Konrad Kapp
Software Engineer at Arista Networks
Dublin, Ireland
Summary
👤
Senior🎓
Top SchoolKonrad Kapp is a software engineer based in Dublin with a decade of experience blending computational statistics, quantitative finance, and systems programming. With an MSc in Mathematical Statistics and paused PhD work in Computer Science, he brings rigorous mathematical thinking to C++ and Python development and has recently expanded into C#. At Arista Networks he applies this cross-disciplinary background to production systems, while his open-source documentation contributions to NumPy reveal a knack for clarifying complex technical APIs. Comfortable across languages and driven to learn new frameworks, he pairs analytical depth in risk/financial modeling with practical engineering that improves both code and its documentation.
10 years of coding experience
Master of Science (MSc), Mathematical Statistics, Master of Science (MSc), Mathematical Statistics at Nelson Mandela Metropolitan UniversityDoctor of Philosophy - PhD, Computer Science, (paused), Doctor of Philosophy - PhD, Computer Science, (paused) at University of Cape TownBCommHons Financial Risk Management (Quant Finance), BCommHons Financial Risk Management (Quant Finance) at Stellenbosch University
Afrikaans, English
Github Skills (6)
documentation10
numpy10
rs9
c115
c175
python4
Programming languages (4)
Github contributions (5)
numpy/numpy
May 2017 - Jun 2017
The fundamental package for scientific computing with Python.
Role in this project:
Technical Writer
Technical WriterContributions:6 commits, 13 PRs, 18 comments in 13 days
Contributions summary:Konrad primarily contributed to documentation within the NumPy repository. Their work involved fixing incorrect function signatures in UFunc documentation, correcting spelling and grammar mistakes in the "Numpy C Code Explanations" page, and removing all references to the outdated "bigndarray" feature. This suggests a focus on enhancing the clarity, accuracy, and completeness of the project's documentation. The impact of these contributions is improved readability and maintainability of documentation, making it easier for users to understand and utilize the NumPy library.
pythonscientific-computingnumpy
k-kapp/ShortCppProblems
Nov 2016 - Jan 2021
Contributions:92 pushes, 1 branch in 4 years 3 months
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