Currently, we are especially interested in the analysis of sequencing data from next-generation sequencing experiments and gene expression data from DNA microarrays. In close collaboration with groups from biology and medicine we are studying disease related questions with a special focus on cancer. An ultimate goal of our research is to contribute to the deciphering of regulatory networks with respect to their reconstruction and functional analysis to shed light on causal mechanisms underlying complex diseases and pathological phenotypes.
In order to analyze high-dimensional and heterogeneous data sets efficiently, we engage in innovative interdisciplinary methodological research to develop and improve computational, statistical and mathematical methods. Specifically, we are interested in Bayesian statistics, exploratory data analysis, computational network theory, machine learning, high-dimensional statistics, and nonparametric statistical inference methods and their application to problems in systems and precision medicine.
A common denominator that is shared by all of our projects is that they are data-driven. This ensures the immediate applicability of our methods and their relevance to solve real world problems.