His current research interests span algorithms, statistics, and machine learning, with an emphasis on developing algorithms and information theoretic lower bounds for a variety of fundamental data-centric tasks. Recently, this work has also included questions of how to robustly extract meaningful information from untrusted datasets that might contain a significant fraction of corrupted or arbitrarily biased data points. Prior to joining Stanford, Gregory completed his PhD at UC Berkeley in 2012, and was a postdoctoral researcher at Microsoft Research, New England. He has received several honors, including the ACM Dissertation Award Honorable Mention, NSF Career Award, and Sloan Foundation Fellowship.