Our research involves developing new methods for complex data analysis. In the context of high-dimensional data, we have for example proposed powerful approaches to handle multi-omics data, which can be used to better understand gene expression regulation by considering DNA copy number and methylation together with gene expression, or to better understand how quantitative trait loci affect splicing. We also have experience in analysing SNP data from family association studies, and in method development for prediction using high-dimensional data. We also have developed methods to model net survival in epidemiological studies, and to pre-process and analyse whole-genome genetic screen data. Finally, we also have large experience with mixed-effects models as well as machine learning tools.