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) 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

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

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 These notes are not peer-reviewed, though, and may contain errors—please notify me if you find some.

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: