I am a computational scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. I build numerical algorithms and the parallel software that runs them on leadership-class supercomputers, for plasma physics, atmospheric modeling, and epidemiology.
My work spans three connected areas. The first is particle-based and agent-based methods: cloud microphysics, implicit particle-in-cell algorithms for plasmas, and agent-based epidemiology. The second is high-order discretization and implicit time integration for multiscale hyperbolic-parabolic systems. The third is the implementation of these methods at scale, in production codes that other scientists depend on. Several of these solvers also generate the training data for machine-learning and reduced-order-modeling studies that I co-author.
Current work
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Particle-based cloud microphysics
Lead developer of the super-droplet microphysics in ERF, including cold-cloud (ice) processes, adaptive mesh refinement, and GPU portability.
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Implicit particle-in-cell solvers
Preconditioners and nonlinear solvers for implicit electromagnetic particle-in-cell simulation in WarpX, and high-order continuum kinetic methods for magnetically confined plasmas.
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Agent-based epidemiology
Higher-fidelity agent interaction, disease spread, and capacity-limited hospital care in ExaEpi, an exascale-capable agent-based model.
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Simulation data for machine learning
My solvers supply the training data for reduced-order-modeling and machine-learning studies in plasma physics and cloud microphysics. Early-stage work on learned denoisers for particle-in-cell data.
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Gas optics
Co-developer of PIAFS, a high-order nonlinear hydrodynamics solver with photochemistry for simulating plasma-based and gaseous optical elements.