Bastian Rieck

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ETH Zurich ∙ Technische Universität Kaiserslautern

Bastian Rieck is a senior assistant in the Machine Learning and Computational Biology Lab of Prof. Dr. Karsten Borgwardt at ETH Zurich. 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

Gunnar Rätsch
29 publications
Guy Wolf
25 publications
Smita Krishnaswamy
18 publications
Karsten Borgwardt
12 publications
Stephanie L. Hyland
10 publications
Michael Moor
7 publications
Jannis Born
6 publications
Max Horn
6 publications
Filip Sadlo
6 publications
Matthias Hüser
5 publications
Christian Bock
5 publications

Topological Data Analysis of copy number alterations in cancer

Identifying subgroups and properties of cancer biopsy samples is a cruci...
11/22/2020 ∙ by Stefan Groha, et al. ∙ 22 ∙ share

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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...
11/12/2020 ∙ by Sarah C. Brüningk, et al. ∙ 25 ∙ share

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

Graph-structured data are an integral part of many application domains, ...
11/07/2020 ∙ by Karsten Borgwardt, et al. ∙ 95 ∙ share

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Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

Controlling the COVID-19 pandemic largely hinges upon the existence of f...
09/13/2020 ∙ by Jannis Born, et al. ∙ 23 ∙ share

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

Functional magnetic resonance imaging (fMRI) is a crucial technology for...
06/14/2020 ∙ by Bastian Rieck, et al. ∙ 0 ∙ share

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Path Imputation Strategies for Signature Models

The signature transform is a 'universal nonlinearity' on the space of co...
05/25/2020 ∙ by Michael Moor, et al. ∙ 10 ∙ share

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Set Functions for Time Series

Despite the eminent successes of deep neural networks, many architecture...
09/26/2019 ∙ by Max Horn, et al. ∙ 0 ∙ share

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Topological Machine Learning with Persistence Indicator Functions

Techniques from computational topology, in particular persistent homolog...
07/31/2019 ∙ by Bastian Rieck, et al. ∙ 0 ∙ share

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Persistent Intersection Homology for the Analysis of Discrete Data

Topological data analysis is becoming increasingly relevant to support t...
07/31/2019 ∙ by Bastian Rieck, et al. ∙ 0 ∙ share

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Wasserstein Weisfeiler-Lehman Graph Kernels

Graph kernels are an instance of the class of R-Convolution kernels, whi...
06/04/2019 ∙ by Matteo Togninalli, et al. ∙ 0 ∙ share

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Topological Autoencoders

We propose a novel approach for preserving topological structures of the...
06/03/2019 ∙ by Michael Moor, et al. ∙ 0 ∙ share

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

Intensive care clinicians are presented with large quantities of patient...
04/16/2019 ∙ by Stephanie L. Hyland, et al. ∙ 0 ∙ share

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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...
02/05/2019 ∙ by Michael Moor, et al. ∙ 0 ∙ share

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

While many approaches to make neural networks more fathomable have been ...
12/23/2018 ∙ by Bastian Rieck, et al. ∙ 16 ∙ share

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