About Me

As a computational systems biologist and distinguished research scientist at Oak Ridge National Laboratory (ORNL), Daniel Jacobson and his team focus 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. Their approaches include the use of network theory and topology discovery / clustering, wavelet theory, machine & deep learning (among others: iterative random forests, deep neural networks, etc.). 

Daniel’s team is 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, and more) – 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. 

Daniel’s group is happy to be the first to break the exascale barrier and to have done it for biology. This project led to them winning the 2018 Gordon Bell Prize (the first-ever for systems biology). At present, their latest 9.37 Exaflops mixed precision calculation is the fastest scientific calculation ever done anywhere in the world. 

Many of the team’s projects center around studying systems involved in the Center for Bioenergy Innovation (CBI), plant-microbial Interfaces (PMI), and Crassulacean acid metabolism (CAM) biodesign programs at ORNL. However, it has a broad view of biological complexity and evolution that stretches from viruses, microbes, and plants to drosophila, mice, and humans (including cancer and neuroscience), as well as 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. Daniel’s lab uses petascale computing to analyze and model complex biological systems and is 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.