Recent Publications

@article{jas-etal:17b,
 author = {Jas, M., Engemann, D., Bekhti, Y., Raimondo, F. and Gramfort, A.},
 doi = {http://dx.doi.org/10.1016/j.neuroimage.2017.06.030},
 issn = {1053-8119},
 journal = {NeuroImage},
 keyword = {Automated analysis},
 link = {http://www.sciencedirect.com/science/article/pii/S1053811917305013},
 number = {},
 pages = {},
 pdf = {https://arxiv.org/pdf/1612.08194.pdf},
 title = {Autoreject: Automated artifact rejection for MEG and EEG data},
 volume = {},
 year = {2017}
}

@misc{chambon-etal:17,
 author = {Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.},
 eprint = {arXiv:1707.03321},
 pdf = {http://arxiv.org/pdf/1707.03321.pdf},
 title = {A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series},
 year = {2017}
}

@unpublished{ablin-etal:2017,
 author = {Ablin, P., Cardoso, J. and Gramfort, A.},
 hal_id = {hal-01552340},
 hal_version = {v1},
 keyword = {Preconditioning ; Quasi-Newton methods ;  Second order methods ;  Maximum likelihood estimation ; Independent Component Analysis ;  Blind source separation},
 link = {https://hal.inria.fr/hal-01552340},
 month = {Jun},
 note = {working paper or preprint},
 pdf = {https://hal.inria.fr/hal-01552340/file/quasi-newton-methods%20%286%29.pdf},
 title = {Faster ICA by preconditioning with Hessian approximations},
 year = {2017}
}

@misc{massias-etal:2017,
 author = {Massias, M., Fercoq, O., Gramfort, A. and Salmon, J.},
 eprint = {arXiv:1705.09778},
 pdf = {http://arxiv.org/pdf/1705.09778.pdf},
 title = {Heteroscedastic Concomitant Lasso for sparse multimodal electromagnetic brain imaging},
 year = {2017}
}

@misc{jas-etal:2017,
 author = {Jas, M., Dupré La Tour, T., Şimşekli, U. and Gramfort, A.},
 eprint = {arXiv:1705.08006},
 pdf = {http://arxiv.org/pdf/1705.08006.pdf},
 title = {Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding},
 year = {2017}
}

@misc{le-etal:2017,
 author = {Le, L., Marini, C., Gramfort, A., Nguyen, D., Cherti, M., Tfaili, S., Tfayli, A., Baillet-Guffroy, A., Prognon, P., Chaminade, P., Caudron, E. and Kégl, B.},
 eprint = {arXiv:1705.07099},
 pdf = {http://arxiv.org/pdf/1705.07099.pdf},
 title = {Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy},
 year = {2017}
}

@misc{1703.07285,
 author = {Massias, M., Gramfort, A. and Salmon, J.},
 eprint = {arXiv:1703.07285},
 pdf = {http://arxiv.org/pdf/1703.07285.pdf},
 title = {From safe screening rules to working sets for faster Lasso-type solvers},
 year = {2017}
}

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 during 5 years assistant professor 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.

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.
  • 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.
  • MNE - A complete package to process EEG and MEG data: forward and inverse problems (MNE, dSPM, MxNE), stats, time-frequency analysis.

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