is an Assistant Professor at the University of New Mexico, Department of Mathematics and Statistics. The core direction of his research is numerical analysis and scientific computing. His specific focus is on high-performance computing, iterative solvers for large sparse (non)linear systems, their associated preconditioning, and numerical PDEs. He approaches his research both from a software perspective centered on providing these methods to the broader community and also from a theoretical perspective centered on the development of new methods. His chief software projects are XBraid
(Parallel Multigrid Solvers in Time), PyAMG
(Algebraic Multigrid Solvers in Python), and hypre
(high performance preconditioners).
Jacob earned his Ph.D. in computer science from the University of Illinois at Urbana-Champaign under the direction of Prof. Luke Olson. His dissertation resulted in new methods for smoothed aggregation-based algebraic multigrid (AMG), which proved effective for a variety of problems, e.g., anisotropic diffusion, Helmholtz, elasticity and Euler flow. Next, he joined University of Colorado at Boulder for one year as a postdoc under Profs. Thomas Manteuffel and Stephen McCormick. Jacob joined Lawrence Livermore National Laboratory in September 2011, followed by his move to UNM in 2018.
Areas: Numerical analysis, computational science, high performance computing
Keywords: Iterative methods, preconditioning, multigrid, neural networks, numerical PDEs, parallel-in-time