Data Science Group @ LMU Munich

Statistics, Data Science and Machine Learning


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Team


Prof. Dr. David Ruegamer

Prof. Dr. David Ruegamer


Associate professor

Associate Professor and Group Leader at LMU since summer 2023. Before David was Interim Professor at LMU Munich, RWTH Aachen, TU Dortmund and also Senior Data Scientist in 2019. He earned his PhD in Statistics in 2018 (supervisor Sonja Greven).

Marcel Arpogaus

Marcel Arpogaus


PhD Student (co-supervised)

Marcel is PhD Student at University of Goettingen (supervisor Thomas Kneib) and works on multivariate transformation models.

Dr. Philipp Baumann

Dr. Philipp Baumann


PhD Student (co-supervised)

Philipp is a former PhD Student at ETH Zurich (supervisors Torsten Hothorn and Jan-Egbert Sturm) and works on (autoregressive) transformation models.

Daniel Dold

Daniel Dold


PhD Student (co-supervised)

Daniel is PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl and Oliver Duerr) and works on uncertainty quantification in Bayesian neural networks.

Emilio Dorigatti

Emilio Dorigatti


PhD Student (co-supervised)

Emilio is PhD Student in the Statistical Learning and Data Science Group (supervisors Bernd Bischl and Benjamin Schubert) and works on uncertainty quantification in semi-structured neural networks.

Chris Kolb

Chris Kolb


PhD Student (co-supervised)

Chris is PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and works on sparsity in neural networks.

Philipp Kopper

Philipp Kopper


PhD Student (co-supervised)

Philipp is a PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and works on deep survival analysis.

Tobias Pielok

Tobias Pielok


PhD Student (co-supervised)

Tobias is a PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and works on Bayesian deep learning and model-based optimization.

Katharina Rath

Katharina Rath


PhD Student (co-supervised)

Katharina is a PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and at the Max Planck Institute for Plasma Physics (IPP) (supervisors Udo von Toussaint and Christopher Albert) and works on Gaussian processes.

Philipp Schiele

Philipp Schiele


PhD Student (co-supervised)

Philipp is a PhD Student in the Statistics and Econometrics Group (supervisor Stefan Mittnik) and works on deep time series models and convex optimization.

Emanuel Sommer

Emanuel Sommer


PhD Student

Emanuel is a PhD Student in the Data Science Group. Previously he worked on dependence modeling in the realm of risk measure forecasting and as a Data Scientist, specializing in large-scale Learning-to-Rank Applications. He is currently working on Bayesian Deep Learning.

Rickmer Schulte

Rickmer Schulte


PhD Student

Rickmer is a PhD Student in the Data Science Group. He is currently working on Optimization and Boosting methods.

Theresa Stueber

Theresa Stueber


PhD Student (co-supervised)

Theresa is a PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and in the Clinical Data Science Group (supervisor Michael Ingrisch), and works in the area of machine and deep learning with radiological data.

Tobias Weber

Tobias Weber


PhD Student

Tobias is a PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and in the Clinical Data Science Group (supervisor Michael Ingrisch) and works on generative models in the medical image domain.

Lisa Wimmer

Lisa Wimmer


PhD Student (co-supervised)

Lisa is a PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and works on uncertainty quantification in deep learning.

Past Visitors


Dr. Lucas Kook

Dr. Lucas Kook


Postdoc (Visited in 2023)

Lucas did his PhD Student at the University of Zurich with Beate Sick and Torsten Hothorn and now works on causality and distributional regression models with deep learning in the Copenhagen Causality Lab.

Andrew McInerney

Andrew McInerney


PhD Student (Visited in Summer 2023)

Andrew is a PhD Student at the University of Limerick with Kevin Burke and works on neural networks from a statistical-modelling perspective.

Alumni


Jann Goschenhofer

Jann Goschenhofer


PhD Student (co-supervised)

Jann was PhD Student in the Statistical Learning and Data Science Group (supervisor Bernd Bischl) and works on learning with limited labeled data.

Dr. Felix Ott

Dr. Felix Ott


Former (co-supervised) PhD Student

Felix is a former PhD student of Bernd Bischl and Christopher Mutschler from Fraunhofer IIS who obtained his PhD in 2023 for his work on camera-based localization with deep learning methods and multimodal information fusion for pose estimation.

Dr. Daniel Schalk

Dr. Daniel Schalk


Former (co-supervised) PhD Student

Daniel is a former PhD Student of Bernd Bischl and obtained his PhD for his work on component-wise gradient boosting.

Publications


We try to keep our publications up to date on Google Scholar.


Recent Notable Papers


  • Scalable Higher-Order Tensor Product Spline Models AIStats 2024 (ArXiv)
  • Bayesian Semi-structured Subspace Inference AIStats 2024 (ArXiv)
  • Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI Position Paper (ArXiv)
  • Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction WACV 2024 (ArXiv)
  • Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization (ArXiv)
  • Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry ECML-PKDD 2023 (Best-Paper Research Track, ArXiv)
  • A New PHO-rmula for Improved Performance of Semi-Structured Networks ICML 2023 (ArXiv)
  • Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis PAKDD 2023 (ArXiv)
  • Approximate Bayesian Inference with Stein Functional Variational Gradient Descent ICLR 2023 (OpenReview)
  • Frequentist Uncertainty Quantification in Semi-Structured Neural Networks AIStats 2023 (MLR)
  • Semi-Structured Distributional Regression The American Statistician (2023) (ArXiv)

We are grateful for funding from LMU Munich, BMBF and the MCML.