Marcel Wever
Research Group Leader at L3S Research Center
Paderborn, North Rhine-Westphalia, Germany
Summary
🤩
Rockstar🎓
Top SchoolMarcel Wever is a research group leader and applied machine learning researcher with nine years of experience bridging academic rigor and practical AutoML engineering. He holds a summa cum laude PhD in Computer Science from Paderborn University and has held research and coordination roles at institutions including L3S, Leibniz Universität Hannover, LMU Munich, and the Munich Center for Machine Learning. Marcel contributes to open-source AutoML tooling—notably integrating the MLPlan framework with WEKA and scikit-learn in the widely used OpenML automlbenchmark—bringing deep expertise in pipeline encoding, metric support, and prediction runtime analysis. He combines hands-on implementation skills with academic leadership, managing education programs and research projects that translate complex model selection problems into reproducible benchmarks. Based in Paderborn, Germany, he is comfortable operating at the intersection of software engineering, empirical evaluation, and ML systems research. An attention to detail in encoding and column handling underscores his focus on reliable, production-ready ML workflows.
9 years of coding experience
7 years of employment as a software developer
Doctor, Computer Science, summa cum laude, Doctor, Computer Science, summa cum laude at Paderborn University
Github Skills (10)
scikit10
machine-learning10
benchmarking10
benchmark10
automl10
python10
scikit-learn10
java9
javas9
mlops8
Programming languages (7)
Github contributions (5)
openml/automlbenchmark
Jul 2020 - May 2021
OpenML AutoML Benchmarking Framework
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 2 PRs, 35 comments in 9 months
Contributions summary:Marcel primarily contributed to the `MLPlan` framework within the `automlbenchmark` repository. Their work involved integrating `MLPlan` with both WEKA and scikit-learn, adding support for various machine learning metrics. They also addressed issues related to encoding and column reordering within `MLPlan`, and added prediction runtime calculation.
automlbenchmarkingopenmlmachine-learningbenchmark
mwever/tpami-automlc
Apr 2020 - Apr 2025
Contributions:19 pushes, 1 branch in 5 years
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