About

After previously being a student assistant at the working group Computational Statistics at the Ludwig-Maximilians-University Munich, I joined it as a PhD student in june 2018 with a main focus on machine learning.

I have a Bachelor's Degree (B.Sc.) in Business Mathematics and Actuarial Sciences from the University of Applied Sciences Rosenheim and a Master's Degree (M.Sc.) in Statistics from the LMU Munich.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 344, 3rd floor

Phone: +49 89 2180 3196

daniel.schalk [at] stat.uni-muenchen.de

Research Interests

• Machine Learning
• Component-Wise boosting
• Distributed Computing
• Statistical Computing

You Can Find me on

• GitHub
• ResearchGate

References

1. Schalk D, Bischl B, Rügamer D (2022) Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. arXiv preprint arXiv:2210.07723.
   link | pdf .
2. Schalk D, Bischl B, Rügamer D (2022) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. Journal of Computational and Graphical Statistics.
   link | pdf .
3. Schalk D, Hoffmann V, Bischl B, Mansmann U (2022) Distributed non-disclosive validation of predictive models by a modified ROC-GLM. arXiv preprint arXiv:2202.10828.
   link | pdf .
4. *Coors S, *Schalk D, Bischl B, Rügamer D (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
   link | pdf .
5. Au Q, Schalk D, Casalicchio G, Schoedel R, Stachl C, Bischl B (2019) Component-Wise Boosting of Targets for Multi-Output Prediction. arXiv preprint arXiv:1904.03943.
   link | pdf.
6. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
   link | pdf.