Guy Brock, Ph.D.
The areas of statistical genetics and the analysis of microarray data. Recent projects include developing methods for nonparametric linkage analysis in extended pedigrees, evaluating methods for handling selection bias in linkage studies, and missing value estimation in microarray data. Also interested in developing methods for handling proteomic data.
Somnath Datta, Ph.D.
Various topics in Statistics and Biostatistics such as Bioinformatics, Bootstrap Methods, Compound Decision Problems, Empirical Bayes Methods, Nonparametric Function Estimation, Statistical Genetics, Survival Data Analysis, Time Series Analysis, etc. Currently working on multistage data that are an important special case of multivariate survival or event time data. Also interested in nonparametric and semiparametric inference procedures for such multistage models. Interested in nonparametric inference procedures for marginal effects in clustered data, such as those that arise in longitudinal studies. In the area of Bioinformatics, working on developing novel statistical methods for gene expression and proteomic data.
Susmita Datta, Ph.D.
Bioinformatics, Biostatistics, Statistical Issues in Population Biology, Statistical Genetics, Infectious Disease Modeling and Survival Analysis. Also involved in developing statistical methods for analyzing microarray data. Working on the problems of modeling gene expression profiles through partial least squares regression, validation of clustering algorithms for grouping genes and developing various statistical tools for detection of differential gene expression. Also actively interested in proteomic data (MALDI-TOF, SELDI) analysis to understand disease etiology (colon, lung cancer etc.), as well as collaborative research with interdisciplinary scientists from Biochemistry, Biology, Public Health and Computer science.
L. Jane Goldsmith, Ph.D.
Primarily collaborative research in medicine and dentistry. Also interested in statistical methodology research relating to sample size, information theory, nonlinear models and inter-rater agreement.
Seong H Kim, Ph.D.
Bioinformatics, Proteomics, Bayesian statistics.
Maiying Kong, Ph.D.
Research interests: Parametric and semiparametric response surface modeling in drug interaction; Bioassay; Linear and nonlinear regression; High dimensional splines; Mixed effect models; Generalized linear models; Pre-clinical studies; Early phase studies; PD/PK modeling; Statistical computing.
Statistical methods in bioinformatics with applications to high-dimensional biology techniques, statistical computing, clinical trial design, group-sequential methods, linear and mixed-effects models, and modeling. Current activity is in evaluation of statistical methods for analysis of microarray data through simulation approaches.