I am interested in the development and repurposing of deep learning algorithms for neurophysiological signals. In particular, I investigate how deep learning algorithms can reduce the calibration times of brain-computer interfaces, increase their robustness, and allow for new invariance. I am also interested in whether deep learning could help push the limits of what can be decoded from EEG and neural signals in general.

  • Machine Learning
  • Deep Learning
  • Embedding Neurophysiological signals
  • Brain-Computer Interfaces
  • Mental Imagery
  • PhD in AI and Neurotechnology, Present

    Radboud University, Donders Institute

  • Master in Machine Learning (M2A), 2020

    Sorbonne University

  • Master in Computer Science (MPRI), 2019

    ENS Paris-Saclay

  • BSc in Fundamental Computer Science, 2018

    ENS Paris-Saclay