Michael Borregaard
Software Engineer at GLOBE Institute
Copenhagen, Capital Region of Denmark
Summary
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RockstarMichael Borregaard is a software engineer in Copenhagen with 11 years of experience blending scientific rigor and production-grade code, informed by his role as an associate professor at the University of Copenhagen in macroecology and island biogeography. He is an active open-source contributor in the Julia ecosystem, improving visualization libraries like Plots.jl and Makie.jl and enhancing statistical tooling in GLM.jl with performance-minded refactors and robust prediction intervals. Michael focuses on core functionality and code quality—adding features, fixing subtle bugs (e.g., histogram binning and annotation handling), and reducing allocations to speed up analytics. Comfortable across back-end and full-stack contributions, he brings a researcher's attention to testing and documentation, making complex numerical tools more reliable and usable.
11 years of coding experience
Danish, English, Spanish
Stackoverflow
7,974reputation
304kreached
147answers
15questions
plot
top-5%
julia
top-1%
Github Skills (32)
algorithm10
data-visualizations10
algorithms10
visualization10
data-science10
testing10
statistics10
lm10
data-visualisation10
plot10
graphic10
regression10
attribute10
statistical-models10
predictive-modeling10
Programming languages (20)
Github contributions (5)
JuliaPlots/Plots.jl
Oct 2016 - Jan 2022
Powerful convenience for Julia visualizations and data analysis
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:44 releases, 3 reviews, 545 commits in 5 years 3 months
Contributions summary:Michael contributed to the Plots.jl repository by merging pull requests, primarily integrating changes from the 'tbreloff/master' branch. These merges involved modifications to various backend files like `gr.jl`, `plotlyjs.jl`, and `plotly.jl`, as well as core files such as `series.jl` and `Plots.jl`. Their work included enhancements to the visualization capabilities, support for color libraries, and bug fixes related to histogram binning and annotation handling.
data-analysisjulia-languageconveniencevisualizationsdata-visualization
JuliaStats/GLM.jl
Dec 2016 - Jan 2019
Generalized linear models in Julia
Role in this project:Data Scientist
Data ScientistContributions:1 review, 18 commits, 3 PRs in 2 years 1 month
Contributions summary:Michael significantly contributed to the `glm.jl` repository by implementing and improving the `predict` function, focusing on adding confidence intervals and prediction intervals for linear models. They refactored the code to improve performance by minimizing allocations. The user also added tests to ensure the correctness of the prediction calculations, covering various confidence levels and interval types. Further, the user improved the usability of the predict function with docstrings and keyword arguments.
regressionstatisticsdataframesgeneralized-linear-modelsdata-science
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