Sam Dillavou

Sam Dillavou is a postdoctoral researcher working with Douglas Durian and Andrea Liu at the University of Pennsylvania. He is an experimentalist interested in emergent behaviors of complex systems and ways in which machine learning (ML) can inform and support these endeavors. His projects include constructing analog electronic networks that learn as a consequence of physical dynamics and without any central control (“physical learning”) and probing driven, out of equilibrium systems like granular flows, where ML has been useful as an experimental guide. He completed his PhD in physics at Harvard University under the guidance of Shmuel Rubinstein, studying dynamics of static and sliding bodies and memory effects in disordered materials.