My research centres on how to understand complex data sets, mostly in biomedical contexts, using (among other things) topological machine learning methods. I consider myself to be a toolsmith, creating new tools that help us deal with challenging problems.

If you are interested in topological machine learning, consider joining TDA in ML, a Slack community dedicated to discussing all things topological machine learning.


Please find a list of my preprints and publications below. Equal contributions by several authors are indicated using a superscript dagger symbol, i.e. . A superscript ‘double dagger’, or diesis, i.e. , denotes a publication that was jointly directed. I aim to provide a BibTeX file for citing the publication as well as the slides corresponding to the paper. If neither a preprint nor slides are available but you still want to read the publication, please drop me a line to—it is possible that I am only allowed to share a publication privately.










  • 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




Here are some notes on various topics. These notes are not peer-reviewed, though, and may contain errors—please notify me if you find some.

Research interests

Our world is full of complex phenomena that happen at different temporal or spatial scales. I am interested in developing methods and techniques that help us understand these phenomena, especially in the presence of noisy or time-varying measurements.

Broadly speaking, this means that I have a penchant for developing new topological machine learning techniques, i.e. algorithms that are ‘aware’ of the fundamental topological properties of a data set. If you are familiar with machine learning terminology, this translates to developing new inductive biases and new architectures.

(I also have a general interest in algebraic & differential topology—both fields contain a wealth of potentially useful tools for visualisation and machine learning!)

Since ’theory without practice is empty,’ I also aim to build bridges to applications, in particular those that arise in the life sciences or healthcare in general. These domains are a source of complex data sets and challenges, and solutions to these challenges promise to have a profound, positive impact on society. As a mathematician-turned-computer-scientist-turned-something-else, this sparks immense joy and gratitude!

I am fortunate to work with many outstanding human beings, some of which also have personal websites: