My research interests are in the related areas of cognitive science, artificial intelligence, and robotics. I believe that computational approaches are inevitable for mechanistic explanations of learning and information processing that underlie cognition. I have always been a strong proponent of (brain-inspired) artificial neural networks (ANNs) as a preferred computational paradigm. On the one hand, I would like to understand how ANNs work, on the other, I have applied various (small scale) models in various tasks, mainly in language modeling, and robotics. I think that developmental cognitive robotics provides a promising approach that allows putting together various building blocks in an autonomous agent, with an emphasis on machine learning (supervised, unsupervised, or reinforcement based). I am also interested in explainable AI, intrinsic motivation (in RL) and abstraction in cognition.

I also worked on motor cognition using EEG measurements and used the brain-computer interface, exploiting the motor imagery paradigm.

I am a senior researcher of the Cognition and Neural Computation group.

I supervise doctoral, master and bachelor students: see the list of defended theses and of supervised projects by incoming students during the mobility semester (MEi:CogSci consortium).

From time to time, I provide feedback to public media on some relevant topics.

My profiles at GoogleScholar, ResearchGate, LinkedIn.