Marius Lindauer
Prodekan (Vice Dean) at Leibniz Universität Hannover
Hanover, Lower Saxony, Germany
Summary
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Rockstar🎓
Top SchoolMarius Lindauer is a professor and vice dean at Leibniz Universität Hannover with over a decade of experience advancing automated machine learning and practical AI. He leads academic institutes and community efforts (co-head of AutoML.org) while maintaining hands-on contributions to influential open-source tools such as SMAC3, a Bayesian optimization package for hyperparameter tuning. His research bridges deep learning and classical AI methods—SAT, planning, evolutionary algorithms—to make AI more accessible by automating model and algorithm selection. Known for rigorous, reproducible solutions, he earned a summa cum laude doctorate in computer science and often brings production-quality engineering into academic projects. Colleagues value him for combining leadership roles with concrete backend development and tool-building that directly help practitioners.
11 years of coding experience
2 years of employment as a software developer
M.Sc, Informatics, M.Sc, Informatics at Universität PotsdamDr. rer. nat, Computer Science, summa cum laude, Dr. rer. nat, Computer Science, summa cum laude at University of Potsdam
English, German
Github Skills (5)
hyperparameter-optimization10
automated-machine-learning10
python10
unit-testing8
command-line-interface7
Programming languages (6)
Github contributions (5)
automl/SMAC3
Sep 2015 - Sep 2021
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 releases, 9 reviews, 402 commits in 6 years 1 month
Contributions summary:Marius primarily focused on implementing and modifying the core functionalities of the target algorithm (TAE) within the SMAC framework. This involved the creation of a dummy method for the TAE and modification of its structure. The user also added several utility classes related to the reading of input and command-line options. In addition, modifications were made to a runhistory class and associated unit tests were performed.
bayesian-optimizationhyperparameter-tuninghyperparameterautomated-machine-learninggaussian-process
automl-edu/AutoMLLecture
Apr 2019 - May 2022
Lecture on Automated Machine Learning
Contributions:8 reviews, 204 commits, 13 PRs in 3 years 1 month
pythondata-scienceautomated-machineautomated-machine-learningmachine-learning
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