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Dr. Sylvia K. Plevritis is Professor of Biomedical Data Science, and of Radiology atStanford University, and Chair of Biomedical Data Science. She leads a systemsbiology cancer research program that bridges genomics, biocomputation, imaging andpopulation sciences to decipher properties of cancer progression to guide advances inearly detection and treatment response. Dr. Plevritis received her Ph.D. in ElectricalEngineering and M.S. in Health Services Research, both from Stanford University, witha focus on cancer imaging physics and modeling cancer outcomes, respectively. Shehas had a primary authorship role on over 100 scientific cancer-related articles. She is afellow of the American Institute for Medical and Biological Engineering (AIMBE) andDistinguished Investigator in the Academy of Radiology Research. She receivedthe 2016 Inaugural Award for Basic Scientist of the Year in Stanford Radiology. Dr.Plevritis has served on numerous NIH study sections, chaired scientific programs forthe several professional societies including the American Association for CancerResearch (AACR) and presented keynote lectures across multiple scales ofcomputational cancer biology. Sylvia Plevritis is the Program Director of the StanfordCenter in Cancer Systems Biology (CCSB), Program Director of the Stanford CancerSystems Biology Scholars Program (CSBS), and co-Division Chief of IntegrativeBiomedical Imaging Informatics at Stanford (IBIIS). In addition, she has been a PrincipalInvestigator with the NCI Cancer Intervention Surveillance Network (CISNET) for overfifteen years. She serves on NCI Board of Scientific Advisors, Leadership Council ofthe Stanford Cancer Institute and the Leadership Council of the Stanford Bio-XProgram.
My research program focuses on computational modeling of cancer biology and cancer outcomes. My laboratory develops stochastic models of the natural history of cancer based on clinical research data. We estimate population-level outcomes under differing screening and treatment interventions. We also analyze genomic and proteomic cancer data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.