About me

Welcome to my professional page ! I am a first year PhD student in the LAMSADE at Université Paris Dauphine-PSL, under the supervision of Florian Yger, Sylvain Chevallier and Fabien Lotte.

My research focuses on Brain Computer Interfaces (BCI) and the goal of my PhD is to learn context invariant representations for EEG data. These representations aim to capture the underlying brain activity independent of external factors, such as environmental conditions, cognitive states (intersession or intersubject variabilities), or task requirements. By removing the influence of context, the obtained representations can enhance the interpretability and generalization capabilities of EEG analysis models.

The tools I use for this work come from the machine learning world, but also from Riemannian geometry. Indeed, a very useful tool used when handeling EEG data is its covariance matrix that lives on the Riemannian manifold of symmetric, positive and definite matrices. Therefore, using geometry-aware algorithms and tools using this specific Riemannian geometry is the gold standard when dealing with EEG data.

Feel free to contact me if you want to learn more about my research !