Welcome

Welcome to the website of the AIDOS LAB at the Institute of AI for Health, an institute of the Helmholtz Zentrum München! 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 geometrical deep learning, topological machine learning, and representation learning. We want to make use of geometrical and topological information—also known as manifold learning—to imbue neural networks with more information in their respective tasks, leading to better and more robust outcomes.

Following the dictum ‘theory without practice is empty,’ we also develop methods to address challenges in biomedicine or healthcare applications. Of particular interest are the analysis of MRI data sets to improve our understanding of human cognition and neurodegenerative disorders, as well as the analysis of multivariate clinical time series to detect and prevent the onset of sepsis or myocardial ischemia.

About

‘AIDOS’ has two meanings that complement each other well. The first meaning refers to our mission statement, viz. to develop Artificial Intelligence for Discovering Obscured Shapes. The second meaning originates from the Greek word ‘αἰδώς,’ which means ‘awe,’ ‘reverence,’ or ‘humility.’ This awe or humility should serve as one of our guiding principles when we work on challenging problems in healthcare research, aiming to improve our world using machine learning.

People

Bastian Rieck
PI

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Publications

Here are all publications of lab members, sorted by year. Publications appear in the order in which they are accepted.

Preprints

2021

2020

2019

2018

2017

2016

2015

  • B. Rieck, H. Leitte: Comparing Dimensionality Reduction Methods Using Data Descriptor Landscapes, Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2015
  • B. Rieck, H. Leitte: Persistent Homology for the Evaluation of Dimensionality Reduction Schemes, Computer Graphics Forum, Volume 34, Number 3, pp. 431–440, 2015
  • B. Rieck, H. Leitte: Agreement Analysis of Quality Measures for Dimensionality Reduction, Workshop on Topology-Based Methods in Visualization (TopoInVis), 2015

2014

2013

  • B. Rieck, H. Mara, S. Krömker: Unwrapping Highly-Detailed 3D Meshes of Rotationally Symmetric Man-Made Objects, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W1, pp. 259–264, 2013
  • M. Forbriger, H. Mara, B. Rieck, C. Siart, O. Wagener: Der ‘‘Gesprengte Turm’’ Am Heidelberger Schloss – Untersuchung Eines Kulturdenkmals Mithilfe Hoch Auflösender Terrestrischer Laserscans, Denkmalpflege in Baden-Württemberg, Nachrichtenblatt der Landesdenkmalpflege, Volume 3, pp. 165–168, 2013

2012

2011

Join us

Thanks for your interest in our group! Why not consider joining the team? We are seeking students (at all levels) with strong quantitative backgrounds (computer science, mathematics, physics, …). You should be interested in working at the intersection of different fields and feel comfortable about writing code.

We are not interested in ‘leader-board science’ or ‘chasing the state-of-the-art’ in a table. That is not to say that we are not interested in producing relevant methods! Our overarching goal is to produce excellent science using methods whose performance we can explain and understand. This necessitates comprehensive comparisons with other methods, ablation studies, and many additional tricks to figure out what is going on. If this sounds enticing to you, we would love to hear from you!

Open positions