Scholar
Florent Bouchard
Google Scholar ID: Vzlw-NgAAAAJ
L2S, CNRS, CentraleSupélec, Univ. Paris Saclay
Citations & Impact
Citations
282
H-index
10
i10-index
10
Publications
20
Co-authors
0
Publications
Resume
Academic Achievements
- Published 3 papers in International Machine Learning Conferences (ICML, ECML-PKDD), 7 Journal Papers (IEEE-TSP, Signal Processing, SIAM-SIMAX, IEEE-SPL), 16 International Signal Processing Conferences (ICASSP, EUSIPCO, etc.), and 7 French National Signal Processing Conferences (GRETSI). Involved in major fundings such as DATAIA-YARN (Principal Investigator, 240K€), ANR DELTA (Collaborator, 600K€), and ANR MASSILIA (Collaborator, 235K€).
Research Experience
- CNRS Researcher at Université Paris Saclay, CNRS, CentraleSupélec, laboratoire des signaux et systèmes (L2S) since 2020; Postdoctoral Researcher at Université Savoie Mont Blanc, LISTIC from 2018 to 2020, supervised by Guillaume GINOLHAC; Visiting Melbourne University in August-September 2019, supervised by Jonathan MANTON.
Education
- PhD from 2015 to 2018 at Université Grenoble Alpes, supervised by Marco CONGEDO & Jérôme MALICK. His research focused on Riemannian geometry and optimization for joint diagonalization: application to source separation of electroencephalography.
Background
- A CNRS researcher at L2S, Université Paris-Saclay, focusing on developing robust statistical learning methods by exploiting Riemannian geometry and optimization.
Miscellany
- Supervising several PhD students and postdoctoral researchers, with research areas including EEG signal classification, robust geometric learning, and barycenters on Stiefel and Grassmann manifolds.