Unit Tübingen

The ELLIS Unit Tübingen is implemented through the Tübingen AI Center, which brings together leading researchers in core machine learning, applications, and societal impact from the University of Tübingen and the Max Planck Institute for Intelligent Systems, forming one of Europe’s leading AI research clusters. The center closely collaborates with the ELLIS Institute through funded co-appointments for its principal investigators and supports the ELLIS Society through several coordination positions. Permanently funded as one of Germany’s six national AI centers, it serves as a hub for internationally competitive AI research. It enjoys faculty-level status at the University of Tübingen—including the ability to establish its own professorships—while maintaining a lean structure designed to attract global talent and advance cutting-edge AI research.

• Foundations of Machine Learning and Robust Intelligence
  Developing principled methods for representation learning, compositional generalization, causal modeling, probabilistic inference, and other theoretical foundations of modern machine learning, including approaches to robust and reliable AI.
• Open and Efficient Foundation Models
  Participation in open-source initiatives to develop reproducible large language and multimodal foundation models using permissive datasets, transparent training pipelines, and standardized evaluation frameworks.
• Autonomous Perception and Decision Making
  Machine learning for autonomous systems, including computer vision, robotics, and self-driving vehicles, enabling robust perception and decision-making in complex real-world environments.
• Safety, Alignment, and Trustworthy AI
  Developing trustworthy AI systems through interpretability, uncertainty quantification, robustness, and methods for aligning advanced AI systems with human goals and societal values.
• AI for Education and Societal Impact
  Development of AI tutoring systems and digital learning assistants to support personalized education and expand access to high-quality AI-assisted learning.
• Scientific Discovery with AI
  AI methods that accelerate scientific progress in fields such as biology, neuroscience, physics, climate science, and medicine through data-driven modeling, simulation, and automated hypothesis generation.
• AI for Medicine and the Life Sciences
  Machine learning approaches for biomedical imaging, computational biology, neuroscience, and personalized medicine.
• Algorithms, Law, and Society
  Studying the societal, ethical, and legal implications of AI systems and developing frameworks for responsible AI development and deployment.