Team Member

I am an associate professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam (AMLab, UvA).

Before joining the UvA, I worked as a post-doctoral researcher in applied differential geometry at the Technical University Eindhoven (TU/e) Department of Applied Mathematics. During my PhD research at TU/e (completed cum laude in Biomedical Engineering), I focused on developing medical image analysis algorithms based on sub-Riemannian geometry in the Lie group SE(2). This work built upon the same mathematical principles that underlie models of human visual perception. I’ve found that these mathematical foundations are particularly valuable in machine learning, where we can leverage symmetries and geometric structure to develop robust and efficient representation learning methods.

My current research focuses on developing generalizations and efficient implementations of group equivariant architectures while expanding the application scope of the geometric deep learning paradigm.

I am honored to have received several recognitions for my work, including the MICCAI Young Scientist Award 2018 and the Philips Impact Award (MIDL 2018). I’ve also been fortunate to secure two personal research grants from the Dutch Research Council (NWO): a VENI grant on Context-Aware Artificial Intelligence in Medical Image Analysis and a VIDI grant on Neural Ideograms: Shaping AI with Geometry-Grounded Learning.