Koby Hayashi
Machine Learning Engineer at Pacific Northwest National Laboratory
Atlanta, Georgia, United States
Summary
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Top SchoolKoby Hayashi is a Machine Learning Engineer at Pacific Northwest National Laboratory with 11 years of experience bridging high-performance computing, randomized numerical linear algebra, and graph-based ML. He completed a DOE-backed PhD at Georgia Tech focused on parallel and randomized methods for nonnegative low-rank approximations and scalable hypergraph clustering, producing publications across applied math, HPC, ML, and computational neuroscience. His work includes scalable algorithms for Hyper-Graph Neural Networks and efficient clique sampling techniques developed during internships at Lawrence Berkeley Lab and Berkeley Lab. Comfortable working at the intersection of theory and production, he has repeatedly translated advanced dimension-reduction and optimization research into tools that scale to millions of nodes. Based in Atlanta, he brings a rare combination of deep algorithmic rigor and practical experience in large-scale ML systems.
11 years of coding experience
2 years of employment as a software developer
GED High School, GED High School at Flintridge Preparatory SchoolDoctor of Philosophy Computational Science, Doctor of Philosophy Computational Science at Georgia Institute of TechnologyMaster of Science Computer Science, Master of Science Computer Science at Wake Forest University
Github Skills (11)
nmf10
factorization10
manifold9
java7
ranking7
machine-learning6
science6
react6
computer-science5
distributed-systems4
ndarray4
Programming languages (2)
Github contributions (3)
ramkikannan/planc
Dec 2017 - Jan 2019
Distributed NMF/NTF Library
Contributions:37 commits in 1 year
ndarraymachine-learningdistributed-systemsmanifoldntf
kbhayashi/helloworld
Apr 2015 - Apr 2015
Contributions:1 branch in 1 day
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