Emile Mathieu
Machine Learning Researcher at Xaira Therapeutics
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolEmile Mathieu is a machine learning researcher with 11 years of experience bridging rigorous academic research and industry impact, currently working at Xaira Therapeutics after postdoctoral roles at Cambridge and Oxford. He holds a PhD in Statistics/Machine Learning from Oxford and advanced mathematical training from École Normale Supérieure and École des Ponts, bringing deep probabilistic and applied-math expertise to real-world problems. Emile has hands-on contributions to open-source probabilistic programming—implementing and testing Particle Marginal Metropolis-Hastings in the widely used Turing.jl—demonstrating both theoretical skill and practical software engineering. His background spans ML research internships at Facebook AI and applied roles improving online bidding and transport-data analysis, showing an ability to move from models to deployed systems. Colleagues describe him as a researcher who pairs statistical rigor with a pragmatic focus on reproducible, test-backed implementations.
11 years of coding experience
8 years of employment as a software developer
Applied Mathematics & Computer Science, Applied Mathematics & Computer Science at Ecole Nationale des Ponts et ChausséesDoctor of Philosophy - PhD Statistics / Machine learning, Doctor of Philosophy - PhD Statistics / Machine learning at University of OxfordCPGE Physique & Sciences de l'ingénieur, CPGE Physique & Sciences de l'ingénieur at Lycée Georges Clémenceau (Nantes)Master 2 (M2) Mathématiques / Vision / Apprentissage (Machine Learning), Master 2 (M2) Mathématiques / Vision / Apprentissage (Machine Learning) at École Normale Supérieure Paris-Saclay
French, English, Spanish
Github Skills (4)
bayesian-inference10
bayesian10
probabilistic-programming10
mcmc10
Programming languages (4)
Github contributions (5)
TuringLang/Turing.jl
Aug 2017 - Nov 2017
Bayesian inference with probabilistic programming.
Role in this project:
Data Scientist
Data ScientistContributions:144 commits, 19 PRs, 46 pushes in 3 months
Contributions summary:Emile implemented and tested Particle Marginal Metropolis-Hastings (PMMH) sampling within the Turing.jl probabilistic programming framework. Their contributions included the creation of PMMH-related files, defining the PMMH sampler, and integrating it with existing sampling techniques such as SMC. The user also added tests to validate the PMMH implementation and incorporated features for proposals and support checking.
julia-languagebayesian-inferencebayesian-methodsbayesian-neural-networkshamiltonian-monte-carlo
emilemathieu/dotfiles
Sep 2017 - Mar 2024
Contributions:21 pushes, 1 branch in 6 years 7 months
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