Kyle Chickering is a research scientist with a PhD in Applied Mathematics and 11 years of experience translating deep mathematical insight into scalable generative-model training systems. Currently at Luma AI, he focuses on pre-training and scaling large generative models and previously advanced LLM pre-training dynamics during a research internship at the Institute of Foundation Models. His work blends theoretical rigor—publishing early-career pure-math research and leading a graduate student group on shock-formation—with pragmatic engineering wins such as 100x speedups in prototype algorithms, multi-fold reductions in training time, and production video foundation models. He has a track record of building infrastructure from scratch at startups, optimizing distributed GPU utilization, and improving model conditioning to unlock orders-of-magnitude performance gains. Based in Davis, CA, he also founded a directed-reading program to mentor undergraduates, reflecting a commitment to community and reproducible research.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD Applied Mathematics, Doctor of Philosophy - PhD Applied Mathematics at University of California, Davis
Contributions:2 pushes, 1 branch in 5 years 11 months
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