Pierre Baudot is a Senior Data Researcher with 8+ years applying information theory, algebraic topology and machine learning to medical AI, notably cancer detection, at Median Technologies. Trained at ENS and Paris VI (PhD in cognitive science), he bridges deep theoretical work—information cohomology, topological data analysis and neural coding developed during postdocs at Max Planck and Institut des Systèmes Complexes—with practical, clinical-grade AI research. His background in electrophysiology and neuroscience informs a rare focus on structure-function correspondences and the statistical foundations of learning systems. Pierre’s work blends abstract mathematical formalisms (monads, operads, homotopy links) with applied measures and estimators, making him particularly skilled at translating complex theory into robust data-driven tools for biomedical problems. An active researcher with much of his code private, he nonetheless emphasizes foundational AI and measure-theoretic clarity in model design.
8 years of coding experience
6 years of employment as a software developer
Ecole Normale Supérieure rue d’Ulm: (ENS Paris) magister biology, Biologie, général, Ecole Normale Supérieure rue d’Ulm: (ENS Paris) magister biology, Biologie, général at Ecole normale supérieure
Master 2 (M2) & Biology Magister of ENS Ulm, Science Cognitive & Bioloy, Summa cum laude, Master 2 (M2) & Biology Magister of ENS Ulm, Science Cognitive & Bioloy, Summa cum laude at X-ENS-Paris XI & VI University
PhD, Cognitive science, summa cum laude, PhD, Cognitive science, summa cum laude at Université Pierre et Marie Curie (Paris VI)
computes most of information functions (joint entropy, conditional, mutual information, total correlation information distance) and deep information networks
Contributions:16 releases, 121 commits, 4 PRs in 1 year 10 months
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