Jakob Bossek
Akademischer Rat
Münster, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolJakob Bossek is an experienced research-focused software engineer and academic (Akademischer Rat) with 13 years of experience blending machine learning, statistics and computer science across German universities and an international postdoc in Adelaide. He holds a doctorate in Information Systems and a strong quantitative foundation from degrees in Computer Science and Statistics, which he applies to model-based optimization and visualization work. His open-source contributions to the well-known mlr machine learning ecosystem improved mlrMBO’s handling of discrete parameters and enhanced plotting/autoplot capabilities for 1D and 2D functions, reflecting a practical eye for reproducible, interpretable tooling. Jakob moves fluidly between research and engineering, turning theoretical optimization methods into usable examples and visual diagnostics. Colleagues know him for advancing academic projects into robust software components and for bringing statistical rigor to applied ML problems. Based in Münster and now affiliated with Universität Paderborn, he combines deep domain expertise with a track record of improving community tools.
12 years of coding experience
Diplom, Computer Science, Diplom, Computer Science at Technische Universität DortmundDoktor, Information Systems, Doktor, Information Systems at Westfälische Wilhelms-Universität Münster
German, English, Polish
Github Skills (20)
r10
machine-learning10
mlr10
hyperparameter-optimization10
ggplot10
data-science9
auto-tuning9
classification9
fine-tuning9
predictive-modeling9
regression9
performance-tuning9
learnpress8
learnr8
learndash8
Programming languages (6)
Github contributions (5)
mlr-org/mlr
Sep 2013 - Oct 2014
Machine Learning in R
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:27 commits, 10 PRs, 16 pushes in 1 year
Contributions summary:Jakob contributed to the mlr-org/mlr repository, which focuses on machine learning in R. The commits focused on extending the mlrMBO package, adding examples and functionality for optimizing functions with discrete and numeric parameters using model-based optimization (MBO). These changes included preparing the exampleRun function to handle discrete parameters, implementing plotting functions for 1D discrete functions, and enhancing the autoplot function for 1D and 2D numeric functions. These contributions aimed to improve the visualization and optimization capabilities of the package.
imbalance-correctionlearnersensemble-learningclassificationr-package
jakobbossek/grapherator
Dec 2017 - Sep 2021
A modular multi-step graph generator
Contributions:3 releases, 82 commits, 76 pushes in 3 years 10 months
graph-generatormulti-stepmodulargraph
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