Summary
Claire Donnat is an Assistant Professor of Statistics at the University of Chicago with nine years of experience at the intersection of statistics, machine learning, and network science, focusing on graph methods for biomedical applications. Trained at École Polytechnique and Stanford (Ph.D. in Statistics), she blends rigorous theory with practical algorithm development for large-scale and sparse problems. Her background includes impactful research and internships in industry labs (Facebook Core Data Science, Hudson River Trading, G-Research) where she applied deep learning and graph-based techniques to real-world time series, market, and user-network problems. She has a strong teaching and mentorship record from Stanford, recognized with departmental and university awards for excellence as a TA. Rare among academics, her profile combines military-style leadership training and hands-on systems work from an early EDF infrastructure internship, reflecting a pragmatic, disciplined approach to complex data challenges. Based in Palo Alto, she leverages both academic depth and industry experience to translate network methods into actionable biomedical insights.
9 years of coding experience
Mathematics and Physics, Mathematics and Physics at Lycée Sainte Geneviève
Bachelor’s Degree, Engineering, Physics and Applied Mathematics, Bachelor’s Degree, Engineering, Physics and Applied Mathematics at Ecole polytechnique
Doctor of Philosophy (Ph.D.), Statistics, Doctor of Philosophy (Ph.D.), Statistics at Stanford University
French, Russian, English