The group develops machine learning methods, models and tools for computational sciences, in particular computational biology. The methodological backbone of the group is kernel methods and regularized learning. The group particularly focusses in learning with multiple and structured targets, multiple views and ensembles. Applications of interest in computational biology include protein function and interaction prediction as well as molecular classification and identification.