About me

I am an Assistant Professor in the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, where I teach and do research at the intersection of machine learning, artificial intelligence and cognitive science.

Prior to my current position, I was a Marie Skłodowska-Curie Fellow at KU Leuven, and before that I was a postdoc at the Baylor College of Medicine and a visiting researcher at the University of Cambridge.

An important goal of my research is to understand the computational principles of continual learning, a crucial skill for both artificial and biological learning agents. 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.

My interests are broad, and I am always keen to explore new directions and take on challenges outside my comfort zone.

Longer ago, 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).