Maksim Zhdanov

PhD student at AMLab

m.zhdanov@uva.nl

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.

news

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

Implicit Neural Convolutional Kernels for Steerable CNNs

Maksim Zhdanov, Nico Hoffmann, and Gabriele Cesa

2022

Abs arXiv PDF

Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and other transformations belonging to an origin-preserving group G, such as reflections and rotations. They rely on standard convolutions with G-steerable kernels obtained by analytically solving the group-specific equivariance constraint imposed onto the kernel space. As the solution is tailored to a particular group G, the implementation of a kernel basis does not generalize to other symmetry transformations, which complicates the development of group equivariant models. We propose using implicit neural representation via multi-layer perceptrons (MLPs) to parameterize G-steerable kernels. The resulting framework offers a simple and flexible way to implement Steerable CNNs and generalizes to any group G for which a G-equivariant MLP can be built. We apply our method to point cloud (ModelNet-40) and molecular data (QM9) and demonstrate a significant improvement in performance compared to standard Steerable CNNs.
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer

Maksim Zhdanov, Saskia Steinmann, and Nico Hoffmann

2022

Abs arXiv PDF Code

Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain’s functional and structural aspects, including mechanisms behind multiple pathologies. One such pathology is schizophrenia which is often followed by auditory verbal hallucinations. The latter is commonly studied by observing functional connectivity during speech processing. In this work, we have made a step toward an in-depth examination of functional connectivity during a dichotic listening task via deep learning for three groups of people: schizophrenia patients with and without auditory verbal hallucinations and healthy controls. We propose a graph neural network-based framework within which we represent EEG data as signals in the graph domain. The framework allows one to 1) predict a brain mental disorder based on EEG recording, 2) differentiate the listening state from the resting state for each group and 3) recognize characteristic task-depending connectivity. Experimental results show that the proposed model can differentiate between the above groups with state-of-the-art performance. Besides, it provides a researcher with meaningful information regarding each group’s functional connectivity, which we validated on the current domain knowledge.