Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design

Abstract

Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of hypotheses using expensive experiments in a resource-efficient manner. My research focuses on developing novel adaptive experimental design methods to address this broad challenge. Specifically, I develop new probabilistic modeling and decision making tools that operate in small data settings. These approaches have yielded substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs). This cover letter outlines key methods I have developed and their real-world sustainability applications in areas such as nano-porous materials discovery, hardware design, and additive manufacturing. Additionally, I highlight my initiatives to foster collaboration between Science/Engineering and AI communities.