Recent Publications

@article{Rodrigues2021,
 abstract = {Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. This arises in many practical situations, such as when inferring the distance and power of a radio source (is the source close and weak or far and strong?) or when estimating the amplifier gain and underlying brain activity of an electrophysiological experiment. In this work, we present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters. Our method extends recent developments in simulation-based inference(SBI) based on normalizing flows to Bayesian hierarchical models. We validate quantitatively our proposal on a motivating example amenable to analytical solutions, and then apply it to invert a well known non-linear model from computational neuroscience.},
 archiveprefix = {arXiv},
 author = {Rodrigues, Pedro L. C. and Moreau, Thomas and Louppe, Gilles and Gramfort, Alexandre},
 eprint = {2102.06477},
 journal = {arXiv 2102.06477},
 keywords = {stat.ML, cs.LG, q-bio.QM},
 month = {February},
 note = {arXiv:2012.06477},
 pdf = {https://arxiv.org/pdf/2102.06477v1.pdf},
 primaryclass = {stat.ML},
 title = {Leveraging Global Parameters for Flow-based Neural Posterior Estimation},
 year = {2021}
}

@inproceedings{Jallais2021a,
 address = {R{{\o}}nne, Denmark},
 author = {Jallais, Ma{\"e}liss and Coelho Rodrigues, Pedro Luiz and Gramfort, Alexandre and Wassermann, Demian},
 booktitle = {{IPMI 2021}},
 hal_id = {hal-03090959},
 hal_version = {v1},
 keywords = {Brain Microstructure ; Diffusion MRI ; Likelihood-Free Inference},
 month = {June},
 pdf = {https://hal.inria.fr/hal-03090959/file/SBI_dMRI.pdf},
 title = {{Cytoarchitecture Measurements in Brain Gray Matter using Likelihood-Free Inference}},
 url = {https://hal.inria.fr/hal-03090959},
 year = {2021}
}

@article{Rodrigues2020a,
 abstract = {There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data. Despite exciting recent results and a wide range of possible applications, an important bottleneck of LFI when applied to time series data is the necessity of defining a set of summary features, often hand-tailored based on domain knowledge. In this work, we present a data-driven strategy for automatically learning summary features from univariate time series and apply it to signals generated from autoregressive-moving-average (ARMA) models and the Van der Pol Oscillator. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values such as autocorrelation coefficients even in the linear case.},
 archiveprefix = {arXiv},
 author = {Rodrigues, Pedro L. C. and Gramfort, Alexandre},
 eprint = {2012.02807},
 journal = {arXiv 2012.02807},
 keywords = {stat.ML, cs.LG, stat.AP},
 month = {December},
 note = {arXiv:2012.02807},
 pdf = {https://arxiv.org/pdf/2012.02807v1.pdf},
 primaryclass = {stat.ML},
 title = {Learning summary features of time series for likelihood free inference},
 year = {2020}
}

@inproceedings{richard-etal:20,
 archiveprefix = {arXiv},
 author = {Richard, Hugo and Gresele, Luigi and Hyvärinen, Aapo and Thirion, Bertrand and Gramfort, Alexandre and Ablin, Pierre},
 booktitle = {Advances in Neural Information Processing Systems 33},
 eprint = {2006.06635},
 title = {Modeling Shared Responses in Neuroimaging Studies through MultiView {ICA}},
 url = {https://arxiv.org/abs/2006.06635},
 year = {2020}
}

@inproceedings{chevalier-etal:20,
 archiveprefix = {arXiv},
 author = {Chevalier, Jerome-Alexis and Salmon, Joseph and Gramfort, Alexandre  and Thirion, Bertrand},
 booktitle = {Advances in Neural Information Processing Systems 33},
 eprint = {2009.14310},
 title = {Statistical control for spatio-temporal {MEG/EEG} source imaging with desparsified mutli-task Lasso},
 url = {https://arxiv.org/abs/2009.14310},
 year = {2020}
}

@article{banville-etal:20,
 author = {Banville, Hubert J. and Chehab, Omar and Hyvärinen, Aapo and Engemann, Denis{-}Alexander and Gramfort, Alexandre},
 journal = {Journal of Neural Engineering},
 pdf = {https://arxiv.org/pdf/2007.16104},
 title = {Uncovering the structure of clinical {EEG} signals with self-supervised
learning},
 url = {http://iopscience.iop.org/article/10.1088/1741-2552/abca18},
 year = {2020}
}

@inproceedings{janati-etal:20a,
 abstract = {Comparing data defined over space and time is notoriously hard. It involves quantifying both spatial and temporal variability while taking into account the chronological structure of the data. Dynamic Time Warping (DTW) computes a minimal cost alignment between time series that preserves the chronological order but is inherently blind to spatio-temporal shifts. In this paper, we propose Spatio-Temporal Alignments (STA), a new differentiable formulation of DTW that captures spatial and temporal variability. Spatial differences between time samples are captured using regularized Optimal transport. While temporal alignment cost exploits a smooth variant of DTW called soft-DTW. We show how smoothing DTW leads to alignment costs that increase quadratically with time shifts. The costs are expressed using an unbalanced Wasserstein distance to cope with observations that are not probabilities. Experiments on handwritten letters and brain imaging data confirm our theoretical findings and illustrate the effectiveness of STA as a dissimilarity for spatio-temporal data.},
 address = {Online},
 author = {Janati, Hicham and Cuturi, Marco and Gramfort, Alexandre},
 booktitle = {AISTATS},
 editor = {Silvia Chiappa and Roberto Calandra},
 month = {26--28 Aug},
 pages = {1695--1704},
 pdf = {http://proceedings.mlr.press/v108/janati20a/janati20a.pdf},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 title = {Spatio-temporal alignments: Optimal transport through space and time},
 url = {http://proceedings.mlr.press/v108/janati20a.html},
 volume = {108},
 year = {2020}
}

Full list of publications

Short Bio

I am currently senior research scientist (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) and in 2019 a grant ANR Chaire on Arficial Intelligence called BrAIN.

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