Maksim Zhdanov

PhD student at AMLab


>>> print(self.status)

2nd year PhD student at AMLab

>>> print(self.supervisors)

Max Welling, Jan-Willem van de Meent, and Alfons Hoekstra

>>> print(self.research_interests)

physics-inspired deep learning, geometric deep learning


May 2024 Clifford-Steerable CNNs was accepted to ICML 2024! See you all in Vienna!
Feb 2024 Together with David Ruhe, Maurice Weiler and others, we have developed Clifford-Steerable CNNs - a new class of isometry-equivariant CNNs that are able to operate on pseudo-Euclidean spaces, such as Euclidean space and Minkowski spacetime. Really strong results on PDEs, new relativistic benchmark and lots of very cool math. The paper is available here.
Sep 2023 Our paper Implicit Convolutional Kernels for Steerable CNNs has been accepted to NeurIPS 2023! We developed a simple and efficient way to parameterize group equivariant convolutional kernels using implicit neural representation, which greatly simplifies the design of steerable CNNs. Check out the blog post for details.

latest posts

selected publications

  1. clifford_steerable.png
    Clifford-Steerable Convolutional Neural Networks
    Maksim Zhdanov, David Ruhe, Maurice Weiler, and 3 more authors
  2. imp_kernels.png
    Implicit Neural Convolutional Kernels for Steerable CNNs
    Maksim Zhdanov, Nico Hoffmann, and Gabriele Cesa
    NeurIPS 2023
  3. gnns.png
    Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
    Maksim Zhdanov, Saskia Steinmann, and Nico Hoffmann
    ICPR 2022