Research
I am a senior assistant in the Machine Learning & Computational Biology Lab of Prof. Dr. Karsten Borgwardt at ETH Zürich. My research centres on how to understand complex data sets, mostly in biomedical contexts, using (among other things) topologybased machine learning methods. If you are interested in topological machine learning, consider joining TDA in ML, a Slack community dedicated to discussing all things topological machine learning.
See below for a list of my publications and other materials.
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.
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 0000000343350302.
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.
Preprints

Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review
Michael Moor^{†}, Bastian Rieck^{†}, Max Horn, Catherine R. Jutzeler^{‡}, and Karsten Borgwardt^{‡}
Preprint, medRxiv:2020.08.31.20185207v1, 2020. 
Direct Antimicrobial Resistance Prediction from MALDITOF Mass Spectra Profile in Clinical Isolates through Machine Learning
Caroline Weis, Aline Cuénod, Bastian Rieck, Felipe LlinaresLópez, Olivier Dubuis, Susanne Graf, Claudia Lang, Michael Oberle, Kirstine K. Soegaard, Michael Osthoff, Karsten Borgwardt^{‡}, and Adrian Egli^{‡}
Preprint, bioRxiv:2020.07.30.228411, 2020.
GitHub repository 
Path Imputation Strategies for Signature Models of Irregular Time Series
Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, and Bastian Rieck
Preprint, arXiv:2005.12359, 2020.
A shorter version of this paper, entitled Path Imputation Strategies for Signature Models, was accepted for presentation at the Workshop on the Art of Learning with Missing Values (ARTEMISS) at ICML 2020. 
Uncovering the Topology of TimeVarying fMRI Data using Cubical Persistence
Bastian Rieck^{†}, Tristan Yates^{†}, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas TurkBrowne^{‡}, and Smita Krishnaswamy^{‡}.
Preprint, arXiv:2006.07882, 2020.
2020

Topological Methods for fMRI Data
Bastian Rieck^{†}, Tristan Yates^{†}, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas TurkBrowne^{‡}, and Smita Krishnaswamy^{‡}
Accepted for presentation at the 2020 ICML Workshop on Computational Biology.
Slides • An extended version of this paper is available as arXiv:2006.07882. 
Path Imputation Strategies for Signature Models
Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, and Bastian Rieck
Accepted for presentation at the Workshop on the Art of Learning with Missing Values (ARTEMISS) at ICML 2020.
An extended version of this paper is available as arXiv:2005.12359. 
Enhancing statistical power in temporal biomarker discovery through representative shapelet mining
Thomas Gumbsch, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt.
Accepted for presentation at ECCB 2020. 
Graph Filtration Learning
Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, and Roland Kwitt
Accepted for presentation at ICML 2020. 
Set Functions for Time Series
Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt
Accepted for presentation at ICML 2020. 
Topological Autoencoders
Michael Moor^{†}, Max Horn^{†}, Bastian Rieck^{‡}, and Karsten Borgwardt^{‡}
Accepted for presentation at ICML 2020.
GitHub repository 
Comorbidities, Clinical Signs and Symptoms, Laboratory Findings, Imaging Features, Treatment Strategies, and Outcomes in Adult and Pediatric Patients with COVID19: A Systematic Review and MetaAnalysis
Catherine R. Jutzeler^{†}, Lucie Bourguignon^{†}, Caroline V. Weis, Bobo Tong, Cyrus Wong, Bastian Rieck, Hans Pargger, Sarah TschudinSutter, Adrian Egli, Karsten Borgwardt^{‡} and Matthias Walter^{‡}
To appear in Travel Medicine and Infectious Disease. Also available as medRxiv:2020.05.20.20103804v1, 2020. 
Networkguided search for genetic heterogeneity between gene pairs
Anja C. Gumpinger, Bastian Rieck, Dominik D. Grimm, and Karsten Borgwardt
To appear in Bioinformatics.
GitHub repository 
Topological and kernelbased microbial phenotype prediction from MALDITOF mass spectra
Caroline Weis^{†}, Max Horn^{†}, Bastian Rieck^{†}, Aline Cuénod, Adrian Egli, and Karsten Borgwardt
Accepted for presentation at ISMB 2020; also available in Bioinformatics, Volume 36, Issue Supplement_1, pp. i30–i38.
BibTeX • GitHub repository 
Early prediction of circulatory failure in the intensive care unit using machine learning
Stephanie L. Hyland^{†}, Martin Faltys^{†}, Matthias Hüser^{†}, Xinrui Lyu^{†}, Thomas Gumbsch^{†}, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt^{‡}, Gunnar Rätsch^{‡}, and Tobias M. Merz^{‡}
Nature Medicine, Volume 26, Issue 3, pp. 364–373, March 2020.
BibTeX • GitHub repository
2019

A Wasserstein Subsequence Kernel for Time Series
Christian Bock^{†}, Matteo Togninalli^{†}, Elisabetta Ghisu, Thomas Gumbsch, Bastian Rieck, and Karsten Borgwardt
Accepted for presentation at the Optimal Transport & Machine Learning Workshop (OTML) at NeurIPS 2019.
GitHub repository • This is an extended version of the ICDM paper of the same name. Please use the citation for our ICDM paper. 
Wasserstein Weisfeiler–Lehman Graph Kernels
Matteo Togninalli^{†}, Elisabetta Ghisu^{†}, Felipe LlinaresLópez, Bastian Rieck, and Karsten Borgwardt
Advances in Neural Information Processing Systems 32 (NeurIPS 2019), pp. 6436–6446. Also available as arXiv:1906.01277, 2019.
BibTeX • GitHub repository 
A Wasserstein Subsequence Kernel for Time Series
Christian Bock^{†}, Matteo Togninalli^{†}, Elisabetta Ghisu, Thomas Gumbsch, Bastian Rieck, and Karsten Borgwardt
Proceedings of the 19th IEEE International Conference on Data Mining (ICDM), pp. 964–969, 2019.
BibTeX • GitHub repository 
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
Proceedings of the 4th Machine Learning for Healthcare Conference (MLHC), Volume 106 of Proceedings of Machine Learning Research, pp. 2–26, August 2019.
BibTeX • GitHub 
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/ethzb000327207 
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 TimeDependent 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, RuprechtKarlsUniversität Heidelberg
BibTeX • urn:nbn:de:bsz:16heidok229145 • DOI: 10.11588/heidok.00022914 • Textonly version 
Persistence Concepts for 2D Skeleton Evolution Analysis (extended abstract)
Bastian Rieck, Filip Sadlo, and Heike Leitte
Accepted for presentation at the Workshop on TopologyBased Methods in Visualization (TopoInVis), 2017.
BibTeX • GitHub repository 
Hierarchies and Ranks for Persistence Pairs
Bastian Rieck, Filip Sadlo, and Heike Leitte
Accepted for presentation at the Workshop on TopologyBased Methods in Visualization (TopoInVis), 2017.
BibTeX • 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)
2016

‘Shall I compare thee to a network?’—Visualizing the Topological Structure of Shakespeare’s Plays
Bastian Rieck and Heike Leitte
Accepted for presentation at the 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 
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
Accepted for presentation at the 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
Accepted for presentation at the Workshop on TopologyBased Methods in Visualization (TopoInVis), 2015.
BibTeX • Slides
2014

Enhancing Comparative Model Analysis using Persistent Homology
Bastian Rieck and Heike Leitte
Accepted for presentation at the IEEE Vis Workshop on Visualization for Predictive Analytics, 2014.
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 BadenWürttemberg, Nachrichtenblatt der Landesdenkmalpflege, Heft 32013, S. 165–168. 
Unwrapping HighlyDetailed 3D Meshes of Rotationally Symmetric ManMade Objects
Bastian Rieck, Hubert Mara, and Susanne Krömker
ISPRS Annals, Volume II5/W1, pp. 259–264.
BibTeX
2012
 Multivariate Data Analysis Using PersistenceBased 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, RuprechtKarlsUniversität Heidelberg
BibTeX • urn:nbn:de:bsz:16opus130111 • DOI: 10.11588/heidok.00013011 • GitHub repository
Notes
Here are some notes on various topics. These notes are not peerreviewed, 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.

TopologyBased Representation Learning
July 2020; slides for an invited talk at the research group of Prof. Kathryn Hess Bellwald at EPFL
This PDF is best viewed with an application that supports animations, such as Adobe Acrobat Reader DC 
The Many Faces of Manifolds
February 2020; slides for the intradepartmental seminar at DBSSE in Basel 
Introduction to TopologyBased Graph Classification
January 2020; slides for an invited talk at the AI & Topology session of the Applied Machine Learning Days in Lausanne 
Perspectives in Persistent Homology
September 2019; slides for a keynote talk at the Applications of Topological Data Analysis Workshop colocated with ECMLPKDD 2019 in Würzburg 
Introduction to Machine Learning for Biology
June 2019; workshop slides for the 2019 DBSSE 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 Datenresearch
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 PoincaréVermutung
May 2014; lecture in a course at ‘Privatgymnasium St. LeonRot’, 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 PoincaréVermutung
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 highdimensional 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 visualisation and machine learning!
Some of my colleagues also have personal websites: