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.
Interests
- Machine Learning
- Deep Learning
- Embedding Neurophysiological signals
- Brain-Computer Interfaces
- Mental Imagery
Education
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PhD in AI and Neurotechnology, Present
Radboud University, Donders Institute
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Master in Machine Learning (M2A), 2020
Sorbonne University
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Master in Computer Science (MPRI), 2019
ENS Paris-Saclay
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BSc in Fundamental Computer Science, 2018
ENS Paris-Saclay