Summary
Yvann Le Fay is a PhD candidate in Applied Mathematics based in Paris with a decade of experience applying advanced statistical and Monte Carlo methods across academia and industry. He works on sampling, variational inference and Bayesian-flavored techniques under supervisors like Nicolas Chopin and Matti Vihola, and has published in Bayesian Analysis while contributing research at CREST, Aalto and Inria. Comfortable teaching and translating complex theory into practical code, he also built efficient statistical routines for quantitative teams such as Lombard Odier. Known for a methodical yet creative approach, he thrives in fast-paced collaborative environments and bridges rigorous mathematical thinking with hands-on implementation.
10 years of coding experience
1 year of employment as a software developer
MPI, Mathematics, physics, computer science, MPI, Mathematics, physics, computer science at Lycée Henri IV
Bachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at Université Paris-Saclay
Master of Science - MS, MVA, Statistics and Machine Learning, Master of Science - MS, MVA, Statistics and Machine Learning at École normale supérieure Paris-Saclay
Master of Engineering - MEng, Applied Mathematics, Master of Engineering - MEng, Applied Mathematics at ENSAE Paris
Doctor of Philosophy - PhD, Applied Mathematics & Statistics, Doctor of Philosophy - PhD, Applied Mathematics & Statistics at Institut Polytechnique de Paris
English, French