About

I am a PhD student and research associate at the Chair of Statistical Learning and Data Science since spring 2020 at the Ludwig Maximilian University Munich.

I obtained a Bachelor's degree (B.Sc.) in Economics and a Master's degree (M.Sc.) in Statistics from the Ludwig Maximilian University Munich. My research interest is in the intersection of machine learning and statistics. I am particularly interested in deep learning and survival analysis at the moment.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

philipp.kopper [at] stat.uni-muenchen.de

Research Interests

• Statistical Deep Learning
• Survival Analysis in Machine Learning
• Piece-wise exponential additive mixed models

You Can Find me on

• GitHub
• LinkedIn

References

1. Kopper P, Wiegrebe S, Bischl B, Bender A, Rügamer D (2022) DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis Advances in Knowledge Discovery and Data Mining, pp. 249–261. Springer International Publishing.
   link|pdf.
2. Hartl WH, Kopper P, Bender A, Scheipl F, Day AG, Elke G, Küchenhoff H (2022) Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models. Critical Care 26, 7.
   link|pdf.
3. Seibold H, Nalenz M, Kopper P, Czerny S, Decke S, Dieterle R, Eder T, Fohr S, Hahn N, Hartmann R, Heindl C, others (2021) A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses. PLOS ONE 16, e0251194.
   link.
4. Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2021) Semi-Structured Deep Piecewise Exponential Models Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, pp. 40–53. PMLR.
   link|pdf.
5. Kopper P (2019) Lime and neighborhood. In: In: Molnar C (ed) Limitations of Interpretable Machine Learning Methods,
   link.