Recent Publications

@unpublished{ablin-etal:18c,
 author = {Ablin, P., Cardoso, J. and Gramfort, A.},
 hal_id = {hal-01936887},
 hal_version = {v1},
 link = {https://hal.archives-ouvertes.fr/hal-01936887},
 month = {Nov},
 note = {working paper or preprint},
 pdf = {https://hal.archives-ouvertes.fr/hal-01936887/file/main.pdf},
 title = {Beyond Pham's algorithm for joint diagonalization},
 year = {2018}
}

@article{ablin-etal:18b,
 archiveprefix = {arXiv},
 author = {Ablin, P., Fagot, D., Wendt, H., Gramfort, A. and Fevotte, C.},
 eprint = {1811.02225},
 journal = {ArXiv e-prints},
 keyword = {Statistics - Machine Learning, Computer Science - Machine Learning},
 link = {https://arxiv.org/abs/1811.02225},
 month = {nov},
 pdf = {https://arxiv.org/pdf/1811.02225.pdf},
 primaryclass = {stat.ML},
 title = {A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning},
 year = {2018}
}

@inproceedings{chambon-etal:18b,
 author = {Chambon, S., Thorey, V., Arnal, P., Mignot, E. and Gramfort, A.},
 booktitle = {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},
 doi = {10.1109/MLSP.2018.8517067},
 issn = {1551-2541},
 keyword = {Sleep;Electroencephalography;Prediction algorithms;Feature extraction;Tensile stress;Kernel;Event detection;Deep learning;EEG;event detection;sleep;EEG-patterns;time series},
 month = {Sept},
 number = {},
 pages = {1-6},
 pdf = {https://arxiv.org/pdf/1807.05981},
 title = {A Deep Learning Architecture to Detect Events in EEG Signals During Sleep},
 volume = {},
 year = {2018}
}

@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}
}

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