Klaus Crusius
Senior System Engineer at HP
Frankfurt Rhine-Main Metropolitan Area Germany
Summary
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Rockstar🎓
Top SchoolKlaus Crusius is a Senior System Engineer based in the Frankfurt Rhine-Main area with 12 years of professional experience and a doctorate in mathematics from Johannes Gutenberg University Mainz. He combines deep numerical analysis expertise with practical systems engineering at HP, bringing rigorous algorithmic thinking to production-scale back-end problems. An active open-source contributor, Klaus has made substantive core contributions to the Julia language—improving set operations, comparison logic, NaN handling, and type inference—and strengthened scientific computing tooling through testing work on high-performance ODE solvers in SciML. His profile reflects a rare blend of formal mathematical training and hands-on coding rigor, able to translate numerical theory into robust, testable implementations. Outside work he is engaged in social volunteerism in Germany, tutoring mathematics and supporting integration of displaced persons, which informs a collaborative, service-oriented approach to engineering.
12 years of coding experience
Doctor of Natural Sciences, Mathematics, Doctor of Natural Sciences, Mathematics at Johannes Gutenberg University Mainz
German, English, Spanish
Github Skills (16)
algorithm10
data-manipulation10
data-structures10
algorithms10
differential-equations10
set-operations10
ordinary-differential-equations10
data-structure10
julia10
testing10
test-automation9
adaption9
adaptation9
programming-language9
language-design9
Programming languages (6)
Github contributions (5)
JuliaLang/julia
Aug 2017 - Jan 2022
The Julia Programming Language
Role in this project:
Back-end Developer
Back-end DeveloperContributions:64 reviews, 61 commits, 110 PRs in 4 years 6 months
Contributions summary:Klaus primarily contributed to the Julia programming language's core functionality, specifically focusing on set operations, comparison functions, and array manipulations. They implemented new methods for set operations, refactored existing comparison logic to use `isless`, and addressed issues related to handling `NaN` values in `findmin` and `findmax`. Furthermore, they made modifications to improve the type inference and implementation of functions like `replace`. The contributions included improvements to existing code and implementations of more robust and efficient algorithms.
sciencejulia-languagemachine-learningjulialangpetsc
SciML/OrdinaryDiffEq.jl
Dec 2020 - Jan 2021
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:8 reviews, 12 commits, 4 PRs in 9 days
Contributions summary:Klaus focused on developing and maintaining test cases for the `ordinarydiffeq.jl` repository. Their contributions include writing comprehensive tests for symplectic algorithms, ensuring their correctness. They also addressed issues related to static arrays and added tests using `ArrayPartition` to improve test coverage, and the user also included testing for continuous callbacks and verified that the code compiles.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
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