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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.

C1 | Medicine

The research at MCML in Medicine and Healthcare focus on objectives that are necessary to overcome the hurdles for the deployment of ML approaches in clinical environments. In particular, advances are required in interpretable and explainable deep learning, robust and data efficient learning, privacy preserving learning, as well as in trust and safety of autonomous AI and ML systems.

Link to Michael Ingrisch

Michael Ingrisch

Prof. Dr.

Clinical Data Science in Radiology

Link to Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality

Link to Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Julia Schnabel

Julia Schnabel

Prof. Dr.

Computational Imaging and AI in Medicine

Link to Christian Wachinger

Christian Wachinger

Prof. Dr.

Artificial Intelligence in Radiology

Link to Peter Schüffler

Peter Schüffler

Prof. Dr.

Associate Researcher

Computational Pathology

C2 | Biology

MCML focuses on crucial issues in Biology and Biomedicine, addressing AI challenges such as liability, black-box behavior, and privacy. The goals include advancing personalized healthcare and fostering collaboration between algorithms and human experts. Additionally, MCML aims to be a key training hub for the next generation of AI-empowered professionals in medical and biological fields.

Link to Julien Gagneur

Julien Gagneur

Prof. Dr.

Computational Molecular Medicine

Link to Christian Müller

Christian Müller

Prof. Dr.

Biomedical Statistics and Data Science

Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems

Link to Ralf Zimmer

Ralf Zimmer

Prof. Dr.


C3 | Physics and Geo Sciences

Geoinformation from Earth Observation satellite data is vital for addressing societal challenges. The research focus at MCML in this area is on tailoring data science and ML for geo-relevant applications. This includes physics-aware ML, uncertainty quantification, explainable geoinformation retrieval, Quantum ML for a digital twin of the Earth, and ethical considerations in ML for Earth Observation.

Link to Xiaoxiang Zhu

Xiaoxiang Zhu

Prof. Dr.

Data Science in Earth Observation

Link to Daniel Grün

Daniel Grün

Prof. Dr.

Associate Researcher

Astrophysics, Cosmology and Artificial Intelligence

C4 | Computational Social Sciences

The landscape for empirical social scientists has transformed with the rise of computational social science. Our researchers focus on aligning research goals with available digital trace data, evaluating data quality in relation to research objectives, and ensuring reproducibility through thorough documentation. They emphasize the critical need to assess and evaluate data feeding into AI systems to prevent biases, unfair operations, and the exacerbation of social inequalities.

Link to Stefan Feuerriegel

Stefan Feuerriegel

Prof. Dr.

Artificial Intelligence in Management

Link to Frauke Kreuter

Frauke Kreuter

Prof. Dr.

Social Data Science and AI Lab

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

Link to Christoph Kern

Christoph Kern

Prof. Dr.

Associate Researcher

Social Data Science and AI Lab

C5 | Humane AI

MCML emphasizes ML and data science research for human benefit, improving actions, automating tasks, and revealing insights. Basic ML research, though generic, offers wide applicability. In human-centered ML, we prioritize efficient human-algorithm-data interaction, expanding beyond traditional human-computer interaction to include intelligent systems and data, all within a framework of ethical considerations in AI development and deployment.

Link to Alena Buyx

Alena Buyx

Prof. Dr.

Ethics in Medicine and Health Technologies

Link to Sven Nyholm

Sven Nyholm

Prof. Dr.

Ethics of Artificial Intelligence

Link to Albrecht Schmidt

Albrecht Schmidt

Prof. Dr.

Human-Centered Ubiquitous Media

Link to Ben Lange

Ben Lange


JRG Leader

JRG Leader Ethics of AI

Link to Sven Mayer

Sven Mayer

Prof. Dr.

Associate Researcher

Human-Computer Interaction and Artificial Intelligence

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.

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.


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