Noble Kennamer is a research scientist and soon-to-be Ph.D. in Computer Science from UC Irvine with 11 years of experience applying machine learning and Bayesian statistics to real-world scientific problems. He specializes in variational methods for optimal experimental design and has built active learning and uncertainty-aware deep learning systems that have been integrated into the data pipeline of the largest astronomical sky survey. His work spans graph neural nets, spatiotemporal architectures for confounding factors, and satellite-based rainfall prediction, reflecting a knack for translating domain expertise into production-ready models. Prior internships at Google and a current research role at Netflix underscore his ability to move models from research to operational settings. Based in Los Gatos, he combines rigorous physics and mathematics training with a pragmatic engineering mindset and is actively seeking full-time opportunities.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS, Physics, GPA: 3.99, Bachelor of Science - BS, Physics, GPA: 3.99 at University of California, Irvine
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