Maxime Vono
Staff Research Lead at Criteo
Paris, Ile-de-France
Summary
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Senior🎓
Top SchoolMaxime Vono is a Staff Research Lead at Criteo with a PhD in statistical machine learning and nine years of experience bridging academic excellence and industrial impact. He has authored 15+ papers in top AI venues and collaborated with leading researchers from Oxford, Polytechnique and the French Academy of Sciences, bringing rigor to applied problems in advertising and commerce media. At Criteo he leads hybrid teams of ML engineers and researchers building next-generation bidding engines resilient to cookie deprecation, focusing on privacy-preserving ML (differential privacy, cohort/federated learning) and multi-task learning. His background spans high-dimensional sampling, uncertainty quantification and large-scale simulation from roles at Huawei and the Paris Observatory, giving him a rare blend of theoretical depth and production-minded engineering. He’s known for translating cutting-edge research into deployable systems that anticipate regulatory and data-fragmentation challenges in ad tech. As a subtle differentiator, he pairs top-tier publications with hands-on mentorship and cross-disciplinary collaborations that accelerate both science and product delivery.
9 years of coding experience
8 years of employment as a software developer
Baccalauréat scientifique Mathématiques, Baccalauréat scientifique Mathématiques at Lycée Paul HéroultDoctor of Philosophy - PhD Statistics, Doctor of Philosophy - PhD Statistics at Toulouse INPMaster's Degree Applied Mathematics, Master's Degree Applied Mathematics at University of Lille 1 Sciences and TechnologyEngineer's Degree Data Science, Engineer's Degree Data Science at Centrale LilleB.Sc. Mathematics, B.Sc. Mathematics at Lycée du Parc
French, English, Spanish, Italian
Github Skills (6)
gaussian8
stochastic8
sampling8
advertising7
requirements-management5
python3
Programming languages (3)
Github contributions (5)
mvono/PyGauss
Oct 2020 - Feb 2022
Companion package of the review paper entitled 'High-dimensional Gaussian sampling: A review and a unifying approach based on a stochastic proximal point algorithm' by Maxime Vono et al.
Contributions:6 commits, 1 PR, 22 pushes in 1 year 4 months
approachdimensionalreviewpointgaussian
mvono/2019-TSP-split-Gibbs-sampler
Jan 2019 - Sep 2020
Contributions:7 commits, 2 pushes in 1 year 8 months
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