Kjiersten Fagnan

Kjiersten Fagnan began working with JGI in 2012 as a NERSC bioinformatics computing consultant, after completing a petascale postdoctoral fellowship at NERSC and LBL’s Computational Research Division.. As a postdoc her research focused on stable and accurate computational methods for reacting subsurface flows, and evolved into scalable methods for scientific data analysis. In 2014 Dr. Fagnan became the JGI-NERSC Engagement Lead with a focus on adapting JGI workloads to run on supercomputing hardware and worked closely with staff to understand the data-intensive nature of JGI workloads. Dr. Fagnan was appointed CIO of JGI in 2016 and in 2018 was hired to be the JGI’s Data Science and Informatics department head. In 2018, Dr. Fagnan was part of the Gordon Bell prize winning team at the Supercomputing conference, SC’18, led by researchers at Oak Ridge National Laboratory. Dr. Fagnan has been the Distinguished Speaker at IBM Research in Almaden where she presented work related to distributed data and workflow management. Dr. Fagnan is also a co-PI for the National Microbiome Data Collaborative where she leads infrastructure and user-centered design efforts.

Education

• PhD Applied Mathematics University of Washington, Seattle
• BA Applied Mathematics University of California, Berkeley
• MBA Haas School of Business, University of California, Berkeley

About the JGI Chief Informatics Officer

The Chief Informatics Officer provides advice and strategic direction for the overall design and implementation of computational biology and bioinformatics approaches in support of meeting DOE mission critical programmatic goals.

Awards and Service

• UC CORO participant, 2021
• Member of the Networking and Information Technology Research and Development (NITRD) Program
• Chair of the Bike East Bay Board of Directors
• Coach – Girls who code, Technovation
• Gordon Bell Prize, 2018
• NERSC, CRD Petascale Postdoctoral Fellow

Publications

• Joubert et al., “Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction,” SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, 2018, pp. 717-730, doi: 10.1109/SC.2018.00060.
• Wood-Charlson, E.M., Anubhav, Auberry, D. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat Rev Microbiol 18, 313–314 (2020). https://doi.org/10.1038/s41579-020-0377-0
• S. Pau and J.B. Bell and A.S. Almgren and K.M. Fagnan and M.J. Lijewski. 2012. An Adaptive Mesh Refinement Algorithm for Compressible Two-Phase Flow In Porous Media. Computational Geophysics 16(3).
• Dosanjh and S. Canon and J. DeSlippe and K. Fagnan and R. Gerber and L. Gerhardt and J. Hick and D. Jacobsen and D. Skinner and N.J. 2014. Wright Extreme Data Science. Big Data and Extreme-Scale Computing Conference.