My research focuses on utilizing various computational approaches to identify novel inherited genomic predictors of cancer risk, disease progression, and response to therapy and explore clinically focused questions using genomic, transcriptomic, and methylation data. Towards that end, I have developed a germline computational genomics program within the Clinical Computational Oncology Laboratory at Dana-Farber Cancer Institute and made contributions to identify novel germline drivers of metastatic transformation in advanced prostate cancer that informed the NCCN recommendations for universal germline genetic testing in this patient population. In addition, I led the effort to identify two novel colorectal cancer (CRC) predisposition genes (ATM and PALB2), collectively explaining CRC risk in 1.2% of all unselected and early-onset CRC patients and expanding the molecular diagnostic yield of germline genetic testing by 20%. Furthermore, I spearheaded an international effort to identify the first Mendelian germline predisposition gene in testicular germ cell tumors, CHEK2, with potentially immediate clinical and mechanistic implications. More recently, I have developed a particular interest in applying machine-learning approaches to the germline genetic data of cancer patients to uncover clinically informative germline drivers of cancer risk, cancer progression and response to therapy, an effort that may introduce a new paradigm for deep learning-based clinical germline variant characterization. Overall, I am a physician-scientist active in clinical and translational research that aims to make biological discoveries using germline-inclusive integrative computational cancer genomics.