Recent Publications

@unpublished{sabbagh-etal:2019,
 author = {Sabbagh, D., Ablin, P., Varoquaux, G., Gramfort, A. and Engemann, D.},
 hal_id = {hal-02147708},
 hal_version = {v1},
 month = {June},
 note = {working paper or preprint},
 pdf = {https://hal.archives-ouvertes.fr/hal-02147708/file/main.pdf},
 title = {{Manifold-regression to predict from MEG/EEG brain signals without source modeling}},
 url = {https://hal.archives-ouvertes.fr/hal-02147708},
 year = {2019}
}

@article{banville-etal:19,
 author = {Banville, H., Albuquerque, I., Moffat, G., Engemann, D. and Gramfort, A.},
 journal = {Proc. Machine Learning for Signal Processing (MLSP)},
 publisher = {IEEE SigPort},
 title = {Self-supervised representation learning from electroencephalography signals},
 url = {http://sigport.org/4866},
 year = {2019}
}

@unpublished{ablin:hal-02140383,
 author = {Ablin, P., Moreau, T., Massias, M. and Gramfort, A.},
 hal_id = {hal-02140383},
 hal_version = {v1},
 month = {May},
 note = {working paper or preprint},
 pdf = {https://hal.inria.fr/hal-02140383/file/main.pdf},
 title = {{Learning step sizes for unfolded sparse coding}},
 url = {https://hal.inria.fr/hal-02140383},
 year = {2019}
}

@unpublished{massias:hal-02263500,
 author = {Massias, M., Vaiter, S., Gramfort, A. and Salmon, J.},
 hal_id = {hal-02263500},
 hal_version = {v1},
 month = {August},
 note = {working paper or preprint},
 pdf = {https://hal.archives-ouvertes.fr/hal-02263500/file/main.pdf},
 title = {{Dual Extrapolation for Sparse Generalized Linear Models}},
 url = {https://hal.archives-ouvertes.fr/hal-02263500},
 year = {2019}
}

@article{2019arXiv190202509B,
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190202509B},
 archiveprefix = {arXiv},
 author = {Bertrand, Q., Massias, M., Gramfort, A. and Salmon, J.},
 eprint = {1902.02509},
 journal = {arXiv e-prints},
 keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Applications},
 month = {February},
 pdf = {https://arxiv.org/pdf/1902.02509.pdf},
 primaryclass = {stat.ML},
 title = {{Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise}},
 year = {2019}
}

@inproceedings{janati-etal:2019b,
 address = {Cham},
 author = {Janati, H., Bazeille, T., Thirion, B., Cuturi, M. and Gramfort, A.},
 booktitle = {Information Processing in Medical Imaging},
 editor = {Chung, Albert C. S.
and Gee, James C.
and Yushkevich, Paul A.
and Bao, Siqi},
 isbn = {978-3-030-20351-1},
 pages = {743--754},
 pdf = {https://arxiv.org/pdf/1902.04812.pdf},
 publisher = {Springer International Publishing},
 title = {Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates},
 year = {2019}
}

@article{2019arXiv190109235M,
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190109235M},
 archiveprefix = {arXiv},
 author = {Moreau, T. and Gramfort, A.},
 eprint = {1901.09235},
 journal = {arXiv e-prints},
 keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning},
 month = {January},
 pdf = {https://arxiv.org/pdf/1901.09235.pdf},
 title = {{Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals}},
 year = {2019}
}

Full list of publications

Short Bio

I'm currently research director (DR, HDR) at Inria in the Parietal Team. My work is on statistical machine learning, signal and image processing, optimization, scientific computing and software engineering with primary applications in brain functional imaging (MEG, EEG, fMRI). Before joining Inria, I was an assistant professor for 5 years at Telecom ParisTech in the signal processing and machine learning department and before I was at the Martinos Center for Biomedical Imaging at Harvard University in Boston. I am also a co-director of the Center for Data Science at Université Paris-Saclay. In 2015, I got an ERC Starting Grant for my project called Signal and Learning Applied to Brain data (SLAB).

Contact

Email: alexandre.gramfort@inria.fr

Address: Inria Saclay Île-de-France, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'École Polytechnique 91120 Palaiseau

Software

  • scikit-learn - A Python project for machine learning.
  • MNE - A complete package to process EEG and MEG data: forward and inverse problems, preprocessing, stats, time-frequency analysis.
  • openmeeg - C++ package for low-frequency bio-electromagnetism including the EEG/MEG forward problem. OpenMEEG implements the Symmetric BEM which has shown to provide very accurate solutions. Some features: parallel processing, Python Bindings, Matlab integration with Fieldtrip and BrainStorm.

More on my Github Page

Team

Engineers

Post-docs

PhD Students

Alumni

Positions

  • PhD/Post-doc positions on machine learning and signal processing with applications in neuroimaging (MEG, EEG)

This list is fuzzy so please contact me directly for potential opportunities.

Teaching