Research Software Engineering (RSE)

Research software engineering addresses the use of software engineering principles for research applications. With a strong focus on Machine Learning, we strife to develop data processing tools which are applicable by practitioners and at the same time can conveniently be extended to answer scientific questions.

Focus Areas:

Projects and Software

We are currently developing the mlr3 universe of packages for Machine Learning in R. mlr3 implements the building blocks of Machine Learning in an efficient, object-oriented fashion. The base package is extended by a plethora of extension packages, summarized in the following figure:

mlr3 universe
mlr3 universe

For more details on the extension packages, see the Wiki page on GitHub. Other software projects are listed under Software.

Members

Name       Position
Dr. Michel Lang       PostDoc
Dr. Andreas Bender       PostDoc
Martin Binder       PhD Student
Stefan Coors       PhD Student
Susanne Dandl       PhD Student
Florian Pfisterer       PhD Student
Marc Becker       Software Engineer
Lennart Schneider       PhD Student

Publications

  1. Pfisterer F, Kern C, Dandl S, Sun M, Kim MP, Bischl B (2021) mcboost: Multi-Calibration Boosting for R. Journal of Open Source Software 6, 3453.
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  2. Rügamer D, Shen R, Bukas C et al. (2021) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. arXiv:2104.02705 [stat].
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  3. Sonabend R, Király FJ, Bender A, Bischl B, Lang M (2021) mlr3proba: An R Package for Machine Learning in Survival Analysis. Bioinformatics.
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  4. Moosbauer J, Binder M, Schneider L et al. (2021) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers.
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  5. Binder M, Pfisterer F, Lang M, Schneider L, Kotthoff L, Bischl B (2021) mlr3pipelines - Flexible Machine Learning Pipelines in R. Journal of Machine Learning Research 22, 1–7.
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  6. Becker M, Binder M, Bischl B et al. (2021) mlr3 book.
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  7. Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
  8. Lang M, Binder M, Richter J et al. (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
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  9. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786. link | pdf.
  10. Bender A, Scheipl F (2018) pammtools: Piece-wise exponential Additive Mixed Modeling tools. arXiv:1806.01042 [stat].
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  11. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
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  12. Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M (2017) mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1703.03373.
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  13. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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Contact

Feel free to contact us if you are looking for collaborations:

michel.lang [at] stat.uni-muenchen.de