I am a Lecturer at the Chair of Statistical Learning and Data Science in the Department of Statistics at the LMU Munich. I have a Bachelor's Degree (B.Sc.) in Statistics with a minor in Computer Science, a Master's Degree (M.Sc.) in Statistics with specialization in theory and a Ph.D. (Dr.rer.nat.) in Statistics with focus on functional data analysis, gradient boosting and statistical inference. During my Ph.D. I worked for the Biostatistics Working Group at the LMU, where I did my Ph.D. from Oct 2014 to Jun 2018 under the supervision of Prof. Dr. Sonja Greven. My work was partly funded by the Emmy Noether project ‘Statistical Methods for Longitudinal Functional Data’.
After a short PostDoc stay at the same group I worked for 1 year as a Senior Data Scientist in the industry with strong focus on Data Engineering and Deep Learning research. Together with other Postdocs in our group I am currently leading the subgroups Machine Learning and Deep Learning.
You can find more information about me on my personal website.
Institut für Statistik
David.Ruegamer [at] stat.uni-muenchen.de
I offer various topics for Master theses mainly but not exclusively on the topics listed below. If you are interested, please send me your field of interest or your ideas, a CV and your current transcript of records.
- Gradient Boosting
- Distributional Regression
- Various Deep Learning Topics
- (Variational) Autoencoder
- Generative Adversarial Networks
- Natural Language Processing
- Network Architectures
- Functional Regression Models
- Time Series Analysis
- Survival Analysis
- Recommender Systems, Matrix and Tensor Factorization
You Can Find me on
deepregressionSemi-Structured Deep Distributional Regression coming soon
selfmadeSELective inference For Mixed and ADditive model Estimators coming soon
FDboostBoosting Functional Regression Models on CRAN and github
cAIC4Conditional Akaike Information Criterion for ‘lme4’ on CRAN and github
iboostInference for Model-based Boosting on github
coinflibsConditional Inference after Likelihood-based Selection on github
effortlessefficient operations on row-wise tensor product linked evaluations with special structures on github
- Fritz C, Dorigatti E, Rügamer D (2021) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv:2101.00661 [cs, stat].
- Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2020) Semi-Structured Deep Piecewise Exponential Models. arXiv:2011.05824 [cs, stat].
- Bender A, Rügamer D, Scheipl F, Bischl B (2020) A General Machine Learning Framework for Survival Analysis.
- Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
- Rügamer D, Kolb C, Klein N (2020) A Unified Network Architecture for Semi-Structured Deep Distributional Regression.
- Liew BXW, Rügamer D, Stöcker A, De Nunzio AM (2020) Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting. Gait & Posture 75, 146–150.
- Liew B, Rügamer D, De Nunzio A, Falla D (2020) Interpretable machine learning models for classifying low back pain status using functional physiological variables. European Spine Journal 29, 1845–1859.
- Liew BXW, Rügamer D, Abichandani D, De Nunzio AM (2020) Classifying individuals with and without patellofemoral pain syndrome using ground force profiles – Development of a method using functional data boosting. Gait & Posture 80, 90–95.
- Liew BXW, Peolsson A, Rügamer D et al. (2020) Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach. Scientific Reports.
- Berninger C, Stöcker A, Rügamer D (2020) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. arXiv preprint arXiv:2006.05750.
- Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
- Rügamer D, Baumann P, Greven S (2020) Selective Inference for Additive and Mixed Models. arXiv preprint arXiv:2007.07930.
- Baumann PFM, Hothorn T, Rügamer D (2020) Deep Conditional Transformation Models. arXiv preprint arXiv:2010.07860.
- Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv preprint arXiv:2010.06889.
- Deep Semi-Supervised Learning for Time Series Classification (2020).
- Rügamer D, Brockhaus S, Gentsch K, Scherer K, Greven S (2018) Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals. Journal of the Royal Statistical Society: Series C (Applied Statistics) 67, 621–642.
- Rügamer D, Greven S (2018) Selective inference after likelihood-or test-based model selection in linear models. Statistics & Probability Letters 140, 7–12.
- Säfken B, Rügamer D, Kneib T, Greven S (2018) Conditional model selection in mixed-effects models with caic4. to appear in the Journal of Statistical Software.
- Brockhaus S, Rügamer D, Greven S (2017) Boosting Functional Regression Models with FDboost. to appear in the Journal of Statistical Software.
- Klüser L, Holler PJ, Simak J et al. (2016) Predictors of sudden cardiac death in Doberman Pinschers with dilated cardiomyopathy. Journal of veterinary internal medicine 30, 722–732.
- Gillhuber J, Rügamer D, Pfister K, Scheuerle MC (2014) Giardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany. BMC research notes 7, 112.