Katharine Hyatt
Senior Applied Scientist at Amazon Web Services (AWS)
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolKatharine Hyatt is a Senior Applied Scientist at AWS with 15 years of experience blending research-grade numerical computing and production ML engineering. She holds a PhD in Condensed Matter and Materials Physics and spent postdoctoral years at the Flatiron Institute, bringing deep expertise in tensor methods and scientific computing into industry-scale problems. An active open-source contributor across Julia and Python ecosystems, she has strengthened core projects like Julia itself, CUDA.jl, ITensors.jl, and NumPy through testing, documentation, and feature work—helping improve reliability in widely used scientific libraries. Katharine’s background shows a rare combination of formal theoretical training and meticulous QA/test automation, making her adept at turning complex mathematical algorithms into robust, production-ready software.
15 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD Condensed Matter and Materials Physics, Doctor of Philosophy - PhD Condensed Matter and Materials Physics at UC Santa BarbaraBachelor of Science - BS Theoretical and Mathematical Physics, Bachelor of Science - BS Theoretical and Mathematical Physics at University of Waterloo
Github Skills (28)
unit-testing10
graph-algorithms10
testing10
mathematics10
machine-learning10
math10
data-structure10
numpy10
maths10
tensorflow10
gpu10
doctest10
cuda10
data-structures10
deque10
Programming languages (9)
Github contributions (5)
JuliaLang/julia
Jan 2015 - Jan 2023
The Julia Programming Language
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:29 reviews, 1298 commits, 1147 PRs in 8 years 2 months
Contributions summary:Katharine's commits focus on enhancing the testing framework of the Julia programming language. The contributions primarily involve adding test cases for various methods and functionalities within the language's core and standard libraries. This includes extensive testing of features like basic arithmetic, string manipulations, and specific functions, demonstrating a focus on ensuring the quality, reliability and robustness of the language's core functionalities. Furthermore, the user is involved in enhancing the testing structure by adding error checking and integration tests for the mathematical functions.
sciencejulia-languagemachine-learningjulialangpetsc
JuliaArrays/StaticArrays.jl
May 2017 - Jun 2018
Statically sized arrays for Julia
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:62 commits, 57 PRs, 22 pushes in 1 year
Contributions summary:Katharine primarily contributed to the testing infrastructure and test suite within the `staticarrays.jl` repository. Their commits focused on writing and expanding unit tests for various components, including `SizedArray`, linear algebra functionalities, and matrix operations. The user's work involved adding new test cases to improve code coverage and also focused on error testing within the library.
slicearraysjulia
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