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A | Foundations of Machine Learning

aims at strengthening the competence in Statistical Foundations and Explainability, Mathematical Foundations, and Computational Methods. These fields form the basis for all methodological advances.

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.

Link to Stefan Bauer

Stefan Bauer

Prof. Dr.

Algorithmic Machine Learning & Explainable AI (TUM)

Link to Bernd Bischl

Bernd Bischl

Prof. Dr.

Statistical Learning & Data Science (LMU)

Link to Anne-Laure Boulesteix

Anne-Laure Boulesteix

Prof. Dr.

Biometry in Molecular Medicine (LMU)

Link to Mathias Drton

Mathias Drton

Prof. Dr.

Mathematical Statistics (TUM)

Link to Matthias Feurer

Matthias Feurer

Prof. Dr.

Statistical Learning & Data Science (LMU)

Link to Göran Kauermann

Göran Kauermann

Prof. Dr.

Applied Statistics in Social Sciences, Economics and Business (LMU)

Link to Thomas Nagler

Thomas Nagler

Prof. Dr.

Computational Statistics & Data Science (LMU)

Link to David Rügamer

David Rügamer

Prof. Dr.

Data Science Group (LMU)

Link to Fabian Scheipl

Fabian Scheipl

PD Dr.

Functional Data Analysis (LMU)

Link to Volker Schmid

Volker Schmid

Prof. Dr.

Bayesian Imaging & Spatial Statistics (LMU)

Link to Vincent Fortuin

Vincent Fortuin


Affiliated Senior Researcher

Bayesian Deep Learning (Helmholtz AI)

Link to Georgios Kaissis

Georgios Kaissis


Affiliated Senior Researcher

Privacy-Preserving and Trustworthy AI (TUM)

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.

Link to Ulrich Bauer

Ulrich Bauer

Prof. Dr.

Applied Topology and Geometry (TUM)

Link to Massimo Fornasier

Massimo Fornasier

Prof. Dr.

Applied Numerical Analysis (TUM)

Link to Reinhard Heckel

Reinhard Heckel

Prof. Dr.

Machine Learning (TUM)

Link to Felix Krahmer

Felix Krahmer

Prof. Dr.

Optimization & Data Analysis (TUM)

Link to Gitta Kutyniok

Gitta Kutyniok

Prof. Dr.

Mathematical Foundations of Artificial Intelligence (LMU)

Link to Holger Rauhut

Holger Rauhut

Prof. Dr.

Mathematical Data Science and Artificial Intelligence (LMU)

Link to Suvrit Sra

Suvrit Sra

Prof. Dr.

Resource Aware Machine Learning (TUM)

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.

Link to Stephan Günnemann

Stephan Günnemann

Prof. Dr.

Data Analytics & Machine Learning (TUM)

Link to Eyke Hüllermeier

Eyke Hüllermeier

Prof. Dr.

Artificial Intelligence & Machine Learning (LMU)

Link to Stefanie Jegelka

Stefanie Jegelka

Prof. Dr.

Foundations of Deep Neural Networks (TUM)

Link to Niki Kilbertus

Niki Kilbertus

Prof. Dr.

Ethics in Systems Design and Machine Learning (TUM)

Link to Matthias Schubert

Matthias Schubert

Prof. Dr.

Database Systems & Data Mining (LMU)

Link to Thomas Seidl

Thomas Seidl

Prof. Dr.

Database Systems & Data Mining (LMU)

Link to Volker Tresp

Volker Tresp

Prof. Dr.

Database Systems & Data Mining (LMU)

Link to Steffen Schneider

Steffen Schneider

Affiliated Senior Researcher

Dynamical Inference (Helmholtz AI)

B | Perception, Vision, and Natural Language Processing

forms a dynamic research domain at the intersection of computer science and cognitive sciences. This field explores the synergies between diverse sensory inputs, visual information processing, and language understanding.

C | Domain-specific Machine Learning

shows an immense potential, as both universities have several highly visible scientific domains with internationally renowned experts. This area facilitates translating ML concepts and technologies to many different domains.


Check out the publications by our members.