Our research focuses on understanding the information hidden in large-scale omics datasets. We are particularly interested in elucidating the mechanisms by which cancers develop and in leveraging this knowledge for the development of better cancer prevention strategies and the improved targeting of existing cancer treatment. Throughout the past five years, our work has predominately focused on creating the concept of mutational signatures, on demonstrating the utility of mutational signatures in understanding human cancer, and on identifying mutational signatures in a plethora of diverse cancer types. Our believe is that by developing a next-generation of machine learning approaches, we can obtain a predictive-understanding of the basic molecular processes contributing to cancer develop, which will allow us to better prevent and treat human cancer. Below, you can find a list of select current and prior projects as well as a list of our current funding sources.