Dr. Jiang is an assistant professor in the Division of Public Health Sciences at Washington University School of Medicine in St. Louis. Her research focuses on developing statistical methods for life history data under various complex settings, typically with the goal to accurately reflect dynamic features of the disease history, identify risk factors for disease progression via screening of high-dimensional covariates, and predict future outcomes, with a particular focus on breast cancer. She is passionate about providing efficient and powerful computational algorithms to tackle challenging problems in cancer research.

In this funded study (R37 CA256810), Dr. Jiang proposes several novel statistical models to incorporate patient heterogeneity in a personalized, dynamic manner, leading to a more accurate risk prediction scheme. The accumulation of cancer risk over life, well documented for breast cancer, is ideally suited to methods that incorporate time-varying covariates. However, advanced statistical techniques are needed to comprehensively characterize the changing pattern of the longitudinal trajectories that are associated with the breast cancer risk. This study may leverage the current prediction framework, which will be applicable to a broad range of applications, from prevention to treatment and survivorship.