I am a theoretical physicist exploring learning in physical systems, particularly in the context of mechanics and physically inspired learning rules. My research at the University of Pennsylvania (Liu and Balasubramanian groups) and formerly at the University of Chicago (Murugan group) established such learning rules for flow networks, elastic networks and self-folding origami, highlighting their potential as designer multi-functional, dynamically controlled metamaterials. I am specifically interested in the analogy between learning in mechanical networks and the underlying frameworks of artificial neural networks and machine learning in general. This fundamental connection suggests the use of physically inspired systems as machine learning algorithms with novel properties.In earlier work I studied dynamics of dense granular suspensions (Zhang & Jaeger groups, UChicago), isobaric ensembles of supercooled liquid models, self-avoiding random walks and atmospheric modeling (Eisenberg group, TAU).
Listed skills include Physics, Condensed Matter Physics, Statistical Mechanics, Computational Physics, and 18 others.