - 12:15 to 13:15
Willie Neiswanger, Postdoctoral Scholar, Computer Science Department, Stanford University
Statistics Seminar
ESB 4192 / Zoom

To join via Zoom: To join this seminar virtually, please request Zoom connection details from headsec [at]

Title: AI-Driven Experimental Design for Accelerating Science and Engineering

Abstract: AI-driven experimental design methods have the potential to accelerate costly discovery and optimization tasks throughout science and engineering—from materials design and drug discovery to computer systems tuning and instrument control. These methods are promising as they provide the intelligent decision making needed for use in complex real-world problems where experiments are time-consuming or expensive, and efficiency is paramount. In the first part of my talk, I will discuss challenges that I encountered while applying these methods to new types of scientific optimization problems being pursued at national labs. I will then introduce an information-based framework for flexible experimental design, which overcomes these challenges by enabling easy customization to new problem settings. This framework is theoretically principled, and has been used by scientists for efficient materials synthesis and optimization in large scientific instruments. Along the way, I will discuss my vision for reliable systems that expand the scope of AI-driven experimental design and make it easier to use, so that it can be put in the hands of scientists, engineers, and other practitioners everywhere.