Dan Jacobson
About Dan Jacobson
Dan Jacobson serves as the Chief Scientist for Computational Systems Biology at Oak Ridge National Laboratory, where he has worked since 2014. He specializes in utilizing petascale computing to analyze complex biological systems and is engaged in various research programs and collaborations in the field.
Work at Oak Ridge National Laboratory
Dan Jacobson has been a Computational Biologist at Oak Ridge National Laboratory (ORNL) since 2014. He has held the position of Chief Scientist for Computational Systems Biology since 2018. In his role, he utilizes petascale computing to analyze and model complex biological systems. Jacobson is actively involved in various research programs at ORNL, including the Center for Bioenergy Innovation, Plant-Microbial Interfaces, and Crassulacean Acid Metabolism Biodesign. His work contributes to advancements in computational biology and systems biology.
Previous Experience in Bioinformatics
Before joining ORNL, Dan Jacobson served as the CEO of the National Bioinformatics Network from 2003 to 2008. He also worked as a Computational Biologist at the University of Stellenbosch from 2009 to 2014. His experience in these roles contributed to his expertise in bioinformatics and computational biology, laying the foundation for his current research endeavors.
Education and Expertise
Dan Jacobson earned a Bachelor of Science (BS) in Biochemistry from Florida State University, where he studied from 1983 to 1988. He furthered his education with a Master of Science (MS) in Biochemistry from The Johns Hopkins University, completing his studies from 1989 to 1991. Jacobson also holds a Doctor of Philosophy (PhD) in Computational Biology from Stellenbosch University, focusing on Wine Biotechnology. His educational background supports his extensive knowledge in computational systems biology.
Research Contributions and Collaborations
Dan Jacobson is engaged in the Department of Energy-Veterans Affairs (DOE-VA) collaboration on clinical genomics and human systems biology. He leads projects that focus on developing explainable-AI methods for discovering complex, high-order interactions in biological systems. His research includes the development of exascale applications for biology, emphasizing the integration of advanced computational techniques in biological research.