Matthew Emmett is a Senior Member of Technical Staff and applied mathematician with 16 years of experience building high-performance scientific software, currently implementing math algorithms for AMD's HPC GPUs. His background spans academia and industry—PhD-trained in applied mathematics and multiple postdocs—where he developed novel time-integration and multiscale PDE solvers for large-scale simulation. At CMG he accelerated nonlinear and linear solvers for reservoir simulation on parallel clusters, and he contributes to open-source HPC tooling such as the AMReX project, adding Fortran-to-Python bindings and multifab I/O enhancements. Known for bridging deep numerical analysis with pragmatic engineering, he focuses on turning sophisticated algorithms into production-quality, GPU-ready libraries for exascale computing.
16 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (PhD), Applied Mathematics, Doctor of Philosophy (PhD), Applied Mathematics at University of Alberta
Master of Science (MS), Applied Mathematics, Master of Science (MS), Applied Mathematics at The University of Calgary
BS, Theoretical and Mathematical Physics, BS, Theoretical and Mathematical Physics at Simon Fraser University
AMReX: Software Framework for Block Structured AMR
Role in this project:
Back-end Developer
Contributions:43 commits in 2 years 7 months
Contributions summary:Matthew contributed to the PyBoxLib library, modifying Fortran code to add and enhance Python bindings. Their work involved integrating Fortran subroutines with Python modules, including adding read/write functionality for multifabs and creating new layout functionality. The commits also include updates to Makefiles and improvements to indexing for multifabs with multiple components, and other small updates.
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