Zeynep Akata (since October 2019)

 Explainable Machine Learning

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.

Jakob Macke (since May 2020)

  Machine Learning in Science

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.

Robert Bamler (since November 2020)

  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.

Wolfgang Spohn (since January 2019)

  Philosophy of Science

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

Bob Williamson (starting March 2021)

  Foundations of Machine Learning Systems

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.

Manfred Claassen (since January 2020)

  Clinical Bioinformatics at the Medical Faculty of the University of Tübingen

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

Setareh Maghsudi (since October 2020)

  Decision Making  at the Department of Computer Science at the 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.

Peter Ochs (since September 2020)

  Mathematical Optimization Group at the Department of Mathematics at the 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.