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Welcome to the website of the AIDOS LAB at the Institute of AI for Health, an institute of Helmholtz Munich! We are fascinated by discovering hidden structures in complex data sets, in particular those arising in healthcare applications.

Our primary research interests are situated at the intersection of geometry, topology, and machine learning. We want to make use of geometrical and topological information to imbue neural networks with more information in their respective tasks, leading to better and more robust outcomes. Along the way, we develop new manifold learning techniques, new representation learning algorithms, and much more.

Following the dictum ’theory without practice is empty,’ we address challenges in biomedicine and healthcare applications.

Check out our research to learn more.

Topological Singularity Detection at Multiple Scales
Topological Singularity Detection at Multiple Scales
ICML 2023
The manifold hypothesis is often assumed in data analysis. But what happens if it does not hold? In this publication, we are analysing to what extent a data set deviates from being a manifold. We develop Euclidicity, a new multi-scale score based on persistent local homology, to assess this deviation.
Ollivier–Ricci Curvature for Hypergraphs: A Unified Framework
Ollivier–Ricci Curvature for Hypergraphs: A Unified Framework
ICLR 2023
Bridging geometry and topology, curvature is a powerful and expressive invariant. While the utility of curvature has been theoretically and empirically confirmed in the context of manifolds and graphs, its generalisation to the emerging domain of hypergraphs has remained largely unexplored. Our paper aims to fill this gap and presents a new framework for hypergraph curvature.