Jason Riedy is a seasoned high-performance computing researcher and engineer with 18+ years of experience designing scalable algorithms and systems for graph analysis, sparse/dense linear algebra, and parallel irregular computation. His career spans academia and industry—from PhD work at UC Berkeley and long-term research at Georgia Tech to technical roles at Lucata and AMD—where he’s focused on novel computing architectures, floating-point nuance, and production-scale performance. He maintains an active scholarly footprint (ORCID, Google Scholar, Scopus, DBLP, MathSciNet) and has contributed to foundational libraries such as LAPACK/ScaLAPACK and SuperLU, reflecting deep numerical and parallel expertise. Known for tackling “impossible” problems in graph-structured data, he blends rigorous research with practical engineering and a commitment to free and open services. An attentive communicator, he deliberately filters unknown phone contacts and keeps a public, frequently updated CV and contact point for professional inquiries.
18 years of coding experience
31 years of employment as a software developer
Ph.D. Computer Science, Ph.D. Computer Science at University of California, Berkeley
B.S. Computer Science, B.S. Computer Science at University of Florida
Contributions:6 PRs, 9 pushes, 11 comments in 4 years
redisredis-moduledatabasegraphgraph-database
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