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

Screening 2.0

In Austria there are currently around 573.000 to 645.000 people affected by Diabetes mellitus. This is about 8 to 9 percent of all Austrians. About 143.000 to 215.000 people do not know about their diabetes yet (2 to 3%). They have a higher risk for complications and long-term effects. Because of demographic developments and the rising prevalence of lifestyle related risk factors (overweight/obesity, physical inactivity, unfavourable nutrition, smoking, etc.) the number of diabetics and diabetes related diseases will rise further.

To counter these developments the consortium of Screening 2.0 aims at the development of a comprehensive concept of non-invasive diagnostic tools for individual, exhaustive, and user-friendly diabetes screening (diabetes as a first step) in combination with e-health applications. It involves a service innovation combined with a product innovation:


* Development of a printed qualitative diabetes-screening strip used in a trend analysis for early detection of diabetes


* Development of a comprehensive communication concept


Test strips and screening methods already exist in various ways. However, these are not printed but mostly dipped or coated. Through the development of the printing process in combination with the development of the suitable print varnish production cost and used resources should be reduced significantly. Furthermore the strip must be designed in a way to be easily included in a postal mailing which is the prerequisite for the service innovation.

To implement early detection successfully into the existing health system an innovative service must be developed. Current communication- and supply channels need to be considered while it needs to be carried out nationwide. Screening enables an addition to current supply and communication. Therefore the communication cycle can be closed for the first time during the screening process and not – as before – after the analysis of prescribed medication (therapy) through health insurances. The figure depicts how the screening can extend the existing supply and communication chain.


DI(FH) Viktoria Dorfer MSc


DI(FH) Viktoria Dorfer MSc

Susanne Schaller MMSc

Prof. (FH) DI Dr. Stephan Winkler


2015 - present

Research Areas



Research Institutions

Research Center Hagenberg

University of Applied Sciences, Upper Austria, Hagenberg Campus

Research focus

Software technology and application

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