Bharath received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He did his PhD in computer science at 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 open source 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 is 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. Today, Bharath is focused on designing the decentralized protocols that will unlock data and AI to create the next stage of the internet.
- A PhD in Snapshots
- The Advent of Huang’s Law
- Why Blockchain Could (One Day) Topple Google
- Learning to Learn
- What is Ethereum?
- Liquidity in Drug Discovery
- The Innovator’s Open Source Dilemma
- Building The Open Source Drug Discovery Ecosystem
- What Can’t Deep Learning Do?
- Machine Learning With Small Data
- Software Patents Are Obsolete in the Age of AI
- தமிழில் விஞ்ஞானத்தை வளர்ப்போம்! (Science for all languages!)
- A Short Overview of Drug Discovery
- Why Should Drug Discovery Be Open Source?
- Software Is Eating Science
- Can AI Believe in God? A Parable about Diversity
- Learning Models of Disease
- The Ferocious Complexity of the Cell
- Why Antibiotics Are Hard
- Machine Learning For Scientific Datasets
- Massively multitask networks for drug discovery
- Low data drug discovery with one-shot learning
- NVMKV: A Scalable and Lightweight Flash Aware Key-Value Store.
- MoleculeNet: a benchmark for molecular machine learning
- Atomic convolutional networks for predicting protein-ligand binding affinity
- Computational modeling of β-secretase 1 (BACE-1) inhibitors using ligand based approaches
- Understanding protein dynamics with L1-regularized reversible hidden Markov models
- Is multitask deep learning practical for pharma?
- Retrosynthetic reaction prediction using neural sequence-to-sequence models
- The extended parameter filter
- Dynamic scaled sampling for deterministic constraints
- Spatial Graph Convolutions for Drug Discovery
- Learning Protein Dynamics with Metastable Switching Systems
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