Maksim Zhdanov

PhD student at AMLab

Hi! I am a PhD candidate at AMLab supervised by Max Welling, Jan-Willem van de Meent and Alfons Hoekstra. I am trying to learn PDEs from data using deep learning. Before joining the University of Amsterdam, I was a research assistant at Helmholtz AI, where I worked on applications of machine learning for material science. I also spent some time working on graph neural networks and generative modelling with applications in neuroscience. Long ago, I developed statistical models of clinical treatment at TU Dresden.

Overall, my research interests revolve around physics-inspired deep learning and geometric deep learning. I am also interested in AI4Science and the applications of machine learning to physics.

In my free time, I enjoy reading, playing basketball and testing gravity when skateboarding.


Apr 15, 2023 Absolutely thrilled to announce that I will join the University of Amsterdam and start my PhD this spring, working with Max Welling, Jan-Willem van de Meent and Alfons Hoekstra!
Nov 11, 2022 Extremely proud to present the result of the LOGML research project I have been involved in for the last 3 months under the supervision of Gabriele Cesa. Equivariance for the win!
Aug 22, 2022 Recently, I participated at ICPR 2022, where I presented the paper “Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer”. The GitHub repo is available, and the recording will be released soon too.

selected publications

  1. imp_kernels.png
    Implicit Neural Convolutional Kernels for Steerable CNNs
    Maksim Zhdanov, Nico Hoffmann, and Gabriele Cesa
  2. gnns.png
    Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
    Maksim Zhdanov, Saskia Steinmann, and Nico Hoffmann