Quentin Bertrand
Machine Learning Researcher at Inria
Canada
Summary
👤
Senior🎓
Top SchoolQuentin Bertrand is a machine learning researcher with nine years of experience blending rigorous statistical training and hands-on research to probe how and why learning algorithms work. Currently affiliated with Inria and holding roles at Mila and as a PhD candidate, he focuses on probabilistic modeling and foundational ML, building on prior work at SRI International on DARPA’s probabilistic programming efforts. Trained at École Polytechnique and École Normale Supérieure Paris-Saclay, he combines deep theoretical grounding with practical R/SQL and applied data science experience. His background includes leadership and discipline from military officer training, a distinctive asset that informs his collaborative and methodical research approach.
9 years of coding experience
1 year of employment as a software developer
Statistics & Data Science, Statistics & Data Science at École PolytechniqueEcole Spéciale Militaire de Saint-CyrMathematics, Physics, Mathematics, Physics at Lycée Henri Poincaré, NancyMaster 2 (M2), Mathématiques Vision Apprentissage, Master 2 (M2), Mathématiques Vision Apprentissage at École Normale Supérieure Paris-Saclay
French, English, German
Github Skills (68)
python10
weighted10
scikit10
statistics10
sampling10
linear-models10
data-science10
neuroscience10
data-analysis10
visualization10
machine-learning10
hyperparameters10
neuroimaging10
scikit-learn10
smooth10
Programming languages (7)
Github contributions (5)
QB3/sparse-ho
Apr 2020 - Jul 2022
Fast hyperparameter settings for non-smooth estimators:
Contributions:82 reviews, 274 commits, 99 PRs in 2 years 4 months
pythonhyperparameterhyperparametersmachine-learningsmooth
QB3/QB3.github.io
Nov 2019 - Jan 2025
Contributions:128 pushes, 1 branch in 5 years 3 months
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