We are interested in the development of computational tools to characterize the patterns of mutations and genome instability processes in human cancers through the analysis of genome sequencing data from clinical samples and preclinical models. Our research areas include the discovery of biomarkers of drug response, inference of the molecular mechanisms underlying cancer evolution, early detection of cancer, and the identification of the somatic alterations predictive of response and resistance to immunotherapies. We are also interested in the application of artificial intelligence to model drug response using genomically characterized preclinical models to uncover new vulnerabilities of cancer.