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

Prof. Dr. Michael Ingrisch

LMU München

Clinical Data Science in Radiology

Link to Nassir Navab

Prof. Dr. Nassir Navab

TU München

Computer Aided Medical Procedures & Augmented Reality

Link to Daniel Rückert

Prof. Dr. Daniel Rückert

TU München

Artificial Intelligence in Healthcare and Medicine

Link to Julia Schnabel

Prof. Dr. Julia Schnabel

TU München

Computational Imaging and AI in Medicine

Link to Christian Wachinger

Prof. Dr. Christian Wachinger

TU München

Artificial Intelligence in Radiology

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

Prof. Dr. Julien Gagneur

TU München

Computational Molecular Medicine

Link to Christian Müller

Prof. Dr. Christian Müller

LMU München

Biomedical Statistics and Data Science

Link to Fabian Theis

Prof. Dr. Fabian Theis

TU München

Mathematical Modelling of Biological Systems

Link to Ralf Zimmer

Prof. Dr. Ralf Zimmer

LMU München


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

Prof. Dr. Xiaoxiang Zhu

TU München

Data Science in Earth Observation

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

Prof. Dr. Stefan Feuerriegel

LMU München

Artificial Intelligence in Management

Link to Frauke Kreuter

Prof. Dr. Frauke Kreuter

LMU München

Statistics and Data Science in Social Sciences and the Humanities

Link to Helmut Küchenhoff

Prof. Dr. Helmut Küchenhoff

LMU München

Statistical Consulting Unit (StaBLab)

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

Prof. Dr. Alena Buyx

TU München

Ethics in Medicine and Health Technologies

Link to Sven Nyholm

Prof. Dr. Sven Nyholm

LMU München

Ethics of Artificial Intelligence

Link to Albrecht Schmidt

Prof. Dr. Albrecht Schmidt

LMU München

Human-Centered Ubiquitous Media

Link to Ben Lange

Dr. Ben Lange

Senior Researcher

LMU München

JRG Leader AI in Ethics

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.


Check out the publications by our members.