Biomathematik, Fachbereich Mathematik und Technik

  • We use methods of machine learning, statistics and also mathematical models to answer biological and medical questions. Depending on the problem, this can be

    • Development of new machine learning or new statistical methods related to the life sciences
    • Data analysis and mathematical modelling
    • Data driven control of biomedical systems (e.g. optimal treatment strategies)
    • Causal Modelling

    Currently, we focus on two application areas

    • molecular cancer research (Computational biology)
    • Clinical decision support with AI methods (Biomedical data science)

    An important aspect of our work is the close cooperation with the life sciences, because without the deep domain knowledge we cannot make any scientific progress. We have a many years of experience with high dimensional and multimodal data. Wir haben auch Erfahrungen in der Verknüfung von Kontrolltheorie und Machine Learning


    Koblenz University of Applied Sciences
    Fachbereich Mathematik und Technik
    Joseph-Rovan-Allee 2
    53424 Remagen
    ++ 49 (0) 2642932330

Leading Researchers

Special Expertise

  • Basic Research

    • Machine Learning (ML): Self-Supervised Learning, (Semi) Supervised Learning, Artificial Neural Network (ANN), Decision Tree, Bayesian Neural Networks, Model Based, Generative Models, Dimensionality Reduction, Feature Engineering/Feature Extraction
    • Knowledge-Based Systems: Causality
    • Robotics: Control Algorithms, Simulation Technology

  • Application related Research

    • Smart Assistant Systems: Predictive Analysis, Digital Medicine
    • Perception and Sensor Fusion: Diagnostics
    • Information Retrieval (Knowledge / Data Management and Analysis): Knowledge Discovery in Databases (Data Mining), Decision Support
    • Technology Analysis: Sociological Aspects

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