Riley Murray
Senior Member Of Technical Staff at ICSI - International Computer Science Institute
Livermore, California, United States
Summary
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Rockstar🎓
Top SchoolRiley Murray is a Senior Member of Technical Staff and applied mathematician with 11 years of experience blending research-grade theory and production software in convex optimization and randomized numerical linear algebra. He holds a PhD from Caltech and completed postdoctoral work at Berkeley where he led development of RandBLAS and RandLAPACK and contributed backend math to widely used CVXPY—adding native Mosek support, exponential cone handling, integer programming, and semidefinite dual recovery. At Sandia he advises the Quantum Performance Lab, and he also leads research efforts as a principal investigator at ICSI, bridging academic advances with national-lab applications. Riley’s background spans industry (Facebook data science) to international collaborations (Max Planck), revealing a pattern of turning deep theoretical insights into practical libraries and tools used by the optimization community. He is based in Livermore, CA, uses he/him pronouns, and posts represent his personal views.
10 years of coding experience
9 years of employment as a software developer
BS, Industrial Engineering and Operations Research, BS, Industrial Engineering and Operations Research at University of California, BerkeleyCalifornia Institute of Technology
American Sign Language
Github Skills (5)
mathematical-optimization10
cvxpy10
linear-programming10
convex-optimization10
python10
Programming languages (10)
Github contributions (5)
cvxpy/cvxpy
Feb 2018 - Jan 2023
A Python-embedded modeling language for convex optimization problems.
Role in this project:
Back-end Developer & Mathematician
Back-end Developer & MathematicianContributions:2 releases, 385 reviews, 149 commits in 4 years 11 months
Contributions summary:Riley implemented a new native interface for the Mosek solver within the CVXPY library, enhancing its capabilities. This included adding support for exponential cones, integer programming, and recovery of semidefinite dual variables. Furthermore, the user addressed issues related to the formatting of exponential cone constraints and added an example to show the benefits of specifying CVXPY problems in vectorized ways. The modifications involved changes to the Mosek interface module to support these new features, and to ensure the correct recovery of dual variables in non-discrete problems.
pythonconvex-optimizationproblemsoptimizationconvex
rileyjmurray/hqrrp
Mar 2022 - Aug 2023
Contributions:1 review, 6 PRs, 37 pushes in 1 year 4 months
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