Our research focuses on utilizing experimental and computational approaches to elucidate 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 treatments. 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 belief is that by developing a next-generation of machine learning approaches and uniting them with novel experimental techniques, we can obtain a predictive-understanding of the basic molecular processes contributing to cancer develop, which will allow us to both better prevent and better treat human cancer.