Associate Professor in Geometric Deep Learning
University of Amsterdam (AMLab)
Leading the Ideal Machine Intelligence group. My work bridges the gap between the elegance of physical laws and the power of artificial intelligence. I focus on Geometric Deep Learning—embedding the symmetries and structures of nature into the learning process.
As we build increasingly sophisticated AI, we face the "Unplugging Paradox": the intuition to grant rights to machines that mimic emotion. My personal position is grounded in Biological Idealism. True sentience is not a computational output but a biological imperative—a result of autopoiesis (the metabolic struggle to maintain life).
An AI, no matter how convincing, remains a functional mimic. The ethical risk lies not in 'harming' these tools, but in moral misallocation: displacing our finite empathy and resources away from living, feeling beings towards the algorithms we created to serve them.
PhD in Biomedical Engineering (cum laude) from TU/e. Formerly a post-doc in applied differential geometry. My roots are in the mathematics of sub-Riemannian geometry and visual perception.
NWO VIDI (2023): Neural Ideograms.
NWO VENI (2019): Context-Aware AI.
MICCAI Young Scientist Award (2018).
"Nearly all data is rooted in our physical world." I believe that by grounding AI in geometry, we create models that are not just effective, but theoretically sound and data-efficient.