Educational Background PhD, Northern Illinois University, 2000 MSc, Tech, Birla Institute of Technology & Science, India, 1992 Industry Experience Statistical Scientist, Cancer Bioinformatics, AstraZeneca R&D Boston, Waltham, MA, 2002-2005 Biostatistician, Bristol-Myers Squibb Pharmaceutical Research Institute, Bristol-Myers Squibb, Princeton, NJ, 1999-2002 Lab Overview Advances in high-throughput technologies in the past decade have given rise to large-scale biological data that is measured in a variety of scales. Gene expression studies enable the simultaneous measurement of the expression profiles of tens of thousands of genes and proteins, often from only a handful of biological samples. Data is typically presented as a two-way numeric table in which the rows represent the genes, columns represent the samples and each entry consists of the expression level of a given gene in a given sample. The samples may represent a phenotype such as tissue type, experimental condition or time points. Traditionally these studies have involved the use of microarray technology to measure mRNA expression, and more recently, the use of SNP arrays to measure allele-specific expression and DNA copy number variation, methylation arrays to quantify DNA methylation and next-generation sequencing technologies, such as RNA-Seq and ChIP-Seq, for the measurement of digital gene expression. In addition, high-throughput compound and siRNA screening assays are specifically designed to detect interactions with compounds by directly measuring inhibition of siRNA or kinase activity. These studies have resulted in massive amounts of data requiring analysis and interpretation while offering tremendous potential for growth in our understanding of the pathophysiology of many diseases. The focus of my research is in the development of novel statistical methodology for the analysis of data stemming from such high-throughput studies. It includes methods for dimension reduction and molecular pattern discovery as well as for correlating a qualitative or quantitative outcome variable (including tissue type, presence of disease, patient response to treatment, survival time) with large numbers of covariates (genes, SNPs or sequence tags) based on supervised and unsupervised learning. The primary focus of my research activities consist of the following two problems from statistical learning theory: nonnegative matrix factorization and continuum regression.