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Rachel Karchin is a pioneer in computational cancer genomics, renowned for developing innovative algorithms for variant interpretation and tumor evolution. She created CHASM, a widely used statistical learning method that distinguishes driver from passenger mutations, and led the development of OpenCRAVAT, an open-source variant annotation toolkit that has been widely adopted. Her contributions extend to immuno-oncology, with the BigMHC model driven by deep learning that advances neoantigen prediction in precision medicine. Karchin is a core member of the Johns Hopkins University Institute for Computational Medicine and has received numerous accolades, including being inducted as a Fellow of the American Institute for Medical and Biological Engineering in 2017 and the International Society for Computational Biology in 2025. With a strong academic background, she holds a joint appointment in Oncology at the School of Medicine and a secondary appointment in the Department of Computer Science at the Whiting School. Karchin co-led the Cancer Genome Atlas (TCGA) PanCan Atlas Essential Genes Drivers Analysis Working Group and currently leads the T Cell Repertoire Profiling Data Science team for Break Cancer. Her educational qualifications include a PhD in Computer Science from the University of California, Santa Cruz, awarded in 2003, and earlier degrees from the same institution in 2000 and 1998.
Department of Pathology - PhD in Pathobiology. GRE is not required.