Bastian Rieck

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ETH Zurich

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Technische Universität Kaiserslautern

Bastian Rieck is a principal investigator at the Institute of AI for Health, Helmholtz Zentrum München, where he heads the ADIOS lab. While broadly interested in investigating graph classification and time series analysis from a topological perspective, he is also enticed by finding new ways to explain neural networks using concepts from algebraic and differential topology. Bastian is a big proponent of scientific outreach and enjoys blogging about his research, academia, supervision, and software development.

Featured Co-authors

Peter Bühlmann
43 publications
Martin Grohe
37 publications
Gunnar Rätsch
36 publications
Guy Wolf
34 publications
Nicolai Meinshausen
25 publications
Smita Krishnaswamy
24 publications
Yaron Lipman
21 publications
Haggai Maron
20 publications
Nils M. Kriege
18 publications
Matthew Hirn
17 publications
Karsten Borgwardt
17 publications

∙ 12/18/2021

Weisfeiler and Leman go Machine Learning: The Story so far

In recent years, algorithms and neural architectures based on the Weisfe...

Christopher Morris, et al. ∙

∙ 11/15/2021

Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification

Owing to its pristine soft-tissue contrast and high resolution, structur...

Merel Kuijs, et al. ∙

∙ 10/28/2021

The magnitude vector of images

The magnitude of a finite metric space is a recently-introduced invarian...

Michael F Adamer, et al. ∙

∙ 10/27/2021

Towards a Taxonomy of Graph Learning Datasets

Graph neural networks (GNNs) have attracted much attention due to their ...

Renming Liu, et al. ∙

∙ 07/12/2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Despite decades of clinical research, sepsis remains a global public hea...

Michael Moor, et al. ∙

∙ 06/02/2021

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions

Graph generative models are a highly active branch of machine learning. ...

Leslie O'Bray, et al. ∙

∙ 04/25/2021

Basic Analysis of Bin-Packing Heuristics

The bin-packing problem continues to remain relevant in numerous applica...

Bastian Rieck, et al. ∙

∙ 02/15/2021

Topological Graph Neural Networks

Graph neural networks (GNNs) are a powerful architecture for tackling gr...

Max Horn, et al. ∙

∙ 11/22/2020

Topological Data Analysis of copy number alterations in cancer

Identifying subgroups and properties of cancer biopsy samples is a cruci...

Stefan Groha, et al. ∙

∙ 11/12/2020

Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design

Alzheimer's disease (AD) is associated with local (e.g. brain tissue atr...

Sarah C. Brüningk, et al. ∙

∙ 11/07/2020

Graph Kernels: State-of-the-Art and Future Challenges

Graph-structured data are an integral part of many application domains, ...

Karsten Borgwardt, et al. ∙

∙ 09/13/2020

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

Controlling the COVID-19 pandemic largely hinges upon the existence of f...

Jannis Born, et al. ∙

∙ 06/14/2020

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Functional magnetic resonance imaging (fMRI) is a crucial technology for...

Bastian Rieck, et al. ∙

∙ 05/25/2020

Path Imputation Strategies for Signature Models

The signature transform is a 'universal nonlinearity' on the space of co...

Michael Moor, et al. ∙

∙ 09/26/2019

Set Functions for Time Series

Despite the eminent successes of deep neural networks, many architecture...

Max Horn, et al. ∙

∙ 07/31/2019

Topological Machine Learning with Persistence Indicator Functions

Techniques from computational topology, in particular persistent homolog...

Bastian Rieck, et al. ∙

∙ 07/31/2019

Persistent Intersection Homology for the Analysis of Discrete Data

Topological data analysis is becoming increasingly relevant to support t...

Bastian Rieck, et al. ∙

∙ 06/04/2019

Wasserstein Weisfeiler-Lehman Graph Kernels

Graph kernels are an instance of the class of R-Convolution kernels, whi...

Matteo Togninalli, et al. ∙

∙ 06/03/2019

Topological Autoencoders

We propose a novel approach for preserving topological structures of the...

Michael Moor, et al. ∙

∙ 04/16/2019

Machine learning for early prediction of circulatory failure in the intensive care unit

Intensive care clinicians are presented with large quantities of patient...

Stephanie L. Hyland, et al. ∙

∙ 02/05/2019

Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis

Motivation: Sepsis is a life-threatening host response to infection asso...

Michael Moor, et al. ∙

∙ 12/23/2018

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

While many approaches to make neural networks more fathomable have been ...

Bastian Rieck, et al. ∙