Principal Investigators in the Biostatistics Branch (BB)

Dr. Albert's research focuses on complex modeling of correlated outcomes in biomedical sciences, including the analysis of longitudinal data, diagnostic testing, and data from biomarker studies. He also performs methodological research, including joint modeling of longitudinal and survival data, the analysis of high-dimensional chemical mixture models, and the analysis of circadian rhythms.

Dr. Li Cheung's research lies at the intersection of traditional quantitative epidemiology and novel methods to better understand the etiology and natural history of the multistate cancer process. He seeks to utilize epidemiologic findings to inform risk-based and benefit-based approaches to cancer screening and prevention.

Dr. Mitchell Gail’s research focuses on developing statistical methods for epidemiologic studies, including intervention trials, and genetic and other molecular epidemiologic studies, and on modeling absolute risk of disease, including breast cancer risk projection.

Dr. Hong focuses on the development of cutting-edge statistical methods for analyses of complex large-scale datasets (e.g., high-dimensional censored data and longitudinal data), and their application to the fields of public health, medicine, and health policy research. Her role in DCEG is to advance scientific knowledge in population-based cancer epidemiology and genetics studies through the development and use of novel statistical methodology.

Hormuzd Katki, Ph.D.'s research focuses on understanding how epidemiologic findings could be used for cancer screening and prevention and on developing individualized risk-based approaches to cancer screening.

Danping Liu, Ph.D. develops new statistical methods to evaluate and combine biomarkers for use in risk prediction.

Dr. Pfeiffer's research focuses on statistical methods for absolute risk prediction, problems arising in molecular and genetic epidemiologic studies, and the analysis of data from electronic medical records.

Dr. Philip Rosenberg’s research focuses on advancing medical science by testing clinical, epidemiological, and biological hypotheses using state-of-the-art statistical methods. He has a special interest in rare cancers arising in the inherited bone marrow failure syndromes. He is co-principal investigator on a series of descriptive studies of breast cancer and develops new methods for cancer surveillance research, including novel methods based on the age-period-cohort model, proportional hazards models for comparative age-period-cohort analysis, and methods to forecast the future burden of cancer.

Jianxin Shi develops statistical approaches to study the role of genetics in cancer etiology and survival, and analysis of the microbiome. His work has focused on methods to analyze genome-wide association data for the detection and association testing of copy number variations and for estimating genetic heritability of cancer survival.

Dr. Yu's research research addresses a wide variety of statistically and computationally challenging problems that have arisen in design and analysis of modern high-dimensional genetic and molecular epidemiologic studies, including including statistical methods for genetic and genomic studies, tree-based models and applications in molecular epidemiology studies, and integrative analysis using individual-level and summary-level data.

Dr. Haoyu Zhang, Earl Stadtman investigator in the Biostatistics Branch (BB), develops and applies scalable statistical methods for analyzing multi-ancestry genetic and biobank data, translating these findings to clinical settings to inform prevention and therapeutic strategies.

Dr. Tongwu Zhang works on characterizing tumor heterogeneity and evolution in relation to specific genetic backgrounds and etiological factors using the tools of computational biology, bioinformatics, biostatistics, and mathematics.

Dr. Zhu is an internationally recognized expert in the development of statistical methods to uncover and characterize cancer etiology. His multidisciplinary approach has resulted in major contributions to our understanding of germline and environmental exposures on molecular features of the cancer genome and the role of mutational signatures in prevention, diagnosis, and treatment, as well as implications for risk prediction and survival.