Prof. Dr. Ulrike von Luxburg

Ulrike von Luxburg


University of Tübingen
Department of Computer Science
Maria von Linden Str. 6
72076 Tübingen
Germany

Room: 30-5/A24
Phone: +49 (0)7071 29-70832
E-mail: ulrike.luxburg(at)uni-tuebingen.de

I am a professor for computer science, with research focus on the theory of machine learning.


Quick links:  


Research. My research focus is on theoretical questions about unsupervised machine learning: understanding implicit biases and assumptions of machine learning algorithms, giving formal guarantees to some algorithms, and proving how other algorithms systematically fail. In particular, we currently ask all these questions in the context of explainable machine learning.   Publications     Our research seminar     Research questions    


I am coordinating the research cluster Machine learning: New Perspectives for Science (jointly with Philipp Berens), and the CZS Institute for AI and Law (together with Michele Finck and Stefan Thomas).


Teaching See our teaching page for links to lectures, topics for Bachelor / Master theses, comments about taking exams, etc.


Public AI discussion. In the city of Tübingen, and also in the wider context of Germany, there is an ongoing discussion about research in artificial intelligence and its impact on future society. I find this discussion important and actively participate(d) in quite a number of past events. Most notably, our AI exhibition in the City Museum. Created jointly by 36 master students of anthropology and machine learning, Thomas Thiemeyer and Tim Schaffarczik (anthropology), Guido Szymanska and Wiebke Ratzeburg (museum) and myself, running from Feb 2023 - Jan 2024.

exhibition image exhibition image exhibition image

Other than that, consider watching my lectures on ML and society, or the german lecture Wie funktioniert maschinelles lernen, or reading the corresponding text Wie funktioniert maschinelles lernen (pdf).. Or watch my Kinderuni lecture on youtube: ``Warum ist künstlich Intelligenz nicht immer gerecht?'' (Why is AI not always fair?)

Short CV, awards, community service: see here

Funding and transparency: see here.

Code and data sets : see here.

Job applications (interns, PhD students, Postdocs): see here.