Project 3 Faculty
Xihong Lin (Project Leader) firstname.lastname@example.org Xihong Lin is Professor of Biostatistics and Co-ordinating Director of Program in Quantitative Genomics of the Harvard School of Public Health. My group's major research interests lie in development and application of statistical and computational methods for anlaysis of high-dimensional genomic and 'omics data in population and clinical sciences, and for analysis of correlated data, such as longitudinal, clustered and spatial data. We are interested in statistical genetics and genomics, genetic and epigenetic epidemiology, genes and environment and medical genomics. Current research projects include genome-wide association studies, next generation sequencing studies, gene-environment interactions, genome-wide DNA methylation studies, pathway analysis and network analysis, and proteomics. Articles: PubMed, Google Scholar.
Armin Schwartzman email@example.com Research interests: signal and image analysis, large-scale multiple testing. Armin is involved in Project 3, developing multiple testing approaches to the discovery of biomarkers in high-throughput data. Articles: PubMed, Google Scholar.
Tyler VanderWeele firstname.lastname@example.org My methodologic research concerns how we distinguish between association and causation in the biomedical and social sciences and the study of the mechanisms by which causal effects arise. The focus of my current work includes the analysis of pathways, assessments of interaction, and the evaluation of spillover effects in which one person's exposure will affect the outcomes of another. My research employs counterfactual theory and ideas from causal inference to clarify and formalize concepts used by epidemiologists. My empirical work has been in the areas of perinatal, psychiatric and genetic epidemiology; various fields within the social sciences; and the study of religion and health. Articles: PubMed, Google Scholar.
Tianxi Cai email@example.com Dr. Cai's current research interests are mainly in the area of biomarker evaluation; model selection and validation; prediction methods; personalized medicine in disease diagnosis, prognosis and treatment; statistical inference with high dimensional data; and survival analysis. In addition to her methdological research, Dr. Cai also collaborates with the I2B2 (Informatics for Integrating Biology and the Bedside) center on developing a scalable informatics framework that will bridge clinical research data and the vast data banks arising from basic science research in order to better understand the genetic bases of complex diseases. Articles: PubMed, Google Scholar.
E. Andres Housman - Brown University firstname.lastname@example.org Dr. Andres Houseman is a biostatistician with diverse interests, including molecular epidemiology, biomarker discovery, and environmental exposure assessment. Much of his statistical research involves latent variable methods, model-based clustering, and high-dimensional data analysis. Recent work has focused on computationally efficient methods for epigenomics research. Articles: Google Scholar.