Erik Bekkers


Erik Bekkers is an assistant professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam (AMLab, UvA). Before this he did a post-doc in applied differential geometry at the dept. of Applied Mathematics at Technical University Eindhoven (TU/e). In his PhD thesis (cum laude, Biomedical Engineering, TU/e), he developed medical image analysis algorithms based on sub-Riemannian geometry in the Lie group SE(2) using the same mathematical principles that underlie mathematical models of human visual perception. Such mathematics find their application in machine learning where through symmetries and geometric structure, robust and efficient representation learning methods are obtained. His current work is on generalizations of group convolutional NNs, improvements of their computational and representation efficiency through sparse (graphs) and adaptive learning mechanisms. Erik is a recipient of a MICCAI Young Scientist Award 2018, Philips Impact Award (MIDL 2018) and a personal VENI research grant (awarded by the Dutch Research Council (NWO)).


Research topics

  • Neural Ideograms
  • Group equivariant deep learning
  • Leveraging geometry in (graph) neural networks
  • Computing with geometric quantities (manifold-valued features)
  • Geometric latent space modeling


  • David Romero: Continuous kernel CNNs, Group equivariant networks and Self-attention networks.
  • Putri van der Linden: Geometric Deep Learning, Sparse Visual Representations
  • Rob Hesselink: Geometric Deep Learning, Graphs and Group Equivariance
  • Sharvaree Vadgama: Geometric Latent Space Modeling and Explainable AI
  • Michel Botros: AI-Based Detection and Prediction of Esophageal Cancer
  • Mohammad Islam: Uncertainty-aware AI-based Predictive Modeling for Treatment Decision Making

Selected Publications

  1. NeurREPS @ NeurIPS
    Kendall Shape-VAE : Learning Shapes in a Generative Framework
    Vadgama, Sharvaree, Tomczak, Jakub Mikolaj, and Bekkers, Erik J
    In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations 2022
  2. ICML
    Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
    Knigge, David M, Romero, David W, and Bekkers, Erik J
    International Conference on Machine Learning 2022
  3. ICLR
    Geometric and Physical Quantities improve E (3) Equivariant Message Passing
    Brandstetter, Johannes, Hesselink, Rob, Pol, Elise, Bekkers, Erik, and Welling, Max
    In International Conference on Learning Representations 2022
  4. ICLR
    B-Spline CNNs on Lie groups
    Bekkers, Erik J
    In International Conference on Learning Representations 2019