Kanika Sood is an Associate Professor and PhD candidate in Computer Science with a decade of experience spanning high-performance linear algebra, HPC, and machine learning for sparse linear solver selection. She transitioned from industry as a Systems Engineer at IBM to advanced research at the University of Oregon, contributing to projects at Argonne National Laboratory and Schlumberger. Her academic career includes teaching and curriculum design in programming and information technology, and she currently leads research and instruction at California State University, Fullerton. Kanika's work blends practical system integration experience with rigorous performance-focused research under Prof. Boyana Norris, producing methods to analyze and select high-performance solvers. Beyond publications, she has mined revision control and issue-tracking data to derive productivity metrics across major scientific software ecosystems, showing a rare combination of coding, measurement, and pedagogy. Based in Fullerton, CA, she leverages industry-hardened engineering practices to inform reproducible research and scalable computational solutions.
10 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, 3.77, Doctor of Philosophy (Ph.D.), Computer Science, 3.77 at University of Oregon
Faith Academy
Bachelor of Technology (B.Tech.), Computer Science, 8.3 GPA, Bachelor of Technology (B.Tech.), Computer Science, 8.3 GPA at Mody Institute of Technology & Science
Contributions:15 pushes, 1 branch in 1 year 11 months
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