AMLab | Amsterdam Machine Learning Lab

Max Zhdanov

PhD candidate (advised by J.W. van de Meent, Max Welling, and Alfons Hoekstra)
AMLab
Institute of Informatics
University of Amsterdam
Science Park, Lab 42, L4.22

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I am a PhD candidate at the Amsterdam Machine Learning Lab (AMLab) supervised by Jan-Willem van de Meent, Max Welling and Alfons Hoekstra. I use deep learning in various forms to learn PDEs from data. 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.

Selected Publications

ICPR

Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer

Zhdanov, Maksim, Steinmann, Saskia, and Hoffmann, Nico

In 26TH International Conference on Pattern Recognition Dec 2022

Abs arXiv 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.