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) topology-based 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 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.

Preprints

• Topological Graph Neural Networks
  Max Horn^†, Edward De Brouwer^†, Michael Moor, Yves Moreau, Bastian Rieck^†, ‡, and Karsten Borgwardt^‡
  Preprint arXiv:2102.07835, 2021.

• Topological Analysis of Single-Cell Data Reveals Shared Glial Landscape of Macular Degeneration and Neurodegenerative Diseases
  Manik Kuchroo^†, Marcello DiStasio^†, Eda Calapkulu, Maryam Ige, Le Zhang, Amar H. Sheth, Madhvi Menon, Yu Xing, Scott Gigante, Jessie Huang, Rahul M. Dhodapkar, Bastian Rieck, Guy Wolf, Smita Krishnaswamy^‡, and Brian P. Hafler^‡
  Preprint, bioRxiv:2021.01.19.427286v1, 2021.

• Topological Data Analysis of Copy Number Alterations in Cancer
  Stefan Groha^†, Caroline Weis^†, Alexander Gusev^‡, and Bastian Rieck^‡.
  Preprint, arXiv:2011.11070, 2020.

• 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 MALDI-TOF Mass Spectra Profile in Clinical Isolates through Machine Learning
  Caroline Weis, Aline Cuénod, Bastian Rieck, Felipe Llinares-López, 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.

2021

• Stable Topological Signatures for Metric Trees through Graph Approximations
  Robin Vandaele, Bastian Rieck, Yvan Saeys, and Tijl De Bie
  To appear in Pattern Recognition Letters.

• Exploring Epithelial-Cell Calcium Signaling with Geometric and Topological Data Analysis
  Feng Gao, Jessica Moore, Bastian Rieck, Valentina Greco, and Smita Krishnaswamy
  Accepted for presentation at the Geometrical and Topological Representation Learning at ICLR 2021.
  

• Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
  Jannis Born^†, Nina Wiedemann^†, Manuel Cossio, Charlotte Buhre, Gabriel Brändle, Konstantin Leidermann, Avinash Aujayeb, Michael Moor, Bastian Rieck, and Karsten Borgwardt
  Applied Sciences, Volume 11, No. 2, 2021.

2020

• Topological Machine Learning with Persistence Indicator Functions
  Bastian Rieck, Filip Sadlo, and Heike Leitte
  Topological Methods in Data Analysis and Visualization V, pp. 87–101, Springer, 2020. Also available as arXiv:1907.13496.
  BibTeX

• Hierarchies and Ranks for Persistence Pairs
  Bastian Rieck, Filip Sadlo, and Heike Leitte
  Topological Methods in Data Analysis and Visualization V, pp. 3–17, Springer, 2020. Also available as arXiv:1907.13495.
  BibTeX • 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
  Topological Methods in Data Analysis and Visualization V, pp. 139–154, Springer, 2020. Also available as arXiv:1907.13486.
  BibTeX • 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
  Topological Methods in Data Analysis and Visualization V, pp. 37–51, Springer, 2020. Also available as arXiv:1907.13485.
  BibTeX

• Image Analysis for Alzheimer’s Disease Prediction: Embracing Pathological Hallmarks for Model Architecture Design
  Sarah C. Brüningk^†, Felix Hensel^†, Catherine R. Jutzeler^‡, and Bastian Rieck^‡
  Accepted for presentation at the Machine Learning for Healthcare Workshop at NeurIPS 2020. Also available as arXiv:2011.06531.

• Graph Kernels: State-of-the-Art and Future Challenges
  Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O’Bray, and Bastian Rieck
  Foundations and Trends® in Machine Learning, Volume 13, No. 5–6, pp. 531–712, 2020. Also available as arXiv:2011.03854.
  BibTeX

• Challenging Euclidean Topological Autoencoders
  Michael Moor, Max Horn, Karsten Borgwardt, and Bastian Rieck
  Accepted for presentation at the Topological Data Analysis and Beyond Workshop at NeurIPS 2020.
  

• Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
  Bastian Rieck^†, Tristan Yates^†, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne^‡, and Smita Krishnaswamy^‡.
  To appear in Advances in Neural Information Processing Systems 33 (NeurIPS 2020) as a spotlight presentation. Also available as arXiv:2006.07882, 2020.
  GitHub repository • Poster • Short video • Long video

• Topological Methods for fMRI Data
  Bastian Rieck^†, Tristan Yates^†, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne^‡, 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; also available in Bioinformatics, Volume 36, Issue Supplement_2, pp.  i840–i848.
  BibTeX • GitHub repository

• Graph Filtration Learning
  Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, and Roland Kwitt
  Accepted for presentation at ICML 2020. Also appears in Proceedings of the 37th International Conference on Machine Learning, Volume 119 of Proceedings of Machine Learning Research, pp. 4314–4323, 2020.

• Set Functions for Time Series
  Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt
  Accepted for presentation at ICML 2020. Also appears in Proceedings of the 37th International Conference on Machine Learning, Volume 119 of Proceedings of Machine Learning Research, pp. 4353–4363, 2020.

• Topological Autoencoders
  Michael Moor^†, Max Horn^†, Bastian Rieck^‡, and Karsten Borgwardt^‡
  Accepted for presentation at ICML 2020. Also appears in Proceedings of the 37th International Conference on Machine Learning, Volume 119 of Proceedings of Machine Learning Research, pp. 7045–7054, 2020.
  BibTeX • GitHub repository

• Comorbidities, Clinical Signs and Symptoms, Laboratory Findings, Imaging Features, Treatment Strategies, and Outcomes in Adult and Pediatric Patients with COVID-19: A Systematic Review and Meta-Analysis
  Catherine R. Jutzeler^†, Lucie Bourguignon^†, Caroline V. Weis, Bobo Tong, Cyrus Wong, Bastian Rieck, Hans Pargger, Sarah Tschudin-Sutter, Adrian Egli, Karsten Borgwardt^‡ and Matthias Walter^‡
  Travel Medicine and Infectious Disease, Volume 37, September–October 2020, 101825. Also available as medRxiv:2020.05.20.20103804v1.

• Network-guided 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 kernel-based microbial phenotype prediction from MALDI-TOF 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 Llinares-López, 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/ethz-b-000327207

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
  Accepted for presentation at the Workshop on Topology-Based 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 Topology-Based 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 Topology-Based 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 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.

• A note on the relationship between PCA and SVD
• Basic Analysis of Bin-Packing Heuristics

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 visualisation and machine learning!

Some of my colleagues also have personal websites:

• Daniel Beyer
• Christian Bock
• Max Horn
• Michael Moor
• Leslie O’Bray
• Julia Portl
• Caroline Weis