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Niklas works primarily with machine learning and data science. He is an expert in developing and building data-driven models that can automatically learn the underlying structure in the data.
He has worked on projects in different domains with complex data both in practice and in research for example for prediction of temperature fluctuation in connection with wool insulation tests, for analysis of connections in the human brain and for analysis of bio-molecular data.
Niklas focuses in particular on Explainable Artificial Intelligence (XAI) where he uncovers and explores how to make artificial intelligence transparent and explainable to everyone. Furthermore, he is involved in various machine learning projects in areas such as water supply, wastewater treatment and wave prediction.
He has a bachelor's and master's degree in bioinformatics from the University of Tübingen. He also holds a PhD in computer science from the University of Copenhagen.