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110 SW Park Terrace, Corvallis, OR 97331

Abstract
A huge range of scientific discovery and engineering design problems ranging from materials discovery and drug design to 3D printing and chip design can be formulated as the following general problem: adaptive optimization of complex design spaces guided by expensive experiments where expense is measured in terms of resources consumed by the experiments. For example, searching the space of materials for a desired property while minimizing the total resource-cost of physical lab experiments for their evaluation. The key challenge is how to select the sequence of experiments to uncover high-quality solutions for a given resource budget. In this talk, I will introduce novel adaptive experiment design algorithms to tackle this challenge. This includes both designing new predictive models over combinatorial objects (sequences, graphs, permutations, etc.) that work well in small data settings and provide principled uncertainty quantification and decision policies to carefully select each experiment for complex real-world settings where there are usually multiple objectives, multi-fidelity experiments, and black-box constraints. I will also present results on applying these algorithms to solve high-impact science and engineering applications in domains including nanoporous materials discovery, electronic design automation, and additive manufacturing. In the end, I will also cover some open challenges and future directions that I am excited about.

Speaker Biography
Aryan Deshwal is an Assistant Professor in the Department of Computer Science and Engineering at University of Minnesota. His research agenda is AI to Accelerate Scientific Discovery and Engineering Design where he focuses on advancing foundations of AI/ML to solve challenging real-world problems with high societal impact in collaboration with domain experts. He won the College of Engineering Outstanding Dissertation Award for his PhD at Washington State University. He was selected for Rising Stars in AI by KAUST AI Initiative (2023) and won multiple outstanding reviewer awards from machine learning (ICML (2020), ICLR (2021), and ICML (2021)) conferences.

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