Alexandre Perez
Founding ML Researcher at Fundamental
Barcelona, Catalonia, Spain
Summary
👤
Senior🎓
Top SchoolAlexandre Perez is a founding ML researcher and PhD-trained machine learning scientist with eight years of research experience focused on probabilistic classification and learning with missing data. He completed dual masters in mathematics and computer science at ENS Paris-Saclay and École des Ponts, pursued a PhD at Université Paris-Saclay, and spent time as a visiting researcher in Sanmi Koyejo’s lab at Stanford exploring decision-making and algorithmic fairness. His internships at Mila and the Montreal Neurological Institute reflect strong ties to top ML labs and a track record of translating theoretical insights into differentiable predictors and neural estimators. Based in Barcelona, he is motivated by recent AI breakthroughs and is seeking post-doc or industry research roles to push methodological advances in fairness-aware decision systems. An uncommon strength is his combined mathematical rigor and practical experience deriving optimal predictors under missingness, positioning him to bridge theory and deployable ML solutions.
8 years of coding experience
Master of Science (MVA) Machine Learning and Computer Vision Paris France, Master of Science (MVA) Machine Learning and Computer Vision Paris France at ENS Paris-SaclayPhD Machine Learning, PhD Machine Learning at Université Paris-SaclayMaster of Science Mathematics and Computer Science Paris France, Master of Science Mathematics and Computer Science Paris France at École nationale des ponts et chausséesPreparatory classes MPSI/MP* Lyon France, Preparatory classes MPSI/MP* Lyon France at Lycée du ParcVisiting Student Researcher Computer Science, Visiting Student Researcher Computer Science at Stanford University
French, English, Spanish
Github Skills (63)
benchmark10
health10
python10
data-science10
scikit10
statistics10
machine-learning10
imputation10
predictive10
supervised-learning10
scikit-learn10
data-analysis10
connectivity9
benchmarking9
nltk9
Programming languages (6)
Github contributions (5)
aperezlebel/meta_analysis
Jun 2019 - Sep 2019
The goal is to study the impact of meta-analysis priors on prediction performance.
Contributions:94 pushes, 15 branches in 2 months
predictiongoalmetameta-analysisperformance
aperezlebel/benchmark_mv_approaches
Jan 2020 - Jan 2022
Code of the experiments ran in our GigaScience article: Benchmarking missing-values approaches for predictive models on health databases.
Contributions:939 commits, 1 PR, 429 pushes in 2 years
imputationsupervised-learningbenchmarkingmissing-valueshealth
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