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Principal Investigators

Biostatistics Branch (BB)

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  • Albert, Paul S., Ph.D.
    Complex modeling of correlated outcomes in biomedical sciences, including the analysis of longitudinal data, diagnostic testing, and data from biomarker studies


  • Cheung, Li C., Ph.D.
    Dr. Cheung seeks to utilize epidemiologic findings to inform risk-based and benefit-based approaches to cancer screening and prevention.


  • Gail, Mitchell H., M.D., Ph.D.
    Studies of breast and gastric cancer etiology and risk assessment. Statistical methods for observational and experimental studies, including design and analysis of genetic epidemiologic studies.
  • Graubard, Barry I., Ph.D.
    Use of national surveys and other types of population based-studies for conducting epidemiologic and risk factor surveillance studies related to the incidence of and mortality from cancer. Statistical methods for design and analysis of observational and randomized studies with complex sample designs.


  • Hong, Grace, Ph.D., M.S.
    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.


  • Katki, Hormuzd A., Ph.D.
    Predicting cervical cancer risk. Estimating absolute risks from studies nested within cohorts. Efficient study designs for comparing diagnostic tests. Methodology for clinical genetics.


  • Liu, Danping, Ph.D.
    Developing new statistical methodologies for combining cross-sectional and longitudinal biomarker data for disease prediction, environmental health


  • Pfeiffer, Ruth M., Ph.D.
    Statistical methods for: genetic epidemiology, including power assessment of association studies, family data accounting for ascertainment , DNA pooling for joint genotype estimation, and lab data methods.


  • Rosenberg, Philip, Ph.D.
    HIV/AIDS natural history. Viruses and cancer, including HCV, SV40, and KSHV. Meta-analysis in genetic epidemiology. Smoothing and nonparametric regression. Cancer susceptibility.


  • Shi, Jianxin, Ph.D.
    Developing quantitative methods and leading substantive projects to advance the knowledge about cancer genomics and potential translational applications. Focus on cancer genetic epidemiology, cancer genomics, and microbiome epidemiology.


  • Yu, Kai, Ph.D.
    Statistical methods for genetic and genomic studies. Tree-based models and applications in molecular epidemiology studies. Integrative analysis using individual-level and summary-level data.


  • Zhu, Bin, Ph.D.
    Developing and applying novel statistical methods to increase understanding of biological mechanisms underlying complex diseases, and studying the pattern and determinants of disease in human populations.