SHORT BIO

Since November 2022, I am a postdoctoral researcher at Inria Saclay in the Mind team, working with Alexandre Gramfort and Rémi Flamary.

Previously, I did my PhD in the SONDRA laboratory at CentraleSupélec, University of Paris-Saclay.
My supervisors were Jean-Philippe Ovarlez, Guillaume Ginolhac, Chengfang Ren, and Arnaud Breloy.

I previously graduated from University of Technology of Compiègne with a major in Computer Science and a minor in Mathematical modeling.

My research focuses on statistics lying on Riemannian manifolds and their applications to machine learning and signal processing problems. I mainly work on remote sensing images (e.g. SAR and hyperspectral data) and EEG-MEG data.

My curriculum vitae - résumé - is available here: short version, long version (updated in November 2023).


PUBLICATIONS (Google scholar)

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Preprint

Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
A. Mellot, A. Collas, S. Chevallier, D. Engemann, A. Gramfort
2024
[arXiv]

Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications
A. Collas, R. Flamary, A. Gramfort
2024
[arXiv]


PhD thesis

Riemannian geometry for statistical estimation and learning: application to remote sensing
Supervisors: J-P. Ovarlez, G. Ginolhac, C. Ren, A. Breloy, F. Bouchard
Jury: A. Giremus, N. Le Bihan, C. Richard, N. Boumal, A. Gramfort
[PDF][slides]


Book chapter

The Fisher-Rao geometry of CES distributions
F. Bouchard, A. Breloy, A. Collas, A. Renaux, G. Ginolhac
2023 - Springer
[arXiv]


Journals

Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling
A. Mellot, A. Collas, P. L.C. Rodrigues, D. A. Engemann, A. Gramfort
2023 - Imaging Neuroscience MIT Press
[bioRxiv][DOI]

Parametric information geometry with the package Geomstats
A. Le Brigant, J. Deschamps, A. Collas, N. Miolane
2023 - ACM Transactions on Mathematical Software
[arXiv][slides][Github]

Riemannian optimization for non-centered mixture of scaled Gaussian distributions
A. Collas, A. Breloy, C. Ren, G. Ginolhac, J.-P. Ovarlez
2023 - IEEE Transactions on Signal Processing
[arXiv][Github]

Probabilistic PCA from Heteroscedastic Signals: Geometric Framework and Application to Clustering
A. Collas, F. Bouchard, A. Breloy, G. Ginolhac, C. Ren, J.-P. Ovarlez
2021 - IEEE Transactions on Signal Processing
[PDF][DOI]

Robust Low-Rank Change Detection for Multivariate SAR Image Time Series
A. Mian, A. Collas, A. Breloy, G. Ginolhac, J.-P. Ovarlez
2020 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
[PDF][DOI]


Conferences

Entropic Wasserstein Component Analysis
A. Collas, T. Vayer, R. Flamary, A. Breloy
2023 - IEEE Machine Learning for Signal Processing (MLSP) - Rome, Italy
[arXiv][slides][poster][Github][POT]

Apprentissage robuste de distance par géométrie riemannienne
A. Collas, A. Breloy, G. Ginolhac, C. Ren, J.-P. Ovarlez
2022 - XXVIIIème Colloque Francophone de Traitement du Signal et des Images GRETSI - Nancy, France
[PDF][slides]

Robust Geometric Metric Learning
A. Collas, A. Breloy, G. Ginolhac, C. Ren, J.-P. Ovarlez
2022 - 30th European Signal Processing Conference (EUSIPCO) - Belgrade, Serbia
Best student paper award
[arXiv][Github][slides][poster]

On The Use of Geodesic Triangles Between Gaussian Distributions for Classification Problems
A. Collas, F. Bouchard, G. Ginolhac, A. Breloy, C. Ren, J.-P. Ovarlez
2022 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Singapore
[PDF][DOI][slides][poster]

A Tyler-Type Estimator of Location and Scatter Leveraging Riemannian Optimization
A. Collas, F. Bouchard, A. Breloy, C. Ren, G. Ginolhac, J.-P. Ovarlez
2021 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Toronto, Canada
[PDF][DOI][slides][poster]



TALKS

Riemannian geometry for statistical estimation and learning: applications to remote sensing and M/EEG
2024 - OPIS Seminar - OPIS, CentraleSupélec - Gif-sur-Yvette, France
[slides]

Riemannian geometry for statistical estimation and learning: applications to remote sensing and M/EEG
2023 - TAU Seminar - TAU, Laboratoire de Recherche en Informatique - Gif-sur-Yvette, France
[slides]

Parametric information geometry with the package Geomstats
2023 - Mind team - Palaiseau, France
[slides]

Riemannian geometry for statistical estimation and learning: applications to remote sensing and M/EEG
2023 - S3 Seminar - L2S, CentraleSupélec - Gif-sur-Yvette, France
[slides]

Optimal transport and dimension reduction: Entropic Wasserstein Component Analysis
2023 - ELLIS Unconference - HEC - Jouy-en-Josas, France
[slides]

Entropic Wasserstein Component Analysis
2023 - SIMPAS team - Centre de Mathématiques Appliquées de l'Ecole Polytechnique - Palaiseau, France
[slides]

A (very) short presentation of Riemannian optimization using Pymanopt
2023 - Mind team - Palaiseau, France
[slides]

Estimation and classification of location and covariance matrix using Riemannian geometry: application to remote sensing
2023 - Laboratoire Jean Kuntzmann seminar - Grenoble, France
[slides]

On The Use of Geodesic Triangles Between Gaussian Distributions for Classification Problems
2022 - 5th Sondra Workshop - Avignon, France
[slides]

Optimization and statistical learning using Riemannian geometry: application to remote sensing
2022 - DSO National Laboratories - Singapore
[slides]

Optimization and statistical learning using Riemannian geometry and application to remote sensing
2022 - Inria Saclay, Parietal team - Palaiseau, France
[slides]

Robust Clustering for Satellite Images Time-Series
2022 - ONERA, the French Aerospace Lab - Palaiseau, France
[slides]

Probabilistic PCA from Heteroscedastic Signals: Geometric Framework and Application to Clustering
2021 - Statistical Learning for Signal and Image Processing (SLSIP) Workshop. A German-Finnish-French Workshop - Rüdesheim am Rhein, Germany
[slides][website]

Riemannian Geometry to Robust Estimation Covariance matrices with Application to Machine Learning
2021 - LISTIC laboratory - Annecy, France
[slides]



TEACHING

Teaching assistant in signal processing. Graduate studies in Master E3A at Paris-Saclay University in 2020 - 2021. Topics covered: Fourier analysis, linear regression, online estimation, stochastic process, statistical estimation ...