Pascal Kerschke
Professor For Big Data Analytics In Transportation, Technische Universität Dresden
Dresden, Saxony, Germany
Summary
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Rockstar🎓
Top SchoolPascal Kerschke is a professor specializing in big data analytics for transportation at Technische Universität Dresden with 12 years of experience bridging advanced data science and applied transportation research. He holds a doctorate in Information Systems and a strong academic foundation in data science and data management from TU Dortmund and Münster. Pascal combines rigorous research with practical impact, focusing on scalable analytics that inform transportation planning and operations. He contributes to the open-source ML community through technical documentation for the widely used mlr machine learning package, improving tutorials on resampling, tuning, and variable selection. Colleagues describe him as someone who turns complex methodological concepts into accessible tools and teaching materials for both students and practitioners. Based in Dresden, he blends academic leadership with hands-on contributions that enhance reproducibility and usability in applied ML.
12 years of coding experience
Master of Science - MS, Data Science, Master of Science - MS, Data Science at Technische Universität DortmundDoctor's Degree, Information Systems, Doctor's Degree, Information Systems at Westfälische Wilhelms-Universität Münster
Github Skills (7)
user-manual10
machine-learning10
learn-ruby-on-rails10
basics10
mlr10
documentation10
r10
Programming languages (4)
Github contributions (5)
mlr-org/mlr
Oct 2013 - Mar 2017
Machine Learning in R
Role in this project:
Technical Writer
Technical WriterContributions:50 commits, 34 PRs, 17 pushes in 3 years 4 months
Contributions summary:Pascal's contributions primarily involve updating documentation files within the `mlr-org/mlr` repository. Their commits focus on modifying `.Rnw` files within the `doc/` and `doc/tutorial/` directories. These updates introduce new content, correct existing information, and enhance the tutorial sections on topics like resampling, performance evaluation, hyperparameter tuning, and variable selection, improving the overall clarity and completeness of the documentation for the mlr package.
imbalance-correctionlearnersensemble-learningclassificationr-package
kerschke/kerschke.github.io
Jun 2017 - Mar 2021
Contributions:4 PRs, 42 pushes, 1 branch in 3 years 9 months
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