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3262

Sanjukta Bhattacharya

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Sanjukta Bhattacharya
CEO & Co-Founder of
Atlas Discovery (2011)
About

Sanjukta Bhattacharya is Co-Founder at Atlas Discovery (YC S26), a TechBio startup building foundation models of patient drug response to predict clinical trial outcomes.

Before founding

Sanjukta studied AI at the University of Bristol as a PhD student and previously conducted independent research at Los Alamos National Laboratory (LANL) in New Mexico.

Founder Insights

She is deeply passionate about the intersection of network biology and AI architectures, with a focus on gene expression and discrete diffusion models for clinical prediction.

Experience

Atlas Discovery is backed by Y Combinator, Pear VC, and Glasswing Ventures, and has presented research at top conferences including ICLR, CSHL, and ICML.

Scale & Impact

The company's models have demonstrated 70-90% accuracy in separating drug responders from non-responders across Cancer and Autoimmune disease trials, representing over 10x improvement in accuracy while training on 50x less data.

3262

Sanjukta Bhattacharya

Atlas Discovery
Co-Founder
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Sanjukta Bhattacharya

Company(s)

Atlas Discovery

Industry

Healthtech & Biotech

Stage/Funding

N/A

Company Location

us

Connect

About

Sanjukta Bhattacharya is Co-Founder at Atlas Discovery (YC S26), a TechBio startup building foundation models of patient drug response to predict clinical trial outcomes.

Before founding

Sanjukta studied AI at the University of Bristol as a PhD student and previously conducted independent research at Los Alamos National Laboratory (LANL) in New Mexico.

Founder Insights

She is deeply passionate about the intersection of network biology and AI architectures, with a focus on gene expression and discrete diffusion models for clinical prediction.

Experience

Atlas Discovery is backed by Y Combinator, Pear VC, and Glasswing Ventures, and has presented research at top conferences including ICLR, CSHL, and ICML.

Scale & Impact

The company's models have demonstrated 70-90% accuracy in separating drug responders from non-responders across Cancer and Autoimmune disease trials, representing over 10x improvement in accuracy while training on 50x less data.

About

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