“One should always be a little improbable.”
– Oscar Wilde
Keywords: Optimal transport, Statistical learning, kernel methods, manifold and geometric approaches, optimal control
I mostly teach computer and data science at Université Bretagne Sud. Among others, I give the following courses:
I also teach in the Master Erasmus Mundus Copernicus CDE in the GeoData Science track.
We offer some positions at Obelix for master internships, PhD positions or post-docs. Consult the corresponding webpage. Also, if you are interested in doing research with me, and if you have an excellent track record but do not find a suitable announce, feel free me to contact me or use contact widget !
OTTOPIA is a project dedicated to novel machine learning methods for solving Remote Sensing challenges, mostly based on applications of optimal transport to problems such as e.g. generalized domain adaptation, graph classification and prediction, or causality in remote sensing
This project aims at contributing on the interactions between deep learning and physical models in a two-fold way: i) by exploring how dynamical formulation of learning process can help in understanding better learning deep neural architectures, as well as proposing new learning paradigms based on the regularization of the flows of information; ii) By leveraging on novel neural architectures and available data to devise new data-driven dynamical simulation models.
The OATMIL project is funded by ANR. The goal is to bring innovative use of optimal transport in machine learning, and machine learning for the computation of optimal transport problems. I serve as coordinator for this project