Assistant Professor of Research, Department of Urology, Keck School of Medicine of USC; Director of Data Science, CSI-Cancer, USC Michelson Center for Convergent Bioscience
Dr. Mason received his BS in 2009 in Mechanical Engineering from the Georgia Institute of Technology. Immediately after graduation, he began a doctoral program at USC’s Viterbi School of Engineering. His research work focused on utilizing patient data collected from a large autopsy study to develop stochastic Markov chain models of cancer metastasis for 12 primary cancer types, including breast, lung, prostate, and ovarian.
After receiving his PhD in 2013, Dr. Mason continued his research in mathematical oncology at The Scripps Research Institute before shortly moving back to USC as a postdoctoral scholar at the Convergent Science Institute in Cancer (CSI-Cancer) within USC’s Dornsife College of Letters, Arts and Sciences. Here he would focus his efforts on using clinical and demographic data to predict survival and cancer-related events. Additionally, Dr. Mason became a fellow at the United States Department of Veterans Affairs (VA) through the Big Data-Scientist Training Enhancement Program (BD-STEP) where he developed a prediction model utilizing veteran patient data.
In August 2018, Dr. Mason joined the Department of Urology at the Keck School of Medicine of USC as an Assistant Professor of Research. He intends to focus his efforts on developing the tools and techniques necessary to merge multimodal data for use in prediction models that can be utilized throughout the course of disease and treatment on an individual patient basis. Simultaneously, he also became a research fellow with the United States Food and Drug Administration (FDA), where he applied these techniques to phase 3 clinical trial data for approved cancer therapies. Dr. Mason has recently completed a second MS degree within Clinical, Biomedical, and Translational Investigations. He intends to use the knowledge he has gained to translate these predictive models into clinical use to ultimately improve patient care.