Dan has led research in academic, corporate, NGO, and national laboratory settings, specializing in computational systems biology. His work focuses on the complex molecular interactions that drive phenotypes and disease, examining how these processes are influenced by environmental conditions. His team applies advanced mathematical, statistical, and computational techniques to biological data, addressing challenges in bioenergy, agriculture, ecosystems, zoonotic spillover, and human health—with a particular focus on their intersections in One Health.A leader in high-performance computing (HPC) for biological research, Dan’s team was the first to break the Exascale barrier, performing the fastest scientific calculation ever recorded at 9.4 Exaops. Their pioneering work in computational biology earned the 2018 Gordon Bell Prize, the first ever awarded for systems biology. His contributions to supercomputing and biology have also been recognized with the Secretary of Energy Honor Award, HPCWire Editor’s Choice for Top HPC-Enabled Scientific Achievement, and multiple honors from Oak Ridge National Laboratory (ORNL), where he has played a key role in pushing the boundaries of computational science.Dan’s research spans multiomics integration, leveraging network theory, topology discovery, wavelet theory, explainable-AI and Large Language Models alongside traditional and advanced supercomputing architectures. His team develops novel parametric, non-parametric, and Bayesian statistical methods, including tools for Genome-Wide Epistasis Studies (GWES), applying them to genomic, transcriptomic, proteomic, metabolomic, microbiomic, and chemiomic datasets. Their goal is to map functional relationships across biosynthetic, signaling, transcriptional, and kinetic regulatory networks in diverse biological systems, from viruses to microbes, plants, and humans.Recently, Dan’s lab has tackled urgent global challenges, including pandemic emergence, investigating viral evolution, pathogenesis, host interactions, environmental drivers of disease outcomes, and predictive models for future pandemics. Through interdisciplinary collaborations, his team continues to advance the intersection of biology and supercomputing, leveraging some of the world’s most powerful computational resources to drive discovery.