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Intern
    Data Science Chair

    Research

    At the Data Science chair, we focus on diverse set of research topics, all connected by the common interest of fundamentals in machine learning and the applications we have. Find out more about our research topics: 

    We collaborate with literary scholars on questions like detection of direct speech or sentiment analysis in literature as well as chat data from streaming platforms. 

    Deep learning for imbalanced data

    We research methods for dealing with unbalanced data and use these to answer questions regarding environmental factors or predict rare biological and climate-related events using machine learning.

    deep learning for recommendation

    We are developing recommender systems with deep learning for our applications in a variety of application scenarios.

    We're developing deep learning methods for physical and mathematical processes.

    Our common interests lie in finding general applicable methods for our applications.

    Here you can find a complete overview of all our publications, not separated by topics.