# 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.

A current academic curriculum vitæ is available.

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. ^{†}. 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 bastian@rieck.me—it is possible that I am only allowed
to share a publication privately.

## 2019

Set Functions for Time Series

Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt

Preprint, arXiv:1909.12064, 2019.

Wasserstein Weisfeiler–Lehman Graph Kernels

Matteo Togninalli^{†}, Elisabetta Ghisu^{†}, Felipe Llinares-López, Bastian
Rieck, and Karsten Borgwardt

To appear in *Advances in Neural Information Processing Systems 32* (NeurIPS 2019) as a *spotlight presentation*. Also available as arXiv:1906.01277, 2019.

A Wasserstein Subsequence Kernel for Time Series

Christian Bock^{†}, Matteo Togninalli^{†}, Elisabetta Ghisu, Thomas Gumbsch, Bastian Rieck, and Karsten Borgwardt

To appear in the *Proceedings of the 19th IEEE International Conference on Data Mining* (ICDM), 2019.

Topological Autoencoders

Michael Moor^{†}, Max Horn^{†},
Bastian Rieck^{‡}, and Karsten Borgwardt^{‡}

Preprint, arXiv:1906.00722, 2019.

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

Michael Moor, Max Horn,
Bastian Rieck, Damian Roqueiro,
and Karsten Borgwardt

To appear in the *Proceedings of the Machine Learning and Healthcare Conference* (MLHC), 2019.

A Persistent Weisfeiler–Lehman Procedure for Graph Classification

Bastian Rieck^{†}, Christian Bock^{†}, and Karsten Borgwardt

*Proceedings of the 36th International Conference on Machine Learning* (ICML), Volume 97 of *Proceedings of Machine Learning Research*, pp. 5448–5458, June 2019.

BibTeX • GitHub repository • Poster • Slides • Supplementary materials

Visualization of Equivalence in 2D Bivariate Fields

Boyan Zheng, Bastian Rieck, Heike Leitte, and Filip Sadlo

*Computer Graphics Forum*, Volume 38, Issue 3, pp. 311–323, June 2019.

BibTeX

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 • Poster • DOI: 10.3929/ethz-b-000327207

Topological Machine Learning with Persistence Indicator Functions

Bastian Rieck, Filip Sadlo, and Heike Leitte

Preprint, arXiv:1907.13496. To appear in *Topological Methods in Data Analysis and Visualization V*, Springer, 2019.

Hierarchies and Ranks for Persistence Pairs

Bastian Rieck, Filip Sadlo, and Heike Leitte

Preprint, arXiv:1907.13495. 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)

Persistence Concepts for 2D Skeleton Evolution Analysis

Bastian Rieck, Filip Sadlo, and Heike Leitte

Preprint, arXiv:1907.13486. To appear in *Topological Methods in Data Analysis and Visualization V*, Springer, 2019.

GitHub repository

This is the published version of the extended abstract from 2017 (see below)

Persistent Intersection Homology for the Analysis of Discrete Data

Bastian Rieck, Markus Banagl, Filip Sadlo, and Heike Leitte

Preprint, arXiv:1907.13485. To appear in *Topological Methods in Data Analysis and Visualization V*, Springer, 2019.

## 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.

BibTeX • Slides

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*, Ruprecht-Karls-Universität Heidelberg

BibTeX • urn:nbn:de:bsz:16-opus-130111 • DOI: 10.11588/heidok.00013011 • 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 that are not directly associated with a paper, along with their slides.

Perspectives in Persistent Homology

September 2019; Slides for a keynote talk at the Applications of Topological Data Analysis Workshop co-located with ECMLPKDD 2019 in Würzburg

Introduction to Machine Learning for Biology

June 2019; Workshop slides for the 2019 D-BSSE Retreat in Muttenz

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):

- Andreas Beyer
- Daniel Beyer
- Christian Bock
- Bartosz Bogacz
- Jens Fangerau
- Max Horn
- Michael Moor
- Julia Portl