Hyokyoung (Grace) Hong, Ph.D.

Principal Investigators in BB

A listing of principal investigators in BB. Learn about their research interests.

Biography

Dr. Hong earned her Ph.D. in statistics from the University of Illinois at Urbana-Champaign. Specializing in the development of advanced statistical methods for analyzing complex, large-scale datasets—such as high-dimensional censored and longitudinal data—she applies her expertise to the realms of public health, medicine, and health policy research. Before her tenure at DCEG, she was an Associate Professor in the Department of Probability and Statistics at Michigan State University in East Lansing. Awarded scientific tenure by the NIH, she transitioned to the role of senior investigator in the Biostatistics Branch in August 2021. Alongside her research, Dr. Hong has held editorial roles for prestigious journals, serving as Associate Editor for JASA, Biostatistics, and the New England Journal of Statistics in Data Science, while also contributing as an Academic Editor for PLOS ONE.

Research Interests

In her role in DCEG, Dr. Hong works to advance scientific knowledge in population-based cancer epidemiology and genetics studies through the development and use of novel statistical methodology. She has led the development of methods in high-dimensional time-to-event analysis by proposing a series of novel and innovative ideas. She has made important advances in statistical theory and methodological development in the areas of quantile regression analysis, classification with high-dimensional features, and high-dimensional time-to-event analysis. Quantile regression has emerged as both an efficient way of linking the whole distribution of an outcome to the covariates of interest and an important alternative to commonly used regression models. Dr. Hong has led pioneering work to address the many challenges inherent to this approach, in terms of theory and computation when the covariates of interest are high dimensional. 

Dr. Hong has also contributed to statistical methodology for classification problems with high-dimensional covariates. For example, many classification problems emerge from analyses of gene expression and imaging data to identify individuals with disease or at high risk of developing disease. To circumvent these issues, she proposed a novel high-dimensional classification method that could predict clinical diagnosis of autism spectrum disorder using imaging predictors, integrating priorly known different sources of anatomical information, correlation among imaging predictors, and spatial information.

In addition, some of Dr. Hong’s more recent research includes:

• Health disparities (https://github.com/younghhk/software)
• Sleep and mortality
• The etiology of Burkitt lymphoma (BL)
• Nutritional, molecular, and other biomarkers (such as metabolomics) for cancer
• Telomere biology and its relation to cancer risk
• Statistical feature learning methods and software for lung cancer (https://github.com/younghhk/BLCSC)
• COVID-19 infectious disease modeling (https://github.com/younghhk/software)
• Survival data screening in high-dimensional data (https://github.com/younghhk/software)