Dr. Veerabhadran Baladandayuthapani
Regular Member
The University of Texas MD Anderson Cancer Center
Department of Biostatistics
Research Interests:
- Statistical theory and methods: hierarchical functional data analysis; multivariate nonlinear statistical methods for classification and prediction problems; spatial data analysis; Bayesian graphical models; hierarchical Bayesian modeling and computation; Markov Chain Monte Carlo algorithms; semiparametric/nonparametric methods and mixed models
- Applications: high-throughput functional genomics experiments in nutrition and cancer; complex multivariate datasets in biostatistical and bioinformatics applications; dose response modeling and synergy assessment; environmental applications; risk modeling
The major thrust of my research program is to develop adaptive and flexible statistical models for analyses of multivariate, functional and spatial data arising from high-throughput biomedical studies. These studies yield data-structures that are very rich, containing an abundance of information about various aspects of the processes generating the data, and their high dimensionality and complexity present numerous computational, modeling, and inferential challenges. Such studies include high-throughput genomics data such as gene expression arrays, array CGH, SNP arrays, protein arrays as well as spatial and imaging data. The class of models for analyzing and modeling such data include functional data analysis models, spatial and graphical models. The methodological developments are motivated by important scientific questions in the subject area.
A tutorial in our laboratory would provide advanced training in state-of-the art statistical methods, advanced mathematical modeling and computer programming and software development.
Selected Publications:
Mallick, B. K., Gold, D. L. and Baladandayuthapani, V. (2009). Bayesian Methods for Gene Expression Data. Wiley, U.K.
Baladandayuthapani, V., Mallick, B. K., and Carroll, R.J. (2005). Spatially Adaptive Bayesian Penalized Regression Splines (P-splines). Journal of Computational and Graphical Statistics, 14, 378-394.
Baladandayuthapani, V., Mallick, B. K., Hong, M. Y., Lupton, J. R., Turner, N. D. and Carroll, R. J. (2008). Bayesian Hierarchical Spatially Correlated Functional Data Analysis with Application to Colon Carcinogenesis. Biometrics. 64, 64-73 4.
Baladandayuthapani, V., Ji. Y., Talluri, R., Nieto-Barajas, L. E. and Morris J. S. (2010) Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data. Journal of American Statistical Association 105(492): 1358-1375.
Bonato V., Baladandayuthapani, V.*, Broom, B. M., Sulman E. P., Aldape K. D and Do, K-A. Bayesian ensemble methods for survival prediction in gene expression data. Bioinformatics (in press)
Program Affiliation:
Program in Biostatistics, Bioinformatics and Systems Biology
Contact Information
Phone: 713.563.4268
Email: veera@mdanderson.org
Office: MDA FCT 4.6032 (Unit 1409)
Title: Assistant Professor
Education:
Ph.D. - Texas A&M University - 2005
