Advances in high-throughput genomic technologies in the past two decades have given rise to large-scale biological data that is measured on a variety of scales. Genome-wide studies enable the simultaneous measurement of the expression profiles of tens of thousands of genomic features, from an ever increasing number of biological samples that may represent phenotypes, experimental conditions or time points. Examples include studies of various types of gene and protein expression, methylation and copy number variation, and high-throughput compound screening assays, among others. Similarly, studies in biomedical imaging and computational neuroscience generate tens of thousands of signals from brain or muscle activity under a variety of experimental conditions across the time-frequency domain. These massive data sets offer tremendous potential for growth in our understanding of the pathophysiology of many diseases. My research spans the two major areas of statistical learning - unsupervised and supervised, as well as survival analysis, with applications in the aforementioned domains. Its principal focus is in the development of statistical and computational approaches for high-dimensional data and includes methods for dimension reduction as well as methods for correlating a quantitative or qualitative outcome variable (such as patient survival time, presence of disease, patient response to treatment)  with a large number of covariates (genomic, clinical, laboratory and demographic variables). Our current research activities involve the development of methods for analyzing data from microbiome, radiomics and single-cell RNA-Seq studies.