Data Science

In the research area Data Science, we focus on the development and application of data-driven methods for solving biological, medical, and technical problems. In close cooperation with our partners from research and industry, we develop algorithms for preprocessing and analyzing data using statistical methods and machine learning, as well as visualizing and interpreting the results. For example, within the project LeiVMed Online we develop a platform for benchmarking and visualizing data of treatments of patients in hospitals and developing prediction models for the outcome of treatments.

In many of our projects, we see the need for customized data processing pipelines due to the heterogeneity and complexity of data structures in real world processes and systems. We use applied statistics as well as numerous machine learning approaches, including black box methods (deep neural nets, random forests, gradient boosted trees, etc.) and white box methods (symbolic regression by genetic programming). We build our knowledge discovery pipelines on a variety of different frameworks, especially python scikit-learn, tensorflow, pytorch, MATLAB, and HeuristicLab.



Selected Publications

Selected Projects


In collaboration with members of the research group for clinical core processes at FH Upper Austria, Steyr we are working on the analysis of data of treatments of patients in Austrian hospitals.


FH-Prof. Dr. Klaus Arthofer


FH-Prof. Dr. Klaus Arthofer

Dr. Gerhard Halmerbauer

Ramona Haslinger B.Stat., MSc

Susanne Schaller MMSc

Julia Vetter BBSc

Prof.(FH) DI Dr. Stephan Winkler  


07/2017 – present

Research Areas


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