Creating high-impact statistical methods for genomic data
Xihong is a leader in the development and application of widely used statistical methods for the analysis of massive genomic and health data. Her analytic methods are used for studies of whole-genome sequencing data, gene-environment interactions, biobanks and complex epidemiological and observational study data. Xihong’s research has advanced methods for testing a large number of complex hypotheses, causal inference, cloud-based statistical computing and epidemiological studies. She leads studies to identify genes underlying inherited diseases as a recipient of the Outstanding Investigator Award from the National Cancer Institute and principal investigator for an analysis center of the Genome Sequencing Program at the National Human Genome Research Institute.
Xihong also directs Harvard’s Program in Quantitative Genomics, which works to improve health through interdisciplinary training and study of genetics, behavior, environment and medicine. Recently, she has made influential contributions to research in COVID-19 public health interventions. A native of Beijing, China, Xihong was recognized early in her career by the Committee of Presidents of Statistical Societies with the Presidents’ Award, the statistical analog of the Fields Medal for mathematics, and the American Public Health Association’s Spiegelman Award. She has also received high honors from the American Statistical Association, and in 2018 she was elected a member of the National Academy of Medicine.
Affiliations: Professor and Former Chair, Department of Biostatistics, Harvard T. H. Chan School of Public Health; Professor, Department of Statistics, Harvard University Faculty of Arts and Sciences; Coordinating Editor, Biometrics; Founding Co-Editor, Statistics in Biosciences journal