Afton Geil is a postdoctoral scholar and GPU computing specialist with 14 years of experience bridging academic research and applied engineering. She earned a PhD in Electrical and Computer Engineering from UC Davis, where her work with Professor John Owens focused on parallel algorithms and adapting data structures to GPU architectures to extract significant performance gains. Currently at Berkeley Lab, she brings deep expertise in heterogeneous programming models gained from internships at Intel and sustained research as a graduate student. Her background includes hands-on systems and data-analysis roles outside academia, reflecting practical problem-solving and leadership in nontechnical settings. Collected experience across industry and research gives her a rare combination of production-minded implementation skills and rigorous experimental methodology. Based in Davis, California, she is driven to translate GPU research into real-world performance improvements and tooling.
14 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at University of California, Davis
BS, BA, Engineering Science, Physics, BS, BA, Engineering Science, Physics at Trinity University
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