We work on machine learning and deep learning methods, with a focus on biomedical applications. Our activities include the development of statistical models for prediction, feature selection, clustering, network inference and image analysis. Currently we have research projects on identifying soil degradation in the Swiss Alps based on remote sensing data, modeling bird migration with Physics-informed neural networks, and analyzing ECG data for diagnosis support for cardiovascular diseases. In previous research projects we worked on EEG analysis in the context of Parkinson dementia, on DNA sequence analysis for HIV therapy optimization, on copula distributions and on causal models.