About me

I am an FWO senior postdoc at KU Leuven (Belgium) and visiting researcher at TU Delft (the Netherlands), where I perform research at the intersection of deep learning, computational neuroscience and cognitive science. Until February 2022, I was a postdoc at the Baylor College of Medicine (Houston, Texas) and visiting researcher at the University of Cambridge.

The main goal of my research is to understand the computational principles underlying the cognitive skill of continual learning. I do this through a combination of conceptual analysis, computational modelling, deep neural network implementations and collaborations with experimental labs. My contributions include proposing the influential “three scenarios” framework for continual learning, providing a proof-of-principle demonstration that generative classification is a promising strategy for class-incremental learning, and identifying the stability gap—an intriguing phenomenon in which deep neural networks suffer substantial but temporary forgetting when starting to learn something new.

Another research direction I am interested in is using insights and intuitions from neuroscience to make the behavior of deep neural networks more human-like. For example, I developed the brain-inspired replay method, which alleviates catastrophic forgetting in deep neural networks by replaying self-generated, abstract memory representations.

Previously, for my award-winning PhD in Neuroscience (University of Oxford), I used optogenetics and electrophysiological recordings in mice to study the role of replay in memory consolidation in the brain. Before that I obtained degrees in Statistics (Master, UC Berkeley) and Econometrics (Bachelor, Erasmus University Rotterdam).