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Austin Atsango

Austin is a PhD candidate in Chemistry. His research focuses on ​​developing and employing theoretical and algorithmic advances—including machine learning, multiple timescale techniques, and mean field methods that accelerate chemical simulations—to study molecular properties in the condensed phase. His projects center on elucidating the mechanism of proton transport in fuel cell electrolytes by conducting and analyzing molecular dynamics simulations, modeling the behavior of water at the liquid-vapor interface through active learning strategies, and developing techniques to simulate electronic excitation dynamics in open quantum systems. Outside of the lab, Austin enjoys running, hiking, reading fiction, and exploring nightlife.