• Institute of AI for Health
  • Helmholtz.AI


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.


‘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.


Bastian Rieck
Julius von Rohrscheidt
Jeremy Wayland
Bastian Rieck Julius von Rohrscheidt Jeremy Wayland
Principal Investigator Ph.D. Student Ph.D. Student (incoming)
Adrien Aumon
Kalyan Varma Nadimpalli
Ferdinand Hölzl
Adrien Aumon Kalyan Varma Nadimpalli Ferdinand Hölzl
Visiting Researcher M.Sc. Student Intern

Your name is missing here! Learn more about joining us below.


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










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



  • B. Rieck, H. Mara, and 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, and 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



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.

Since the group is still starting to establish itself, you have the unique opportunity to truly shape and influence things here.

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!

To learn more about our working style, see this note for potential student collaborators.

Bachelor’s and master’s theses

If you are interested in writing your thesis with us, please send your CV, your transcript of records, and a brief cover letter stating your research interests to bastian.rieck@helmholtz-muenchen.de.

Ph.D. positions

If you are interested in working with us and have a background in mathematics, computer science, physics, or a general penchant for computational methods, please send your CV to bastian.rieck@helmholtz-muenchen.de for general inquiries on Ph.D. positions.

Research visits

If you are interested in a short-term research opportunity, such as a research visit over the summer, please reach out bastian.rieck@helmholtz-muenchen.de. We are particularly interested in stays that may result in long-term collaborations.

If you are looking for an internship, note that our institution does not permit paid internships. There are funding opportunities for such internships available; check out RISE or DAAD Scholarships in general.