Bharath received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He received his PhD in computer science from Stanford University where he studied the application of deep learning to problems in drug discovery. At Stanford, Bharath created the deepchem.io open-source project to grow the deep drug discovery community, co-created the moleculenet.ai benchmark suite to facilitate development of molecular algorithms, and more. Bharath’s graduate education was supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences. Bharath founded Deep Forest Sciences to accelerate the discovery and design of small molecules and materials using AI. Bharath is also the lead author of “TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning”, a developer’s introduction to modern machine learning, with O’Reilly Media, and the lead author of “Deep Learning for the Life Sciences”. He is also working on a forthcoming book about DeepChem.