Personal information

machine learning, deep learning, reinforcement learning, artificial intelligence

Biography

Since 2020, I am an assistant professor at the VU Amsterdam. Previously, I did a post-doc at McGill University/Mila, where I was working with Joelle Pineau and Doina Precup. I obtained a PhD in machine learning at the University of Liege with Raphael Fonteneau and Damien Ernst in September 2017.

I'm interested in deep reinforcement learning, particularly in the aspects related to generalization from limited data and how it is possible to integrate model-based and model-free learning. One of my recent works describes how both the model-based and model-free approaches can be integrated via a shared low-dimensional learned encoding of the environment that captures summarizing abstractions (AAAI-19). Another publication uses a similar approach for novelty search in representational space for sample efficient exploration (NeurIPS-2020).

With a particular focus on generalization, I also wrote an introduction to deep reinforcement learning (Foundations and Trends in Machine Learning) as well as a paper providing theoretical insights on bias and overfitting in the general context of POMDPs (JAIR and IJCAI-2020).