Biography

Yu-Ru Su, PhD, specializes in statistical genetics, survival analysis, and functional/longitudinal data analysis. Her research interests cover a wide spectrum of statistical methods for modern biomedical studies, especially in cancer prevention and precision medicine. Her current research focuses on integrating information in genetics, environmental, and clinical data to develop precise risk models of cancers with a goal of promoting personalized prevention/surveillance strategies. 

Before joining Kaiser Permanente Washington Health Research Institute, Dr. Su received her postdoctoral research training at Fred Hutchinson Cancer Research Center, where she was promoted to a staff scientist position. During her time at Fred Hutch, she was part of the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), one of the world’s leading collaborations in colorectal cancer research. At GECCO, she conducted complex analyses aiming to discover genetic risk factors and interactions between genetics and environmental factors for colorectal cancer. These findings are essential for developing risk prediction models. She also developed novel and computationally feasible statistical methods via the kernel machine framework for detecting novel genetic associations with complex diseases by bringing in functional information from multi-omics data. Another field of her methods research focuses on statistical approaches for functional association between functional exposures and a scalar outcome. Dr. Su developed a new dimension reduction technique and a testing approach for inferences on the infinite-dimensional association. The application of these methods in modern genetic and aging studies is leading to a better understanding of underlying mechanism of complex diseases, including cancer and dementia.

Dr. Su received her PhD in biostatistics from the University of California, Davis. Her dissertation focused on statistical estimating procedures used to infer associations of survival outcomes and complex exposures. An example is time-varying covariates, based on incomplete data such as intermittently measured longitudinal covariates and left-truncation or doubly-censored survival outcomes. She investigated asymptotic properties of the proposed methods via modern semiparametric theory and proposed complex algorithms for handling incompleteness in data. 

At Kaiser Permanente Washington Health Research Institute, Dr. Su collaborates with scientists from multiple disciplines to pursue answers and solutions to scientific questions related to breast cancer, Alzheimer’s disease and dementia, and opioid use disorders. She actively collaborates with the Breast Cancer Surveillance Consortium to investigate the screening performance of multiple screening modalitiesin women with and without breast cancer history, to build reliable risk prediction models and personalized strategies for screening and surveillance strategies. She also closely works with the Adult Change in Thought (ACT) study to understand the connection between dementia and other clinical and health conditions.