Research

I am a postdoctoral researcher in the Machine Learning & Computational Biology Lab of Prof. Dr. Karsten Borgwardt at ETH Zürich.

Previously, I was a senior researcher in the Visual Computing Group of Prof. Dr. sc. Filip Sadlo, after finishing my Ph.D. thesis in the Visual Information Analysis Group of Prof. Dr. Heike Leitte. My research centres on how to understand complex data sets in biomedical applications. See below for a list of my publications and other materials.

I also have a profile on ResearchGate and track most of my reviews via publons. My ORCID is 0000-0003-4335-0302.

Publications

Please find a list of my preprints and publications below. Equal contributions by several authors are indicated using a superscript dagger symbol, i.e. †. 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 bastian@rieck.me—it is possible that I am only allowed to share a publication privately.

2019

Visualization of Equivalence in 2D Bivariate Fields
Boyan Zheng, Bastian Rieck, Heike Leitte, and Filip Sadlo
To appear in Computer Graphics Forum (Proceedings of the Eurographics Conference on Visualization), 2019.

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
Bastian Rieck†, Matteo Togninalli†, Christian Bock†, Michael Moor, Max Horn, Thomas Gumbsch, and Karsten Borgwardt
Proceedings of the International Conference on Learning Representations (ICLR), 2019.
OpenReview • BibTeX • GitHub repository • DOI: 10.3929/ethz-b-000327207

Topological Machine Learning with Persistence Indicator Functions
Bastian Rieck, Filip Sadlo, and Heike Leitte
To appear in Topological Methods in Data Analysis and Visualization V, Springer, 2019.

Persistent Intersection Homology for the Analysis of Discrete Data
Bastian Rieck, Markus Banagl, Filip Sadlo, and Heike Leitte
To appear in Topological Methods in Data Analysis and Visualization V, Springer, 2019.

Persistence Concepts for 2D Skeleton Evolution Analysis
Bastian Rieck, Filip Sadlo, and Heike Leitte
To appear in Topological Methods in Data Analysis and Visualization V, Springer, 2019.
This is the published version of the extended abstract from 2017 (see below)

Hierarchies and Ranks for Persistence Pairs
Bastian Rieck, Filip Sadlo, and Heike Leitte
To appear in Topological Methods in Data Analysis and Visualization V, Springer, 2019.
This is the published version of the preprint from 2017 (see below)

2018

Visualization of Parameter Sensitivity of 2D Time-Dependent Flow
Karsten Hanser, Ole Klein, Bastian Rieck, Bettina Wiebe, Tobias Selz, Marian Piatkowski, Antoni Sagristà, Boyan Zheng, Mária Lukácová, George Craig, Heike Leitte, and Filip Sadlo
Lecture Notes in Computer Science: Advances in Visual Computing (Proceedings of the International Symposium on Visual Computing), pp. 359–370, November 2018.

Association mapping in biomedical time series via statistically significant shapelet mining
Christian Bock, Thomas Gumbsch, Michael Moor, Bastian Rieck, Damian Roqueiro, and Karsten Borgwardt
Bioinformatics, Volume 34, Issue 13, pp. i438–i446, July 2018.
BibTeX • GitHub repository

Visualization of 4D Vector Field Topology
Lutz Hofmann, Bastian Rieck, and Filip Sadlo
Computer Graphics Forum, Volume 37, Issue 3, pp. 301–313, June 2018.
BibTeX

Visualization of Fullerene Fragmentation
Kai Sdeo, Bastian Rieck, and Filip Sadlo
Short Paper Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), pp. 111–115, April 2018.
BibTeX

Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks
Bastian Rieck, Ulderico Fugacci, Jonas Lukasczyk, and Heike Leitte
IEEE Transactions on Visualization and Computer Graphics, Volume 24, Issue 1, pp. 822–831, January 2018.
BibTeX • GitHub repository • Supplementary materials

2017

Persistent Homology in Multivariate Data Visualization
Bastian Rieck
Ph.D. thesis, Ruprecht-Karls-Universität Heidelberg
BibTeX • urn:nbn:de:bsz:16-heidok-229145 • DOI: 10.11588/heidok.00022914 • Text-only version

Persistence Concepts for 2D Skeleton Evolution Analysis (extended abstract)
Bastian Rieck, Filip Sadlo, and Heike Leitte
Workshop on Topology-Based Methods in Visualization (TopoInVis), Accepted for Presentation, 2017.
BibTeX • GitHub repository

Hierarchies and Ranks for Persistence Pairs
Bastian Rieck, Filip Sadlo, and Heike Leitte
Workshop on Topology-Based Methods in Visualization (TopoInVis), Accepted for Presentation, 2017.
BibTeX • Slides

2016

‘Shall I compare thee to a network?’—Visualizing the Topological Structure of Shakespeare’s Plays
Bastian Rieck and Heike Leitte
Workshop on Visualization for the Digital Humanities at IEEE VIS 2016.
BibTeX • Slides

Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology
Bastian Rieck and Heike Leitte
Computer Graphics Forum, Volume 35, Issue 3, pp. 81–90, June 2016.
BibTeX • Supplementary materials • Slides

Agreement Analysis of Quality Measures for Dimensionality Reduction
Bastian Rieck and Heike Leitte
Topological Methods in Data Analysis & Visualization IV, pp. 103–117, Springer, 2017.
BibTeX • Slides • This is the published version of the preprint from 2015 (see below)

Interactive Similarity Analysis and Error Detection in Large Tree Collections
Jens Fangerau, Burkhard Höckendorf, Bastian Rieck, Christian Heine, Joachim Wittbrodt, and Heike Leitte
Visualization in Medicine and Life Sciences III: Towards Making an Impact, pp. 287–307, Springer, 2016.
BibTeX

2015

Comparing Dimensionality Reduction Methods Using Data Descriptor Landscapes
Bastian Rieck and Heike Leitte
Symposium on Visualization in Data Science (VDS) at IEEE VIS 2015.
BibTeX • Slides

Persistent Homology for the Evaluation of Dimensionality Reduction Schemes
Bastian Rieck and Heike Leitte
Computer Graphics Forum, Volume 34, Issue 3, pp. 431–440, June 2015.
BibTeX • Slides

Agreement Analysis of Quality Measures for Dimensionality Reduction
Bastian Rieck and Heike Leitte
Workshop on Topology-Based Methods in Visualization (TopoInVis), Accepted for Presentation, 2015.
BibTeX • Slides

2014

Enhancing Comparative Model Analysis using Persistent Homology
Bastian Rieck and Heike Leitte
IEEE Vis 2014 Workshop on Visualization for Predictive Analytics.
BibTeX • Slides

Structural Analysis of Multivariate Point Clouds using Simplicial Chains
Bastian Rieck and Heike Leitte
Computer Graphics Forum, Volume 33, Issue 8, pp. 28–37, December 2014.
BibTeX • Slides

2013

Der “Gesprengte Turm” am Heidelberger Schloss – Untersuchung eines Kulturdenkmals mithilfe hoch auflösender terrestrischer Laserscans
Markus Forbriger, Hubert Mara, Bastian Rieck, Christoph Siart, Olaf Wagener
Denkmalpflege in Baden-Württemberg, Nachrichtenblatt der Landesdenkmalpflege, Heft 3-2013, S. 165–168.

Unwrapping Highly-Detailed 3D Meshes of Rotationally Symmetric Man-Made Objects
Bastian Rieck, Hubert Mara, and Susanne Krömker
ISPRS Annals, Volume II-5/W1, pp. 259–264.
BibTeX

2012

Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures
Bastian Rieck, Hubert Mara, and Heike Leitte
IEEE Transactions on Visualization and Computer Graphics, Volume 18, Issue 12, pp. 2382–2391, December 2012.
BibTeX • Slides

2011

Smoothness analysis of subdivision algorithms
Master’s Thesis, Heidelberg University. Advised by Susanne Krömker and Heike Leitte.
BibTeX • GitHub repository

Notes

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.

Talks

Here’s a list of my recent talks, some of which are accompanied by slides.

An enchiridion for topological data analysis
June 2018; talk at Basel Postdoc Retreat in Klosters

Statistically significant shapelet mining for biomedical time series
June 2018; invited talk in the graduate seminar of Prof. Dr. Filip Sadlo

Shakespearean Social Network Analysis using Topological Methods
July 2016; lecture in the graduate seminar of Prof. Dr. Filip Sadlo

An introduction to persistent homology
May 2016; public lecture for the SIAM Chapter Heidelberg

Ein Bild sagt mehr als tausend Worte: Graphische Darstellungen komplexer Daten|research
May 2016; public lecture for the ‘Akademische Mittagspause 2016” lecture series

Persistent homology for multivariate data visualization
Februrary 2016; invited talk at Sorbonne Universités UPMC in the research group by Dr. Julien Tierny

Aspects of human perception
June 2015; lecture in a course of Dr. Hubert Mara

Die Poincaré-Vermutung
May 2014; lecture in a course at “Privatgymnasium St. Leon-Rot”, a private high school

Aspekte menschlicher Wahrnehmung
January 2014; lecture in a course of Dr. Hubert Mara

Weniger Klartext reden!
September 2013; public lecture for the “Science Academy”

Oh my god, it’s full of data–A biased & incomplete introduction to visualization
April 2013; lecture in the fellows seminar of my graduate school

Die Poincaré-Vermutung
September 2012; public lecture for the “Science Academy”

Applied algebraic topology
July 2011; informal presentation I gave as a precursor to my Ph.D. project

Research interests

In general, I am interested in methods and techniques that help us understand complex data sets. Mostly, those data sets are high-dimensional point clouds for me, but I am also interested in analysing graphs or, most recently, networks. I am also interested in analysing the behaviour of machine learning techniques, such as dimensionality reduction algorithms or clustering techniques.

According to Rob Hyndman, this makes me a data scientist!

From the mathematical side, I am interested in investigating how to use methods from algebraic topology to support the analysis of multivariate data. Since multivariate, unstructured point clouds are very common in domains such as biology and climate research, there are many potential applications for this kind of research.

During my Ph.D., I mainly used persistent homology as a tool for describing a data set. I also have a general interest in algebraic & differential topology—both fields contain a wealth of potentially useful tools for visualization!

Some of my colleagues also have personal websites (or used to have them; they are still my colleagues, but their websites unfortunately do not exist any more for one reason or another):