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).

# 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.,, 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} }Gramfort, A.

@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}, 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} }Gramfort, A.

@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}, 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} }Gramfort, A.

@inproceedings{Massias_Gramfort_Salmon18, author = {Massias, M.,and Salmon, J.}, booktitle = {ICML}, comment = {[Code]}, journal = {ArXiv e-prints}, link = {https://arxiv.org/abs/1802.07481}, pdf = {https://arxiv.org/pdf/1802.07481}, title = {Dual Extrapolation for Faster Lasso Solvers}, year = {2018} }Gramfort, A.

@article{ablin-etal:2017, author = {Ablin, P., Cardoso, J. and}, 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} }Gramfort, A.

@article{bekhti-etal:17, author = {Bekhti, Y., Lucka, F., Salmon, J. and}, journal = {Inverse Problems}, link = {http://iopscience.iop.org/10.1088/1361-6420/aac9b3}, 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} }Gramfort, A.

@inproceedings{chambon-etal:18, address = {Singapour, Singapore}, author = {Chambon, S., Galtier, M. and}, booktitle = {Pattern Recognition in Neuroimaging}, hal_id = {hal-01814190}, hal_version = {v1}, keyword = {Index Terms-EEG ; sleep stage classification ; domain adapta- tion ; neural network ; optimal transport}, link = {https://hal.archives-ouvertes.fr/hal-01814190}, month = {Jun}, pdf = {https://hal.archives-ouvertes.fr/hal-01814190/file/main.pdf}, title = {Domain adaptation with optimal transport improves EEG sleep stage classifiers}, year = {2018} }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

- Tom Dupré La Tour (coadvised with Yves Grenier)
- Stanislas Chambon
- Pierre Ablin (coadvised with Jean-François Cardoso)
- Mathurin Massias (coadvised with Joseph Salmon)
- Hicham Janati (coadvised with Marco Cuturi)

### Alumni

- Mainak Jas [PhD]
- Yousra Bekhti (coadvised with Roland Badeau) [PhD]
- Albert Thomas (coadvised with Stéphan Clémençon) [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 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) [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.

# News

- June 2017: I'll be at the Scipy Conference in Austin, Texas to give the scikit-learn tutorial and the talk on MNE in the neuroscience symposium.
- June 2017: I'll be visiting the University of Washington to give a talk on non-linear signal models for neural time-series on my way the OHBM conference where I'll be presenting 2 posters.
- June 2017: Time for the annual scikit-learn code sprint in Paris!
- Mar. 2017: I'll attend the MNE code sprint at New York University Center for Datascience
- Mar. 2017: I'll be giving a talk on Statistical Learning and optimization for MRI data mining at Inria Grenoble Macaron workshop
- Mar. 2017: I'll be visiting the MEG lab and give an MNE tutorial at Université Libre de Bruxelles in Brussels
- Jan. 2017: I'll be giving a talk on inverse problems, optimization and machine learning at Learning in Astrophysics day
- Jan. 2017: I'll be giving a introductary talk on statistical machine learning for brain imaging at Karolinska insitute in Stockholm at the Machine learning for functional brain imaging workshop
- Oct. 2016: I'll be giving a talk on statistical machine learning and neuroscience at JSTAR Conf in Rennes
- Oct. 2016: I'll be giving a talk on optimal transport for neuroscience applications at Workshop Optimisation et Transport optimal en Imagerie
- Oct. 2016: I'll be giving talks on group analysis with MNE-Software and source localization at Biomag Conf. in Seoul, Corea
- Sept. 2016: I'll be a panel member at France is AI Startup Event at BPI France
- Sept. 2016: I'll be giving a talk about Paris-Saclay Center for Datascience at Data Science Game kick-off event at Microsoft France
- Aug. 2016: I'll be giving a talk on Gap Safe screening rules at MAS Conf. in Grenoble
- July 2016: I'll be one of the keynote speakers at CAP Conf. (Conférence Francophone pour l'apprentissage automatique)
- June 2016: I'll be giving two talks on teaching and doing anomaly detection with scikit-learn at PyData Paris
- June 2016: I'll be teaching MEG/EEG data analysis with MNE for 2 days at PRNI 2016 conf.
- May 2016: I'll be teaching MEG/EEG data analysis with MNE at Dalhousie University in Halifax, Canada
- Nov. 2015: I'll giving a talk on the use of open source in machine learning at Big Data Business Convention BDBC on HEC Campus
- Nov. 2015: I'll be giving a talk on supervised learning on MEG/EEG signals at Paris Workshop on Decoding of Sound and Brain (slides pdf)
- Oct. 2015: I have been awarded an
**ERC Starting Grant**! My project is called "Signal processing and Learning for Brain data (SLAB)" - 22 Sept. 2015: I'll give a seminar on Brain Decoding using fMRI at Donders Center for cognitive neuroscience.
- Sept. 2015: I'll be presenting our latest work on MEG/EEG inverse modeling at BACI conference in Utrecht.
- July. 2015: My work presented at IPMI conf. in Scotland got a runner up award !
- July. 2015: I'll be one the keynote speakers at machine learning in neuroscience workshop at ICML in Lille
- July. 2015: I'll present our work on speeding up the Lasso estimator using SAFE Screening rules at ICML in Lille. Video lecture
- July. 2015: I'll give an oral presentation on my work G. Peyré and M. Cuturi at IPMI conf. in Scotland
- June. 2015: My work with D. Engemann and D. Strohmeier will be presented in oral presentations at PRNI conference in Stanford
- June. 2015: My work with my student Michael Eickenberg will be presented in an oral presentation at OHBM conference in Hawai
- May. 2015: Talk at Chalmers University "The impact of tools and modeling assumptions on neuroscience"
- Apr. 2015: I'll be giving a tutorial on Scikit-Learn for medical imaging at IEEE ISBI conference in NYC.
- 5 Dec. 2014: I'll be talking at Gipsa Lab at Workshop on challenges in multimodality about the problems and the benefits of combining MEG and EEG for source analysis.
- 5 and 12 Nov. 2014: MNE training session at Telecom ParisTech.
- 6 June. 2014: My student Daniel Strohmeier gets the Best Paper Award at PRNI conference for our work on the M/EEG inverse problem.
- 17 June. 2014: I'll be talking on the MNE project at the INCF Paris workshop
- 24 Aug. 2014: My symposium on supervised learning for M/EEG data analysis has been accepted at BIOMAG 2014
- 3 Sept. 2014: I'll give a talk at the workshop on Statistical Challenges in Neuroscience in Warwick UK
- 8 Jun. 2014: My work will be presented in 2 oral presentations at Human Brain Mapping conference.
- 14-16 May 2014: I'll be teaching MEG/EEG data analysis with MNE at MRC Lab in Cambridge UK
- 20 Jan. 2014: I'll be teaching MEG/EEG data analysis with MNE in Stockholm Karolinska Institute NatMEG data analysis workshop
- 4 Dec. 2013: MNE Training Session in Paris, ICM
- 23 Oct. 2013: I'll be at BrainHack in Paris
- 7 Oct. 2013: I'll be at CIMEC in Trento, Italy to teach MNE and Scikit-Learn
- 23 Sept. 2013: I'll be in Magdeburg, Germany to teach MNE for the Timely Workshop