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Phyllis Gimotty, Ph.D.  Dr. Gimotty is a Professor of Biostatistics at the University of Pennsylvania whose expertise is integral to our population-based studies and analysis of large datasets. Her research focuses on application of statistical methods in cancer translational research and risk assessment. She is currently the biostatistics core director for multiple SPORE projects. Her research interests include survival analysis, classification trees, longitudinal data, and evaluating diagnostic tests and methods for incomplete data.

Elizabeth White, Ph.D.  Dr. White is an Assistant Professor of Otolaryngology-Head and Neck Surgery at the University of Pennsylvania and an accomplished virologist whose focus is on high risk HPVs.  Her lab studies HPV interactions with the host cellular environment with the goal of understanding how viruses cause cancer and modulate immune responses. Her expertise is pivotal in our present efforts to understand the interplay among viral oncogene function, HPV+ tumor metabolic plasticity, and therapy response.

Alexander Pearson, M.D., Ph.D.  Dr. Pearson in an Associate Professor of Hematology-Oncology at the University of Chicago.  His independent research on head and neck cancers integrates mathematical modeling and statistical genomics with laboratory data in order to pursue more effective treatments.  He is presently developing and applying machine learning algorithms to help discern histologic correlates of high risk metabolic profiles in HPV-related cancers.

Robert Brody, M.D.  Dr. Brody is an Assistant Professor of Otolaryngology-Head and Neck Surgery at the University of Pennsylvania and is pursuing  a Masters of Science in Clinical Epidemiology. He has designed and created a flexible database to serve population-based studies in head and neck cancer patients.  He has populated this database with the world's largest surgically treated cohort of HPV+ cases.  He is collaborating in using this cohort to identify cases with adverse oncologic outcomes and a pursue a molecular basis for their treatment resistance along with predictive biomarkers.

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