I am a broadly trained computational biologist and bioinformatician with over 13 years of experience in cancer genomics. I have a rich experience in supervising and training bioinformaticians as well as a track record of teamwork within multi-PI projects. Through my interdisciplinary scientific background and communication skills, I am typically the liaison between computational, experimental, and clinical scientists.

My research focuses on the development of genomic profiling methods and associated bioinformatics techniques, with a core expertise in computational and statistical methods for the analysis of integrative genomic data. The overarching goal of my research is to enable discoveries in cancer genetics and to facilitate their rapid translation into the clinic. Throughout my education at the University of Virginia (2008–2013) and subsequent postdoctoral training at the University of Michigan (2013–2015), I have built my expertise and skills in key areas of basic cancer research, including genetics, epigenetics, and transcriptomics. In parallel, I became directly involved in translational cancer genomics, first by implementing novel clinical transcriptome sequencing protocols (Genome Res. 25, 1372–1381), and later by becoming a core member of the MI-ONCOSEQ precision oncology program. I was the bioinformatics lead for MI-ONCOSEQ, and head of my translational cancer genomics research group (2018–2021). Currently, as an Assistant Professor in the Departments of Pathology and Bioinformatics, I lead several projects in the area of immunogenomics in the capacity of a Principal Investigator, and serve as a bioinformatics Co-Investigator on multiple NCI-funded grants focused on cancer genomics. I also collaborate broadly with cancer immunologists, geneticists, and clinical researchers. Since 2016, I also lead computational genomics efforts as part of the University of Michigan Proteogenomic Data Analysis Centers, which is part of the NIH/NCI Clinical Proteomic Tumor Analysis Consortium.

Research interest in my lab focus on two main topics. First, we are interested in dissecting the mechanisms by which cancers escape the immune system. To accomplish this we develop and apply novel computational methods to large-scale genomic and proteomic datasets. We put special emphasis on the major histocompatibility complex (MHC), also known as the HLA, as it is the linchpin of the adaptive immune system, and due to a high degree of polymorphism has been so for underexplored using high-throughput techniques. A second area of interest is genetic instability of advanced cancer. As cancers progress their genomes become increasingly unstable, our goals are to understand how why those changes occur, how to best measure them, and how to turn this knowledge into prognostic and predictive biomarkers.