Prof. Dr. Ulrike von Luxburg

I am a professor for computer science, with research focus on the theory of machine learning. And I am looking for a postdoc who wants to work on the theory of explainable ML. If you are interested, apply as described here.

Quick links:   Publications, my free online courses on Statistical Machine Learning, Mathematics for Machine Learning, Theoretische Informatik (in German).     and many more lectures by our colleagues on our channel Youtube Tübingen machine learning    

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).

We are still looking for a postdoc on any topic that is related to our work on learning theory, see here.

Teaching: My lectures are on youtube, as well as the ones of several other colleagues. See here 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 (my favorite one: KI, Wissenschaft, Gesellschaft und Verantwortung: Ein interaktives Seminar zur Meinungsbildung). 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). Upcoming:

• 24.1.2022 KI und Journalismus
• 23.3.2022: Kaminabend der Jungen Akademie
• 4.4.2022: Panel discussion: AI and democracy.

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.