Bastian Rieck

Welcome to my personal and academic space! I am a senior assistant in the Machine Learning & Computational Biology Lab of Prof. Dr. Karsten Borgwardt at ETH Zürich.

Previously, I obtained my Ph.D. in computer science from Heidelberg University, which is where I also got my diploma in mathematics. This site contains some things I care about.

I can be reached at:

Building BSB, room 2/001
Mattenstrasse 26
4058 Basel
Switzerland

The icons above should cover everything, but in case you prefer text: a current academic curriculum vitæ is available. My ORCID is 0000-0003-4335-0302. I also have a Google Scholar profile, a ResearchGate profile, a publons profile, and a GitHub account. Sometimes, I tweet things.

News

2020-07-03: Our work Enhancing statistical power in temporal biomarker discovery through representative shapelet mining was accepted for presentation at ECCB 2020.
2020-07-02: Happy to hear that our work 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.
2020-06-18: Our work Topological Methods for fMRI Data was accepted for presentation at the 2020 ICML Workshop on Computational Biology.
2020-06-11: Our workshop submission Kernel-based antimicrobial resistance prediction from MALDI-TOF mass spectra was accepted for a spotlight talk at the ML for Global Health Workshop at ICML 2020.
2020-06-01: Truly elated to see three papers accepted at ICML 2020: Graph Filtration Learning, Set Functions for Time Series, and Topological Autoencoders. A big thanks to all collaborators!
2020-04-14: Glad to see our work Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra accepted at ISMB 2020.
2020-03-09: Happy to see that our paper Early prediction of circulatory failure in the intensive care unit using machine learning was published in Nature Medicine!
2019-12-20: I am honoured to have been invited to give a talk on topology-based graph classification at the AI & Topology track of the Applied Machine Learning Days at EPFL.
2019-12-10: I created a Slack community for discussing topological machine learning. Please join TDA in ML if you are interested!
2019-11-18: I am honoured to receive a Spark grant from the Swiss National Science Foundation to fund my proposal TOPAZ: Topology of Alzheimer’s.
2019-11-07: Our work Sparse representations for MALDI-TOF based microbial classification was accepted as a poster presentation at the Machine Learning and Computational Biology Meeting (MLCB).
2019-11-01: Our work Topological Autoencoders has been accepted for an oral presentation at the Swiss Machine Learning Day 2019 (SMLD).
2019-09-27: I am truly honoured to serve as a mentor for the New in ML 2019 Workshop among so many of my role models.
2019-09-16: The slides for my keynote talk at the Applications of Topological Data Analysis Workshop are now available.
2019-09-04: I am delighted to receive an outstanding reviewer award for the ECMLPKDD 2019 Journal Track.
2019-09-03: Our work Wasserstein Weisfeiler–Lehman Graph Kernels has been accepted at NeurIPS 2019 with a spotlight presentation.
2019-09-03: I am honoured to receive a free NeurIPS 2019 registration for being an outstanding reviewer.
2019-08-09: Our work A Wasserstein Subsequence Kernel for Time Series has been accepted at the IEEE International Conference on Data Mining (ICDM).
2019-08-01: Four new preprints have been added. They will become chapters of the upcoming book Topological Methods in Data Analysis and Visualization V.
2019-07-06: Our work Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping has been accepted for a poster presentation and a spotlight talk at the Machine Learning for Healthcare conference (MLHC).
2019-05-20: I am honoured to have been invited to give a keynote talk at the Applications of Topological Data Analysis Workshop, co-located with ECMLPKDD 2019.
2019-04-21: Our work A Persistent Weisfeiler–Lehman Procedure for Graph Classification has been accepted for a poster presentation and a short talk at the International Conference for Machine Learning (ICML).
2018-12-18: Our work Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology has been accepted for a poster presentation at the International Conference for Learning Representations (ICLR).