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 & Deep Learning (amongst others: iterative Random Forests, Deep Neural Networks, etc.). We are focusing on the development of explainable-AI methods for the discovery of complex, high-order interactions in biological systems. These methods are applied to various population and (meta)multiomics data sets (Genomics, Phylogenomics, Transcriptomics, Proteomics, Metabolomics, Microbiomics, Viriomics, Phytobiomics, Chemiomics, etc.) individually as well as in combination in an attempt to better understand the functional relationships as well as biosynthesis, signaling, transcriptional, translational, degradation and kinetic regulatory networks at play in biological organisms and communities.Many of our projects center around studying systems in involved the Center for Bioenergy Innovation (CBI), Plant-Microbial Interfaces (PMI) and Crassulacean Acid Metabolism (CAM) Biodesign programs at ORNL. However, we have a broad view of biological complexity and evolution that stretches from viruses, microbes and plants to Drosophila, mice and humans (including cancer and neuroscience) including an active role in the DOE-VA collaboration on clinical genomics and human systems biology.ORNL is home to some of the world’s largest supercomputers. My lab uses petascale computing to analyze and model complex biological systems and are actively involved in the development of exascale applications for biology with a particular emphasis on explainable-AI. Thus, there are excellent opportunities to be involved in the cutting edge of computational biology and supercomputing.