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

2021 Stefan Anlauf, Andreas Haghofer, Karl Dirnberger, Stephan M. Winkler: Data-Based Prediction of Microbial Contamination in Herbs and Identification of Optimal Harvest Parameters International Journal of Food Engineering (IJFE), DeGruyter
2019 Renate Haselgrübler, Peter Lanzerstorfer, Clemens Röhrl, Flora Stübl, Jonas Schurr, Bettina Schwarzinger, Clemens Schwarzinger, Mario Brameshuber, Stefan Wieser, Stephan M. Winkler, and Julian Weghuber: Hypolipidemic effects of herbal extracts by reduction of adipocyte differentiation, intracellular neutral lipid content, lipolysis, fatty acid exchange and lipid droplet motility Scientific Reports
2018 Stephan M. Winkler: Evolutionary Computation and Symbolic Regression in Scientific Modeling , Johannes Kepler University Linz, Computer Science, Habilitation Thesis
2016 Daniela Borgmann, Sandra Mayr, Helene Polin, Susanne Schaller, Viktoria Dorfer, Christian Gabriel, Stephan Winkler, and Jaroslaw Jacak: Single Molecule Florescence Microscopy and Machine Learning for Rhesus D Antigen Classification Scientific Reports 6

Selected Projects


Moderne Fluoreszenz-Bildgebungstechniken gewinnen in allen Forschungsbereichen der Life Sciences, speziell in der biomedizinischen Diagnostik, z.B. in den Bereichen Tissue Engineering und Zell-Analyse im Mikro- und Nanometer-Bereich, kontinuierlich an Bedeutung. Im Rahmen des Projekts TOMO3D soll ein Fluoreszenzmikroskopie-Setup zur 3D Bildgebung an der FH OÖ aufgebaut werden. Durch dieses Setup soll es möglich werden, die Kombination einer dreidimensionalen Stage mit Fluoreszenzmikroskopie in der Diagnostik und im Monitoring in der Biomedizin einsetzen zu können. In Kombination mit Methoden der Bioinformatik, die in diesem Projekt entwickelt werden, sollen folgende biomedizinische Forschungsziele erreicht werden: Ermöglichung von a) mikroskopischen Analysen von Knorpelgewebe in 3D im Zusammenhang mit regenerativer Medizin und Tissue Engineering, b) nanoskopischen 3D-Untersuchungen von Gewebe zur Klassifikation von Krankheitsfortschritten, und c) Proteindichteanalysen von 3D-Polymerstrukturen und Gewebeschnitten.


Prof.(FH) DI Dr. Stephan Winkler


Daniela Borgmann MSc

Elisabeth Daniel MSc

Lisa Obritzberger MMSc

Susanne Schaller MMSc


2015 - present

Research Areas



Research Institutions

University of Applied Sciences, Upper Austria, Hagenberg Campus

University of Applied Sciences, Upper Austria, Linz Campus

Research focus

Software technology and application 



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