
Topological Data Analysis of copy number alterations in cancer
Identifying subgroups and properties of cancer biopsy samples is a cruci...
read it

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...
read it

Graph Kernels: StateoftheArt and Future Challenges
Graphstructured data are an integral part of many application domains, ...
read it

Accelerating COVID19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Controlling the COVID19 pandemic largely hinges upon the existence of f...
read it

Uncovering the Topology of TimeVarying fMRI Data using Cubical Persistence
Functional magnetic resonance imaging (fMRI) is a crucial technology for...
read it

Path Imputation Strategies for Signature Models
The signature transform is a 'universal nonlinearity' on the space of co...
read it

Set Functions for Time Series
Despite the eminent successes of deep neural networks, many architecture...
read it

Topological Machine Learning with Persistence Indicator Functions
Techniques from computational topology, in particular persistent homolog...
read it

Persistent Intersection Homology for the Analysis of Discrete Data
Topological data analysis is becoming increasingly relevant to support t...
read it

Wasserstein WeisfeilerLehman Graph Kernels
Graph kernels are an instance of the class of RConvolution kernels, whi...
read it

Topological Autoencoders
We propose a novel approach for preserving topological structures of the...
read it

Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient...
read it

Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis
Motivation: Sepsis is a lifethreatening host response to infection asso...
read it

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
While many approaches to make neural networks more fathomable have been ...
read it
Bastian Rieck
verfied profile
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.