Alexandre Gramfort
AI ML Research Scientist at Meta
Paris, Ile-de-France
Summary
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Rockstar🎓
Top SchoolAlexandre Gramfort is an AI/ML research scientist based in Paris with 18 years of experience, currently researching neural and bio time series at Meta Reality Labs. He pairs a rigorous academic pedigree (École Polytechnique, Télécom Paris, PhD in signal and image processing) with production-grade software engineering as a scikit-learn core developer and prolific contributor to neuroimaging projects like nibabel, nilearn and MNE. His open-source work spans adding Freesurfer support to nibabel and modernizing scikit-learn internals, while also improving reproducibility and tooling (CI/CD for pyRiemann, Sphinx-Gallery demos). As an Ingénieur des Mines and former Inria senior researcher, he uniquely blends applied math, domain expertise in brain imaging, and practical DevOps to take ML research toward deployable, reliable software.
18 years of coding experience
12 years of employment as a software developer
BS, Applied Maths, Computer Science, BS, Applied Maths, Computer Science at École PolytechniqueMaster, Image Processing, Applied Maths, Computer Science, Master, Image Processing, Applied Maths, Computer Science at Télécom ParisMaster, Image Processing, Medical Imaging, Machine Learning, Master, Image Processing, Medical Imaging, Machine Learning at Ecole Normale Supérieure de Cachan
French, English
Stackoverflow
64reputation
7kreached
2answers
0questions
Github Skills (52)
data-formats10
github-ci10
documentations10
pytest10
python10
sphinx10
brain-imaging10
scikit10
testing10
github-actions-workflows10
machine-learning10
analyse10
workflow-engine10
matlab10
surf10
Programming languages (23)
Github contributions (5)
scikit-learn/scikit-learn
Mar 2010 - Mar 2022
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:172 reviews, 1096 commits, 485 PRs in 12 years 2 months
Contributions summary:Alexandre's commits primarily focused on modifications and enhancements to existing machine learning models within the scikit-learn library, specifically within the realm of unsupervised learning, feature selection, and linear modeling techniques. The contributions included refactoring and improving the robustness of the code, as well as adding tests, examples, and features, such as incorporating support for diverse output types for several models and improving internal processing within the functions. Additionally, the user has been actively involved in making core changes to enhance and modernize the codebase for the next release.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
mne-tools/mne-python
Dec 2010 - Jan 2023
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:Data Scientist & Backend Developer
Data Scientist & Backend DeveloperContributions:5 releases, 1622 reviews, 2496 commits in 12 years 2 months
Contributions summary:Alexandre's commits focus on enhancing examples, which mainly involve the application and integration of machine learning techniques such as the use of multi-taper spectral analysis for time-frequency representation. They have implemented features relating to data visualization, and have also worked on code related to the inverse solution. The user primarily utilized the MNE-Python library for performing these tasks, demonstrating skills in EEG data analysis and related source localization techniques.
visualizationneuroimagingneurosciencepythonstatistics
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