Zeynep Akata is interested in machine learning, which combines vision and language in the field of Explainable Artificial Intelligence (XAI): (1) How can deep learning methods be trained effectively when no, or only limited amounts of data are available. (2) How to explain decisions of AI systems in a way that makes them understandable to users without expert knowledge.
Robert Bamler develops approximate algorithms that scale up Bayesian inference to large data sets and powerful probabilistic models. His research provides new tools for natural scientists, highly effective codecs for data compression, and the foundations for a new kind of equitable machine learning in decentralized networks.
Jakob Macke’s research goal is to accelerate scientific discovery using machine learning and artificial intelligence: He wants to develop computational methods that help scientists interpret empirical data and use them to discover and constrain theoretical models.
I am interested in understanding and designing machine learning systems as a whole. To that end I am pursuing theoretical questions regarding machine learning problems and how they relate to each other.
Senior Professorship established by the Cluster
Wolfgang Spohn's areas of competence are epistemiology and philosophy of science, with a special focus on logic.
Professorships supported by the Cluster
Clinical Bioinformatics, Medical Faculty, University of Tübingen
Manfred Claassen uses machine learning for single-cell biology in health and disease.
Law and Artificial Intelligence, Department of Law, University of Tübingen
Michèle Finck's research focuses on law and artificial intelligence with a particular emphasis on data (protection) law and governance.
Decision Making, Department of Computer Science, University of Tübingen
Setareh Maghsudi's research focuses on developing decision-making strategies under uncertainty, conflict, and communications constraints, with future-looking applications such as the Internet of Things.
Mathematical Optimization Group, Department of Mathematics, University of Tübingen
The goal of the Mathematical Optimization Group is the development and analysis of efficient algorithms for non-smooth optimization problems, which are motivated by applications in Image Processing, Computer Vision, Machine Learning and Statistics.
Continuous Learning of Multimodal Data Streams, Department of Computer Science, University of Tübingen
Gerard Pons-Moll's research lies at the intersection of Machine Learning, Computer Vision and Computer Graphics. His goal is to build digital humans that look and behave like real ones.