Recent Publications

@article{jaiswal-etal:20,
 author = {Jaiswal, A., Nenonen, J., Stenroos, M., Gramfort, A., Dalal, S., Westner, B., Litvak, V., Mosher, J., Schoffelen, J., Witton, C., Oostenveld, R. and Parkkonen, L.},
 doi = {https://doi.org/10.1016/j.neuroimage.2020.116797},
 issn = {1053-8119},
 journal = {NeuroImage},
 keywords = {MEG, EEG, source modeling, beamformers, LCMV, open-source analysis toolbox},
 pages = {116797},
 title = {Comparison of beamformer implementations for MEG source localization},
 url = {http://www.sciencedirect.com/science/article/pii/S1053811920302846},
 year = {2020}
}

@article{appelhoff_mne-bids:2019,
 author = {Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M.},
 comment = {[Code]},
 doi = {10.21105/joss.01896},
 issn = {2475-9066},
 journal = {Journal of Open Source Software},
 language = {en},
 month = {December},
 number = {44},
 pages = {1896},
 shorttitle = {{MNE}-{BIDS}},
 title = {{MNE}-{BIDS}: {Organizing} electrophysiological data into
the {BIDS} format and facilitating their analysis},
 url = {https://joss.theoj.org/papers/10.21105/joss.01896},
 urldate = {2019-12-19},
 volume = {4},
 year = {2019}
}

@inproceedings{sabbagh-etal:2019,
 author = {Sabbagh, D., Ablin, P., Varoquaux, G., Gramfort, A. and Engemann, D.},
 booktitle = {Advances in Neural Information Processing Systems 32},
 comment = {[Code]},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {7321--7332},
 publisher = {Curran Associates, Inc.},
 title = {Manifold-regression to predict from MEG/EEG brain signals without source modeling},
 url = {http://papers.nips.cc/paper/8952-manifold-regression-to-predict-from-megeeg-brain-signals-without-source-modeling.pdf},
 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}
}

@inproceedings{ablin:hal-02140383,
 author = {Ablin, P., Moreau, T., Massias, M. and Gramfort, A.},
 booktitle = {Advances in Neural Information Processing Systems 32},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {13100--13110},
 publisher = {Curran Associates, Inc.},
 title = {Learning step sizes for unfolded sparse coding},
 url = {http://papers.nips.cc/paper/9469-learning-step-sizes-for-unfolded-sparse-coding.pdf},
 year = {2019}
}

@unpublished{massias:hal-02263500,
 author = {Massias, M., Vaiter, S., Gramfort, A. and Salmon, J.},
 comment = {[Code]},
 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}
}

@inproceedings{bertrand-etal:19,
 author = {Bertrand, Q., Massias, M., Gramfort, A. and Salmon, J.},
 booktitle = {Advances in Neural Information Processing Systems 32},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {3961--3972},
 publisher = {Curran Associates, Inc.},
 title = {Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso},
 url = {http://papers.nips.cc/paper/8651-handling-correlated-and-repeated-measurements-with-the-smoothed-multivariate-square-root-lasso.pdf},
 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