David Rügamer

About

I am currently Interim Professor for Computational Statistics at the RWTH Aachen (lecturer at the LMU Munich on leave). Before I was interim professor for Data Science at the Statistics department of 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 at the Chair of Statistical Learning and Data Science I am further leading the subgroups Probabilistic Machline and Deep Learning and Boosting.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

David.Ruegamer [at] stat.uni-muenchen.de

Teaching

Summer 2021:
- Applied Deep Learning with TensorFlow and PyTorch
- Predictive Modeling
Winter 2020/21:
- Deep Learning
Summer 2020:
- Introduction to R
- Predictive Modeling

Thesis

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.

Research Interests

My main research interest lies in the combination of structured additive models and deep neural networks, scalable statistical modeling and uncertainty quantification in machine learning. You can find my research statement here. Other topics I work on / find interesting:

Selective Inference
Gradient Boosting
Distributional Regression
Transformation Models / Normalizing Flows
Functional Data Analysis
Time Series Analysis
Survival Analysis
Recommender Systems, Matrix and Tensor Factorization
Optimization

News

News on my personal homepage

You Can Find me on

Software

deepregression Semi-Structured Deep Distributional Regression on Github and CRAN
selfmade SELective inference For Mixed and ADditive model Estimators on Github and CRAN (Examples)
FDboost Boosting Functional Regression Models on CRAN and github
cAIC4 Conditional Akaike Information Criterion for ‘lme4’ on CRAN and github
iboost Inference for Model-based Boosting on github (Examples)
coinflibs Conditional Inference after Likelihood-based Selection on github (Examples)
effortless efficient operations on row-wise tensor product linked evaluations with special structures on github

References

Rügamer D, Bender A, Wiegrebe S et al. (2022) Factorized Structured Regression for Large-Scale Varying Coefficient Models Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
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Klaß A, Lorenz S, Lauer-Schmaltz M et al. (2022) Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift IJCAI-ECAI 2022, 1st International Workshop on Spatio-Temporal Reasoning and Learning,
Fritz C, Nicola GD, Günther F et al. (2022) Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. Journal of Computational & Graphical Statistics.
Rügamer D (2022) Additive Higher-Order Factorization Machines. arXiv preprint arXiv:2205.14515.
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Rügamer D, Kolb C, Fritz C et al. (2022) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Journal of Statistical Software (provisionally accepted).
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Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. arXiv preprint arXiv:2204.03342.
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Liew BXW, Kovacs FM, Rügamer D, Royuela A (2022) Machine learning for prognostic modelling in individuals with non-specific neck pain. European Spine Journal.
Fritz C, Dorigatti E, Rügamer D (2022) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Scientific Reports 12, 2045–2322.
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Rügamer D, Baumann P, Greven S (2022) Selective Inference for Additive and Mixed Models. Computational Statistics and Data Analysis 167, 107350.
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Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Cross-Modal Common Representation Learning with Triplet Loss Functions. arXiv preprint arXiv:2202.07901.
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Ott F, Rügamer D, Heublein L et al. (2022) Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. arXiv preprint arXiv:2202.07036.
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Rügamer D, Baumann PFM, Kneib T, Hothorn T (2022) Probabilistic Time Series Forecasts with Autoregressive Transformation Models. arXiv:2110.08248 [cs, stat].
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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.
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Liew BXW, Rügamer D, Duffy K, Taylor M, Jackson J (2021) The mechanical energetics of walking across the adult lifespan. PloS one 16, e0259817.
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Mittermeier M, Weigert M, Rügamer D (2021) Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. NeurIPS 2021, Tackling Climate Change with Machine Learning.
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Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021, Bridging the Gap: From Machine Learning Research to Clinical Practice.
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Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021, Deep Generative Models and Downstream Applications.
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Rügamer D, Baumann PFM, Kneib T, Hothorn T (2021) Transforming Autoregression: Interpretable and Expressive Time Series Forecast. arXiv:2110.08248 [cs, stat].
link|pdf.
Liew BXW, Rügamer D, Zhai XJ, Morris S, Netto K (2021) Comparing machine, deep, and transfer learning in predicting joint moments in running. Journal of Biomechanics.
Schalk D, Bischl B, Rügamer D (2021) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization.
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Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2021) Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),
Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1–6.
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Rezaei M, Dorigatti E, Rügamer D, Bischl B (2021) Learning Statistical Representation with Joint Deep Embedded Clustering. arXiv preprint arXiv:2109.05232.
Falla D, Devecchi V, Jimenez-Grande D, Rügamer D, Liew B (2021) Modern Machine Learning Approaches Applied in Spinal Pain Research. Journal of Electromyography and Kinesiology.
*Coors S, *Schalk D, Bischl B, Rügamer D (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
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Berninger C, Stöcker A, Rügamer D (2021) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Journal of Forecasting.
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Baumann PFM, Hothorn T, Rügamer D (2021) Deep Conditional Transformation Models Machine Learning and Knowledge Discovery in Databases. Research Track, pp. 3–18. Springer International Publishing.
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Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2021) Semi-Structured Deep Piecewise Exponential Models. In: In: Greiner R , In: Kumar N , In: Gerds TA , In: Schaar M van der (eds) Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, pp. 40–53. PMLR.
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Liew B, Lee HY, Rügamer D et al. (2021) A novel metric of reliability in pressure pain threshold measurement. Scientific Reports (Nature).
Bender A, Rügamer D, Scheipl F, Bischl B (2021) A General Machine Learning Framework for Survival Analysis. In: In: Hutter F , In: Kersting K , In: Lijffijt J , In: Valera I (eds) Machine Learning and Knowledge Discovery in Databases, pp. 158–173. Springer International Publishing.
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Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv:2010.06889 [cs, stat].
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Rügamer D, Kolb C, Klein N (2020) A Unified Network Architecture for Semi-Structured Deep Distributional Regression. arXiv:2002.05777 [cs, stat].
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Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
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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.
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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.
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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.
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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.
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Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
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.