Biosciences Division, ORNL
My lab focuses on the development and subsequent application of mathematical, statistical and computational methods to biological datasets in order to yield new insights into complex biological systems. Our approaches include the use of Network Theory and Topology Discovery/Clustering, Wavelet Theory, Machine Learning (e.g. Random Forests, Support Vector Machines, etc.) and Linear Algebra (primarily as applied to large-scale multivariate modeling). Areas of Statistics of particular interest to my lab include the use of both frequentist (parametric and non-parametric) and Bayesian methods as well as the development of new methods in Multivariate Statistics and Genome-Wide Association Studies (GWAS). These mathematical and statistical methods are applied to various (meta)Omics data sets (Genomics, Phylogenomics, Transcriptomics, Proteomics, Metabolomics, Microbiomics and Chemiomics) individually as well as in combination in an attempt to better understand the signaling, transcriptional, translational, degradation and kinetic regulatory networks at play in biological organisms and communities.
Many of our projects center around studying stems in involved the Bioenergy Science Center (BESC) and/or Plant-Microbial Interfaces (PMI) programs at ORNL. However, we have a broad view of biological complexity and evolution that stretches from microbes to plants to humans.
ORNL is home to one of the world’s largest supercomputers. Our group uses petascale computing to analyze and model complex biological systems and will be actively involved in the development of exascale applications for biology. Thus, there are excellent opportunities to be involved in the cutting edge of computational biology and supercomputing.