MCML - Foundations of Machine Learning
A1 | Statistical Foundations & Explainability
Research is being conducted at MCML to improve the reliability, interpretability, and acceptability of results obtained with ML algorithms for their practical application through better integration of statistical concepts. Key challenges include the integration of uncertainty quantification into ML algorithms, the explainability of ML models, the simplification of ML methods, and the incorporation of prior knowledge into ML algorithms.
Stefan Bauer
Prof. Dr.
Algorithmic Machine Learning & Explainable AI
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Anne-Laure Boulesteix
Prof. Dr.
Biometry in Molecular Medicine
Matthias Feurer
Prof. Dr.
Statistical Learning & Data Science
Göran Kauermann
Prof. Dr.
Applied Statistics in Social Sciences, Economics and Business
Thomas Nagler
Prof. Dr.
Computational Statistics & Data Science
Volker Schmid
Prof. Dr.
Bayesian Imaging & Spatial Statistics
Andreas Döpp
Dr. habil.
Associate
Data-driven methods in Physics and Optics
Georgios Kaissis
Dr.
Associate
Privacy-Preserving and Trustworthy AI
A2 | Mathematical Foundations
Some of the tremendous successes of ML have been achieved through the use of mathematical insights. The contribution of our mathematicians in MCML can be divided into two main research areas: Mathematics for ML, i.e. mathematical principles are used to develop new reliable ML algorithms, and ML for mathematics, i.e. ML is used to advance mathematical research, e.g. in imaging, inverse problems, optimal control, or numerical analysis of partial differential equations.
Gitta Kutyniok
Prof. Dr.
Mathematical Foundations of Artificial Intelligence
Holger Rauhut
Prof. Dr.
Mathematical Data Science and Artificial Intelligence
Johannes Maly
Prof. Dr.
Associate
Mathematical Data Science and Artificial Intelligence
A3 | Computational Models
Mathematical models and statistical concepts, which are core elements of ML methods, must be reflected by efficient algorithmic implementations. Furthermore, the execution of corresponding algorithms requires a suitable computational infrastructure. Currently, the steady growth of ML applications brings new algorithmic problems and computational challenges that MCML is addressing in this research area.
Thomas Gabor
Prof. Dr.
Associate
Technology and Research on Artificial Intelligence Laboratory
Johannes Kinder
Prof. Dr.
Associate
Programming Languages and Artificial Intelligence
