Akshay Balsubramani - Lead at VAC

Akshay Balsubramani

Lead at VAC

San Francisco Bay Area United States

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Summary

👤

Senior

🎓

Top School

Akshay Balsubramani is a lead AI scientist and entrepreneur in the San Francisco Bay Area with 11 years of experience applying machine learning to drug discovery, genomics, and chemistry. He builds bespoke AI agents and tooling that let interdisciplinary teams—chemists, biologists, and executives—interact with data in real time to accelerate decisions across discovery-to-clinic pipelines. His work spans fundamental ML research (PhD-level theory and single-cell genomics at Stanford) to productized impact, including saving 40% of reported leads in a late-stage protein campaign and launching multiple monoclonal antibody programs. At VAC and as a consultant to venture and pharma, he’s combined generative models, ADMET/synthesis prediction tools, and visualization systems to scale team workflows. He also contributes code to influential open benchmarks like WILDS, adding models and data loaders for genomics tasks, reflecting a commitment to robust, reproducible ML for real-world distribution shifts. Akshay’s background blends rigorous theory, hands-on engineering, and domain fluency in biology—enabling unusual velocity from prototype models to experimental proof-of-concept.

11 years of coding experience

11 years of employment as a software developer

EECS, EECS at University of California, Berkeley

University of California, San Diego

Stackoverflow

Stats

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Github Skills (11)

data-preprocessing10

data-loading10

pytorch10

machine-learning10

deep-learning10

dataprep10

preprocessing10

python10

load-data10

preprocess10

tensorflow4

Programming languages (4)

TeXJupyter NotebookRubyPython

Github contributions (5)

p-lambda/wilds

Feb 2021 - Jul 2021

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

Role in this project:

ML Engineer

Contributions:112 commits, 76 pushes, 3 branches in 5 months

Contributions summary:Akshay implemented and added new models and data loaders to the `wilds` repository, a machine learning benchmark for in-the-wild distribution shifts. They added rough cut models and data, including files for specific datasets like `EncodeTFBSDataset` and model files such as `CNN_genome.py`. Their work involved modifying existing code files to integrate new models and improve the dataset fetching and preprocessing functionalities of the project.

loadersshiftswildmachine-learningmachine-learning-benchmark

b-akshay/pc-autoencoder

Nov 2016 - Feb 2022

Contributions:12 commits, 9 pushes, 2 branches in 5 years 4 months

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