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).

# Recent Publications

@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.,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} }Gramfort, A.

@unpublished{sabbagh-etal:2019, author = {Sabbagh, D., Ablin, P., Varoquaux, G.,and Engemann, D.}, booktitle = {Advances in Neural Information Processing Systems 32},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} }Gramfort, A.

@article{banville-etal:19, author = {Banville, H., Albuquerque, I., Moffat, G., Engemann, D. and}, 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} }Gramfort, A.

@unpublished{ablin:hal-02140383, author = {Ablin, P., Moreau, T., Massias, M. and}, booktitle = {Advances in Neural Information Processing Systems 32}, editor = {H. Wallach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} }Gramfort, A.

@unpublished{massias:hal-02263500, author = {Massias, M., Vaiter, S.,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} }Gramfort, A.

@incollection{bertrand-etal:19, author = {Bertrand, Q., Massias, M.,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} }Gramfort, A.

@inproceedings{janati-etal:2019b, address = {Cham}, author = {Janati, H., Bazeille, T., Thirion, B., Cuturi, M. and}, booktitle = {Information Processing in Medical Imaging}, editor = {Chung, Albert C. S. and Gee, James C. and Yushkevich, Paul A. and Bao, Siqi}, isbn = {978-3-030-20351-1}, pages = {743--754}, pdf = {https://arxiv.org/pdf/1902.04812.pdf}, publisher = {Springer International Publishing}, title = {Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates}, year = {2019} }Gramfort, A.

# Short Bio

# 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

- Hicham Janati (coadvised with Marco Cuturi)
- Quentin Bertrand (coadvised with Joseph Salmon)
- Hubert Banville (coadvised with Denis Engemann)
- David Sabbagh (coadvised with Denis Engemann)
- Charlotte Caucheteux (coadvised with Jean-Rémi King)

### Alumni

- Joan Massich
- Thomas Moreau
- Pierre Ablin (coadvised with Jean-François Cardoso)
- Mathurin Massias (coadvised with Joseph Salmon)
- Roque Porchetto (now engineer at nexedi) [Engineer]
- Tom Dupré La Tour (coadvised with Yves Grenier) (now post-doc at UC Berkeley) [PhD]
- Stanislas Chambon (now engineer at therapixel) [PhD]
- Jean-Baptiste Schiratti (now engineer at bioserenity) [Post Doc]
- Mainak Jas (now post-doc at Harvard MGH Martinos Center) [PhD]
- Yousra Bekhti (coadvised with Roland Badeau) (now engineer at Engie in Boston, USA) [PhD]
- Albert Thomas (coadvised with Stéphan Clémençon) (now researcher at Huawei Technologies) [PhD]
- Jaakko Leppäkangas [Engineer]
- Romain Laby (coadvised with François Roueff) (now at Criteo) [PhD]
- Thierry Guillemot (now at Rythm Inc.) [Engineer]
- Fabian Pedregosa [PhD] (coadvised with Francis Bach) (now at Google Brain after a post-doc at UC Berkeley) [PhD]
- Michael Eickenberg [PhD] (coadvised with Bertrand Thirion) (now at UC Berkeley) [PhD]
- Jair Montoya [Post Doc]
- Daniel Strohmeier (coadvised with Jens Haueisen) (now at Siemens) [PhD]
- Raghav Rajagopalan [Engineer]

# 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

**Optimization for Data Science**course in Master program Data Science Ecole Polytechnique: Covers the different algorithms to minimize the cost functions that come up in machine learning (first / second order methods, batch / accelerated / stochastic gradient methods, coordinate descent).**Data Camp**course in Master program Data Science Ecole Polytechnique: Purpose is to build a working predictive model on an applied scientific or industrial problem, but also to be able to formulate a data problem as a machine learning task.- Course on Advanced Modeling and Analysis for Neuroimaging Data at Master on biomedical engineering at Paris Descartes.