Christopher Tosh studies theoretical machine learning with Daniel Hsu. His postdoctoral research centers on deriving rigorous guarantees for learning algorithms and representations. His current interests include the representational capabilities of fly olfaction, the design of automated-experimentation algorithms for cancer drug discovery, and the underlying structure of modern artificial neural-network representations. He is a National Science Foundation Graduate Research Fellow, and was the Williams Scholar at the University of Texas in the Department of Mathematics. He holds a Ph.D. in computer science from the University of California, San Diego.