Research in BSCB falls roughly into two categories: Biological Statistics and Computational Biology. However, individual faculty collaborate across both broad disciplines.
Areas of research strength in Biological Statistics Bayesian statistics, computational statistics, experimental design, functional data analysis and machine learning, generalized linear models and mixed models and survival analysis. The type of research covers a wide range from theoretical work on fundamental issues in statistical inference, to applied work motivated by specific applications. Examples of biological applications include clustering and significance of gene expression data from microarrays, mapping of quantitative trait loci (QTL), clinical trials, health care expenditures, and design of agricultural field trials. Since statistics as a discipline is relevant to almost every research field, faculty collaborate broadly across Cornell.
Our major research strengths in Computational Biology are in comparative, evolutionary, and population genomics. Specific problems of interest include the detection of genomic regions underlying complex traits, the detection of positive selection, the evolutionary genomics of plant and animal domestication, the discovery of new human genes, and the identification and characterization of functional noncoding elements in mammals. Our faculty are key members of a strong, active community at Cornell in comparative, evolutionary, and population genomics that spans departments and colleges.