Google Research

Sam Greydanus


Sam is a graduate of Dartmouth College where he majored in Physics. Before joining Google Brain, he interned at CERN, Microsoft Azure, and the DARPA Explainable AI Project. Now he works at Brain as a 2018 AI Resident under the mentorship of Chris Olah and Justin Gilmer. His research goals are to advance the basic science of neural networks and communicate his work clearly. Sam's research centers around using memory-based deep learning models to generate sequences and policies. So far, he has used them to approximate the Enigma cipher, generate realistic handwriting, and visualize how reinforcement-learning agents play Atari games (a paper at ICML 2018). He also wrote an undergraduate thesis about using machine learning to find the ground states of multi-body quantum systems. You can find descriptions of these projects on his personal blog. Currently, Sam is working with the Distill team on a reinforcement learning themed project. He is also interested in how overparameterization affects learning and convergence. Sam is a co-organizer of the 2018 NIPS AI for Social Good workshop which focuses on social problems for which AI has the potential to offer meaningful solutions. He is passionate about reproducible research, science communication, and making AI more accessible to everyone.

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