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Daniel Jacobson



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.) and Linear Algebra (primarily as applied to large-scale multivariate modeling), together with traditional and more advanced computing architectures, such MPI parallelization and Apache Spark. We make use of various programming languages including C, Python, Perl, Scala and R. 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 for Genome-Wide Association Studies (GWAS) and Phenome-Wide Associations Studies (PheWAS). These mathematical and statistical 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 to microbes to plants to humans (including cancer and neuroscience).


Ph.D. Stellenbosch University: Stellenbosch, Western Cape, South Africa

Wine Biotechnology (Computational Biology) (Institute for Wine Biotechnology)


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