Giuseppe Casalicchio


I joined the working group Computational Statistics at the Ludwig-Maximilians-University (LMU) Munich as a PhD student in 2014. Since March 2019, I am supporting the working group within the Munich Center for Machine Learning (MCML) as a Postdoc. I am organizator and instructor for several data science related courses at the Munich R Courses and Professional Data Science Certificate Program at the LMU. Besides that I am an active developer for a variety of R-packages and one of the main developers of OpenML (Open Machine Learning in R). From 2011-2015 I've worked for the Statistical Advisory Lab (StabLab) and as a statistical consultant.
My research interests are benchmarking experiments in machine learning, reproducible research, hyperparameter tuning, as well as automatic and interpretable machine learning.

I have a Bachelor's Degree (B.Sc.) and Master's Degree (M.Sc.) in Statistics from the LMU Munich.


Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 344, 3rd floor

Phone: +49 89 2180 3196

giuseppe.casalicchio [at]

Research Interests

You Can Find me on


  1. Casalicchio G, Molnar C, Bischl B (2018) Visualizing the Feature Importance for Black Box Models. arXiv preprint arXiv:1804.06620. arXiv:1804.06620.
  2. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786. 10.21105/joss.00786.
  3. Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel Classification with R Package mlr. The R Journal 9, 352–369.
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  4. Casalicchio G, Bossek J, Lang M et al. (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 1–15. arXiv:1701.01293.
  5. Casalicchio G, Lesaffre E, Küchenhoff H, Bruyneel L (2017) Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being. Journal of Nursing Scholarship 49, 537–547. Researchgate.
  6. Bischl B, Casalicchio G, Feurer M et al. (2017) OpenML benchmarking suites and the OpenML100. arXiv preprint arXiv:1708.03731. arXiv:1708.03731.
  7. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2016) The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72, 392–401. Researchgate.
  8. Bischl B, Lang M, Kotthoff L et al. (2016) mlr: Machine Learning in R. The Journal of Machine Learning Research 17, 5938–5942. JMLR 17(1).
  9. Casalicchio G, Tutz G, Schauberger G (2015) Subject-specific Bradley–Terry–Luce models with implicit variable selection. Statistical Modelling 15, 526–547. Researchgate.
  10. Bergmann S, Ziegler N, Bartels T et al. (2013) Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase. Poultry science 92, 1171–1176. Researchgate.
  11. Ziegler N, Bergmann S, Huebei J et al. (2013) Climate parameters and the influence on the foot pad health status of fattening turkeys BUT 6 during the early rearing phase. BERLINER UND MUNCHENER TIERARZTLICHE WOCHENSCHRIFT 126, 181–188. Researchgate.