David Loiseaux is a postdoctoral researcher and applied mathematician specializing in multiparameter topological persistence and its applications to machine learning, with nine years of experience spanning academia and research labs. He completed a PhD at Université Côte d'Azur after advanced mathematics training at École Normale Supérieure de Rennes and Université Paris-Saclay, and holds the French agrégation in mathematics. His work bridges topology, geometry, and probabilistic/statistical foundations to develop tools that make topological descriptors usable in ML pipelines, and he has held visiting and teaching positions including Columbia and Berkeley Lab. Based in Berkeley, he combines deep theoretical rigor with practical teaching experience (Math for AI) and a track record of cross-institutional collaboration across Inria, École Polytechnique, and French universities. An understated strength is his ability to translate abstract TDA concepts into reproducible methods for ML, informed by both research internships and extended postdoctoral roles.
8 years of coding experience
Master 2 (M2), Mathématiques, Master 2 (M2), Mathématiques at Université de Rennes I
PhD, Computer Science, PhD, Computer Science at Université Côte d'Azur
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