Greg Peairs
Research Scientist at Amazon Web Services (AWS)
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolGreg Peairs is a research scientist focused on microwave electromechanics, photonics, and quantum information, bringing 14 years of cross-disciplinary experience spanning design, simulation, nanofabrication, data analysis, and software engineering. Currently at AWS driving quantum computing technologies, he combines hands-on experimental skill with production-minded tooling and test automation. His PhD in Physics from UC Santa Barbara and prior research at the University of Chicago and Stanford underpin a deep foundation in both theory and lab systems. Beyond experiments, he has contributed as a QA/test automation engineer to the Julia language’s core numerical test coverage, improving reliability for complex linear algebra and special-case arithmetic. Colocated in New York, he excels at bridging low-level device work with higher-level software infrastructure to accelerate reproducible quantum research. Colleagues rely on him for pragmatic problem-solving that connects nanofabrication realities to scalable computational tools.
14 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at UC Santa BarbaraBS, Physics, Honors with Distinction, BS, Physics, Honors with Distinction at Stanford University
Github Skills (9)
numerics10
computation10
numerical10
numeric10
lapack10
linear-algebra10
julia10
testing10
complex-numbers9
Programming languages (2)
Github contributions (5)
JuliaLang/julia
Sep 2016 - Feb 2017
The Julia Programming Language
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:6 commits, 5 PRs, 1 comment in 4 months
Contributions summary:Greg primarily contributed to improving the test coverage of the Julia programming language's core functionality and its linear algebra library. Their work involved enabling testsets across various modules, including complex number arithmetic, linear algebra (LAPACK), and specific functions like `sqrt` and `log`. They implemented comprehensive tests with nested structures to cover different datatypes and edge cases, ensuring the accuracy and reliability of the Julia language's numerical computations.
sciencejulia-languagemachine-learningjulialangpetsc
gpeairs/julia
Sep 2015 - Feb 2017
Contributions:4 pushes, 14 branches in 1 year 5 months
julia-languageapproachtechnical-computingcomputingjulia
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