I am a Metropolis Postdoctoral Fellow at Los Alamos National Laboratory studying astrophysical plasmas using exascale GPU-based resources. My work at Los Alamos is currently focused on developing AthenaPK, an exascale magnetohydrodynamics code, and Parthenon, the adaptive mesh refinement framework that powers AthenaPK. With AthenaPK, I run simulations of astrophysical jets in various scenarios on Frontier, the world’s first exascale supercomputer.
My computational intrests focus on enabling scientific computing on GPUs via the development of performance-portable codes: codes that are capable of high performance on various GPU and CPU architectures.
Feel free to contact me for collaboration via email.
Download my resume.
PhD in Astrophysics and Computational Mathematics, Science and Engineering, 2022
Michigan State University
BSc in Physics and Mathematics with Emphasis in Applied and Computational Mathematics, 2016
Brigham Young University
Magnetohydrodynamic (MHD) turbulence affects both terrestrial and astrophysical plasmas. The properties of magnetized turbulence must be better understood to more accurately characterize these systems. This work presents ideal MHD simulations of the compressible Taylor-Green vortex under a range of initial subsonic Mach numbers and magnetic field strengths.
In the universe’s most massive galaxies, active galactic nucleus (AGN) feedback appears to limit star formation. The accumulation of cold gas near the central black hole fuels powerful AGN outbursts, keeping the ambient medium in a state marginally unstable to condensation and formation of cold gas clouds.
In cool-core galaxy clusters with central cooling times much shorter than a Hubble time, condensation of the ambient central gas is regulated by a heating mechanism, probably an active galactic nucleus.
Large scale simulations are a key pillar of modern research and require ever-increasing computational resources. Different novel manycore architectures have emerged in recent years on the way towards the exascale era.
A shift is underway in high performance computing (HPC) towards heterogeneous parallel architectures that emphasize medium and fine grain thread parallelism. Many scientific computing algorithms, including simple finite-differencing methods, have already been mapped to heterogeneous architectures with order-of-magnitude gains in performance as a result.