Recent Publications

@article{2018arXiv180510054A,
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180510054A},
 archiveprefix = {arXiv},
 author = {Ablin, P., Gramfort, A., Cardoso, J. and Bach, F.},
 eprint = {1805.10054},
 journal = {ArXiv e-prints},
 keyword = {Statistics - Machine Learning, Computer Science - Learning, Statistics - Applications},
 month = {may},
 pdf = {https://arxiv.org/pdf/1805.10054},
 primaryclass = {stat.ML},
 title = {EM algorithms for ICA},
 year = {2018}
}

@article{dupre-etal:18,
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180509654D},
 archiveprefix = {arXiv},
 author = {Dupré La Tour, T., Moreau, T., Jas, M. and Gramfort, A.},
 eprint = {1805.09654},
 journal = {ArXiv e-prints},
 keyword = {Electrical Engineering and Systems Science - Signal Processing, Computer Science - Learning, Statistics - Machine Learning},
 month = {may},
 pdf = {https://arxiv.org/pdf/1805.09654},
 title = {Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals},
 year = {2018}
}

@article{janati-etal:18,
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180507833J},
 archiveprefix = {arXiv},
 author = {Janati, H., Cuturi, M. and Gramfort, A.},
 eprint = {1805.07833},
 journal = {ArXiv e-prints},
 keyword = {Statistics - Machine Learning, Computer Science - Learning},
 month = {may},
 pdf = {https://arxiv.org/abs/1805.07833},
 primaryclass = {stat.ML},
 title = {Wasserstein regularization for sparse multi-task regression},
 year = {2018}
}

@article{jas-etal:18,
 author = {Jas, M., Larson, E., Engemann, D., Leppäkangas, J., Taulu, S., Hämäläinen, M. and Gramfort, A.},
 comment = {[Code]},
 doi = {10.3389/fnins.2018.00530},
 issn = {1662-453X},
 journal = {Frontiers in Neuroscience},
 link = {https://www.frontiersin.org/article/10.3389/fnins.2018.00530},
 pages = {530},
 title = {A Reproducible {MEG/EEG} Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices},
 volume = {12},
 year = {2018}
}

@inproceedings{Massias_Gramfort_Salmon18,
 author = {Massias, M., Gramfort, A. and Salmon, J.},
 booktitle = {Proceedings of the 35th International Conference on Machine Learning},
 comment = {[Code]},
 link = {https://arxiv.org/abs/1802.07481},
 pages = {3321--3330},
 pdf = {https://arxiv.org/pdf/1802.07481},
 title = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
 volume = {80},
 year = {2018}
}

@article{ablin-etal:2017,
 author = {Ablin, P., Cardoso, J. and Gramfort, A.},
 comment = {[Code]},
 doi = {10.1109/TSP.2018.2844203},
 issn = {1053-587X},
 journal = {IEEE Transactions on Signal Processing},
 keyword = {Approximation algorithms;Brain modeling;Data models;Electronic mail;Neuroscience;Signal processing algorithms;Tensile stress;Blind source separation;Independent Component Analysis;maximum likelihood estimation;preconditioning;quasi-Newton methods;second order methods},
 month = {},
 number = {15},
 pages = {4040-4049},
 pdf = {https://hal.inria.fr/hal-01552340/file/quasi-newton-methods%20%286%29.pdf},
 title = {Faster independent component analysis by preconditioning with Hessian approximations},
 volume = {66},
 year = {2018}
}

@article{bekhti-etal:17,
 author = {Bekhti, Y., Lucka, F., Salmon, J. and Gramfort, A.},
 comment = {[Code]},
 journal = {Inverse Problems},
 link = {http://iopscience.iop.org/article/10.1088/1361-6420/aac9b3/meta},
 pdf = {https://arxiv.org/pdf/1710.08747},
 title = {A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging},
 year = {2018}
}

Full list of publications

Short Bio

I'm currently researcher 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 an active member 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

News