Recent Publications

@inproceedings{dupre-etal:2017,
 address = {New Orleans, USA},
 author = {Dupre la Tour, T., Grenier, Y. and Gramfort, A.},
 booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
 link = {https://hal.archives-ouvertes.fr/hal-01448603/},
 month = {Feb},
 pdf = {https://hal.archives-ouvertes.fr/hal-01448603/document},
 title = {Parametric estimation of spectrum driven by an exogenous signal},
 year = {2017}
}

@inproceedings{montoya-etal:2017,
 address = {Grenoble, France},
 author = {Montoya-Martinez, J., Cardoso, J. and Gramfort, A.},
 booktitle = {International Conference on Latent Variable Analysis, Independent Component Analysis LVA-ICA},
 link = {https://hal.archives-ouvertes.fr/hal-01451432/},
 month = {Feb},
 pdf = {https://hal.archives-ouvertes.fr/hal-01451432/document},
 title = {Caveats with stochastic gradient and maximum likelihood based ICA for EEG},
 year = {2017}
}

@inproceedings{ndiaye-etal:16b,
 author = {Ndiaye, E., Fercoq, O., Gramfort, A. and Salmon, J.},
 booktitle = {Proc. NIPS 2016},
 pdf = {http://arxiv.org/pdf/1602.06225v1.pdf},
 title = {{GAP} Safe Screening Rules for {Sparse-Group Lasso}},
 year = {2016}
}

@techreport{ndiaye-etal:16a,
 author = {Ndiaye, E., Fercoq, O., Gramfort, A., Leclère, V. and Salmon, J.},
 link = {https://arxiv.org/abs/1606.02702},
 pdf = {https://arxiv.org/pdf/1606.02702v1.pdf},
 title = {Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression},
 year = {2016}
}

@inproceedings{jas-etal:16,
 address = {Trento, Italy},
 author = {Jas, M., Engemann, D., Raimondo, F., Bekhti, Y. and Gramfort, A.},
 booktitle = {6th International Workshop on Pattern Recognition in Neuroimaging (PRNI)},
 hal_id = {hal-01313458},
 hal_version = {v1},
 keyword = {magnetoencephalography ; electroencephalography ; preprocessing ; artifact rejection ; automation ; machine learning},
 link = {https://hal.archives-ouvertes.fr/hal-01313458},
 month = {Jun},
 pdf = {https://hal.archives-ouvertes.fr/hal-01313458/file/automated-rejection-repair.pdf},
 title = {Automated rejection and repair of bad trials in MEG/EEG},
 year = {2016}
}

@inproceedings{bekhti-etal:16,
 address = {Trento, Italy},
 author = {Bekhti, Y., Strohmeier, D., Jas, M., Badeau, R. and Gramfort, A.},
 booktitle = {6th International Workshop on Pattern Recognition in Neuroimaging (PRNI)},
 doi = {10.1109/PRNI.2016.7552337},
 hal_id = {hal-01313567},
 hal_version = {v2},
 keyword = {Inverse problem ;  MEEG ;  iterative reweighted optimization algorithm ;  multi-scale dictionary ;  Gabor transform.},
 link = {https://hal.archives-ouvertes.fr/hal-01313567},
 month = {Jun},
 pdf = {https://hal.archives-ouvertes.fr/hal-01313567/file/PRNI16_multiscale.pdf},
 title = {M/EEG source localization with multi-scale time-frequency dictionaries},
 year = {2016}
}

@article{eickenberg-etal:16,
 author = {Eickenberg, M., Gramfort, A., Varoquaux, G. and Thirion, B.},
 doi = {http://dx.doi.org/10.1016/j.neuroimage.2016.10.001},
 issn = {1053-8119},
 journal = {NeuroImage},
 link = {http://www.sciencedirect.com/science/article/pii/S1053811916305481},
 note = {},
 number = {},
 pages = {-},
 pdf = {https://hal.inria.fr/hal-01389809/file/neuroimage.pdf},
 title = {Seeing it all: Convolutional network layers map the function of the human visual system},
 volume = {},
 year = {2016}
}

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

  • Engineer to work on scikit-learn
  • Engineer to work on MNE
  • 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.

News