Michael Wornow
Co-Founder CEO at Kinetic Systems
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Wornow is a founder and machine learning leader who builds AI-first products to tackle hard problems in healthcare, currently serving as Co-Founder and CEO of Kinetic Systems. He blends 11 years of cross-disciplinary experience—from ML research on protein language models at Microsoft and predictive models for scRNA-Seq and histopathology at insitro to lab-focused ML at the Broad Institute—with product and strategy exposure from Bain and finance at Goldman. Trained at Harvard (CS & Statistics) and pursuing a PhD in Computer Science at Stanford, he bridges rigorous research with startup execution and clinician-facing product thinking. His background shows a pattern of applying ML to biological data modalities (DNA aptamers, single-cell, histology, proteins), giving him uncommon depth in computational biology for a startup CEO. Based in the San Francisco Bay Area, he is actively recruiting ML researchers, engineers, AI-forward clinicians, and product ops partners to bring frontier capabilities into clinical workflows.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Arts - BA, Computer Science and Statistics, Bachelor of Arts - BA, Computer Science and Statistics at Harvard UniversityHigh School, High School at The College Preparatory SchoolDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Stanford University
Github Skills (47)
self-supervised-learning10
foundation-models10
python9
common-data-format9
language-model8
jquery-ui8
kubernetes8
training-data7
webapp7
autocomplete7
machine-learning7
multimodal7
genomics7
nlp6
huggingface6
Programming languages (10)
Github contributions (5)
Miking98/us34-final-project
May 2018 - May 2022
Data analysis for US 34 Final Project
Contributions:1 PR, 25 pushes in 4 years 1 month
data-analysisdatadata-science
som-shahlab/aplusml
Sep 2022 - Mar 2023
APLUS ML = A Python Library for Usefulness Simulations of Machine Learning models
Contributions:1 release, 46 commits, 1 PR in 6 months
machine-learningmachine-learning-modelspython
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