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Eeshit Dhaval Vaishnav

Eeshit Dhaval Vaishnav

Founder & CEO at Sequome, leveraging AI for sequence medicine

eeshit-vaishnav
San Francisco, California
Joined July 2024
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Summary

Eeshit Dhaval Vaishnav is a distinguished entrepreneur and CEO of Sequome, a company dedicated to developing sequence medicines through AI-driven decoding and design of biological sequences. His leadership at Sequome focuses on leveraging advanced computational biology to innovate in the pharmaceutical technology sector, including collaborations with Fortune 500 companies. His work in this area stems from a deep understanding of gene regulation and synthetic biology, aiming to transform drug discovery and development. pmwcintl+2
With a Ph.D. from MIT and the Broad Institute of MIT and Harvard, Eeshit is a leading expert in computational biology, genomics, and machine learning. His doctoral research under Prof. Aviv Regev focused on understanding and engineering gene regulatory DNA, culminating in a thesis titled 'Evolution, Evolvability, Expression and Engineering'. His academic contributions include co-first authorship on a seminal Nature paper about predicting gene regulation through neural networks and multiple highly-cited publications in top scientific journals like Nature Biotechnology. google+3
Eeshit possesses a strong interdisciplinary background, holding a double major in Computer Science & Engineering and Biological Sciences & Bioengineering from the Indian Institute of Technology, Kanpur. This unique combination of expertise allows him to bridge the gap between advanced computational methods and complex biological challenges, driving innovation in areas like AI for life sciences and gene sequence optimization. clay+1

Work

Education

Writing

A community effort to optimize sequence-based deep learning models of gene regulation

October 1, 2024

A Nature Biotechnology publication (2024) where he is an author, describing a DREAM Challenge to systematically evaluate model architectures and training strategies for genomics model performance.

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Evolution, Evolvability, Expression and Engineering

January 1, 2022

His Ph.D. thesis exploring the machines that answer questions about evolution, evolvability, and gene expression.

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The evolution, evolvability and engineering of gene regulatory DNA

January 1, 2022

A Nature publication (2022) where he is a co-first author, developing a neural network model to predict how changes to non-coding DNA sequences in yeast affect gene expression.

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Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

January 1, 2020

A Nature Biotechnology publication (2020) that is highly cited, exploring gene-regulatory logic.

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