Aurélien Bellet
Senior Researcher (directeur De Recherche) at Inria
Montpellier, Occitania, France
Summary
🤩
Rockstar🎓
Top SchoolAurélien Bellet is a senior research scientist at Inria with 11+ years specializing in machine learning, particularly distributed/federated learning, privacy-preserving methods, metric and representation learning, and graph-based approaches. He holds a PhD on supervised metric learning with generalization guarantees and has held research and teaching positions at Télécom Paris, USC and Edinburgh, blending strong theoretical foundations with applied work in NLP, speech and health. Now director-level at Inria’s PreMeDICaL team, he combines expertise in optimization and statistical learning theory with a practical focus on decentralized systems. An active open-source contributor, he helped improve usability and documentation for the widely used metric-learn Python project, demonstrating his commitment to making advanced algorithms accessible.
11 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Université Jean Monnet Saint-EtiennePostgraduate Diploma, Computer Science, Postgraduate Diploma, Computer Science at The University of EdinburghHabilitation thesis (HDR), Computer Science, Habilitation thesis (HDR), Computer Science at University of Lille 1 Sciences and TechnologyGraduate exchange student, Computer Science, Graduate exchange student, Computer Science at McMaster University
English, French, Italian
Github Skills (6)
scikit10
metric-learning10
python10
documentation10
scikit-learn10
machine-learning9
Programming languages (5)
Github contributions (5)
scikit-learn-contrib/metric-learn
Jan 2019 - Jun 2022
Metric learning algorithms in Python
Role in this project:
Back-end Developer & Technical Writer
Back-end Developer & Technical WriterContributions:1 release, 31 reviews, 19 commits in 3 years 6 months
Contributions summary:Aurélien primarily contributed to the project's documentation, writing an introduction to metric learning and creating other documentation sections. They also made code changes to support the documentation, such as moving code to the `_util` module, renaming `transformer_` to `components_`, deprecating the `use_pca` parameter, and updating the README with paper references. Their work demonstrates a focus on improving the project's usability and clarity through documentation.
pythonmetric-learningmachine-learninglearning-algorithmsscikit-learn
bellet/metric-learn
Aug 2018 - Aug 2024
Metric learning algorithms in Python
Contributions:66 pushes, 42 branches in 6 years
pythonmetric-learningmachine-learninglearning-algorithms
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