Surag Nair
Senior ML Scientist at Genentech
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSurag Nair is a Principal ML Scientist with 11 years of experience applying machine learning to genomics, therapeutic design, and agentic AI evaluation, currently leading ML efforts at Genentech. He holds a PhD and MS in Computer Science from Stanford and a BE in Electrical Engineering from IIT Delhi, blending deep academic rigor with industry impact. His work spans DNA/mRNA therapeutic optimization, multi-omics integration for target discovery, and benchmarking agentic AI in computational biology. On GitHub he’s contributed production-ready implementations of AlphaZero and translated CS230 code examples into PyTorch/TensorFlow, reflecting a strong foundation in both research and reproducible engineering. Colleagues value his ability to bridge algorithmic innovation with practical model training and evaluation pipelines that move biology-focused ML from prototype to practice.
10 years of coding experience
Indian Institute of Technology Delhi (IIT Delhi)Doctor of Philosophy Computer Science, Doctor of Philosophy Computer Science at Stanford University
Github Skills (13)
neural-network10
pytorch10
tensorflow10
python10
reinforcement-learning10
deeplearning-ai9
machine-learning9
deep-learning9
computer-vision9
faster-rcnn8
keras8
mask-rcnn8
natural-language-processing8
Programming languages (10)
Github contributions (5)
suragnair/alpha-zero-general
Nov 2017 - Jan 2023
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
Role in this project:
Back-end Developer
Back-end DeveloperContributions:110 commits, 64 PRs, 113 pushes in 5 years 2 months
Contributions summary:Surag primarily contributed to the implementation of core components of an AlphaZero implementation, focusing on the neural network and game logic elements. Their work involved creating the foundational classes for different game-related components, including the `Game`, `Coach`, `Arena`, and `NNet` files, indicating a focus on the underlying algorithmic architecture. The user subsequently updated and debugged the neural network components and integrated them with the MCTS algorithm.
alphagoin-gamealpha-zerogomokutensorflow
cs230-stanford/cs230-code-examples
Jan 2018 - Feb 2018
Code examples in pyTorch and Tensorflow for CS230
Role in this project:ML Engineer
ML EngineerContributions:42 commits, 2 PRs, 22 pushes in 1 month
Contributions summary:Surag focused on translating and implementing code examples for training and evaluation in PyTorch and TensorFlow within the context of computer vision and natural language processing. Their contributions include modifications to training and evaluation scripts, the addition of a dropout layer, and the creation of data loaders. The user's work is directly related to model training and evaluation.
pytorchdeep-learningcomputer-visiontensorflownatural-language-processing
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