Robert Bamler

  Data Science and Machine Learning

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 

  Machine Learning in Science

Our goal is to accelerate scientific discovery using machine learning and artificial intelligence (AI): We develop computational methods that help scientists interpret empirical data and use them gain scientific insights.

Zeynep Akata

Wolfgang Spohn

  Philosophy of Science

Wolfgang Spohn's areas of competence are epistemiology and philosophy of science, with a special focus on logic.

Manfred Claassen

  Clinical Bioinformatics, Medical Faculty, University of Tübingen

Manfred Claassen uses machine learning for single-cell biology in health and disease.

Stephan Eckstein

  Mathematical Methods in Computer Science, Department of Mathematics, University of Tübingen

Stephan Eckstein works at the intersection of probability theory and machine learning, with a particular focus on statistical distances and efficient computational methods.

Michèle Finck

  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.

Gerard Pons-Moll

  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.

Bob Williamson

  Foundations of Machine Learning Systems, Department of Computer Science, University of Tübingen

Bob Williamson is interested in understanding and designing machine learning systems as a whole. To that end, he is pursuing theoretical questions regarding machine learning problems and how they relate to each other.