Machine Learning Research Unit

Welcome to the web-home of the new Machine Learning research unit of TU Wien Informatics.


  Machine Learning Research Unit (E 194-06)

Information Systems Engineering Institute
Faculty of Informatics
TU Wien

Physical: Erzherzog-Johann-Platz 1 (FB02), 1040 Vienna, Austria


  1. Stefan Neumann received funding for a Vienna Research Group and will join our research unit in 2024. (October'23)
  2. Our paper Expressivity-Preserving GNN Simulation was accepted at NeurIPS'23. (October'23)
  3. We won the best poster award at G-Research's ICML poster party in London where our very own Max Thiessen presented Expectation-Complete Graph Representations with Homomorphisms.
  4. We hired two amazing new PostDocs! Dr. Pascal Welke on our StruDL project and Dr. Sagar Malhotra . Welcome to the team! (May '23)
  5. Our paper Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions has been accepted at IJCAI'23. It has also been accepted for spotlight presentation at the NeSy'23 workshop. (May '23)
  6. Our paper An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning got accepted at ECMLPKDD'23. (May '23)
  7. Two accepted papers at ACL'23! Hidden Schema Networks and A New Aligned Simple German Corpus. (May '23)
  8. Our paper on Expectation-Complete Graph Representations with Homomorphisms has been accepted at ICML'23! (April '23)
  9. Our paper Can stochastic weight averaging improve generalization in private learning? has been accepted at the RTML workshop of ICLR'23! (April '23)
  10. We are organising the Mining and Learning with Graphs (MLG) workshop this year at ECMLPKDD2023. (April '23)
  11. We organised the 1st community event for students of learning algorithms in Vienna (C'est La Wien, Feb '23)
  12. Caterina Graziani (Università degli Studi di Siena) is visiting us for four months! (Feb '23)
  13. Our paper on Krein support vector machine classification of antimicrobial peptides has been accepted for publication in the Digital Discovery journal! Joint work of Joseph Redshaw, Darren S. J. Ting, Alex Brown, Jonathan D. Hirst, and Thomas Gärtner . (Feb '23)
  14. Two of our papers have been accepted at NeurIPS'22! (Oct '22)
  15. Our very own Fabian Jogl won the (community voted) Best Poster Award at the MLG@ECMLPKDD Workshop. Congratulations! (Sep '22)
  16. We organised machine learning courses for children at the KinderUni Wien. (Jul '22)
  17. Together with Pascal Welke from the University of Bonn, we are organising the Mining and Learning with Graphs (MLG) workshop this year at ECMLPKDD'22. Submit your work until June 20th!
  18. Our very own Tamara Drucks won the Best Poster Award of TU Wien Informatics. Congratulations! (Jan '22)
  19. Our paper on Kernel Methods for Predicting Yields of Chemical Reactions has been accepted for publication in the Journal of Chemical Information and Modeling. Joint work of Alexe Haywood, Joseph Redshaw, Magnus Hanson-Heine, Adam Taylor, Alex Brown, Andrew Mason, Thomas Gärtner , and Jonathan Hirst. (Oct '21)
  20. Our paper on Conditional Network Data Balancing With GANs has been accepted at the NeurIPS Deep Generative Models and Downstream Applications Workshop. Joint work ok Fares Meghdouri, Thomas Schmied, Tanja Zseby, and Thomas Gärtner . (Oct '21)
  21. Our paper on Active Learning Convex Halfspaces on Graphs (supplementary material) has been accepted at NeurIPS'21. Congratulations to Maximilian Thiessen for his first NeurIPS paper! (Sept '21)
  22. Our team won the prize for the Best Practical Result in the Siemens AI-Dependability Assessment Challenge. Congratulations to Joseph Redshaw (University of Nottingham), Maximilian Thiessen (TU Wien), and David Penz (TU Wien)! (July '21)
  23. Our very own David Penz won the Distinguished Young Alumn award of TU Wien Informatics. Congratulations! (June '21)
  24. FFG has aggreed to fund our proposal on Artificial Intelligence for Advanced SAR Processing! (May '21)
  25. TU Wien has agreed to fund our doctoral college on Secure and Intelligent Human-Centric Digital Technologies ! (July '20)
  26. Founding of the Machine Learning research unit. (Feb '20)
  27. BBSRC has aggreed to fund our proposal on Bacteriophage to control Salmonella in pigs! (Oct '19)


Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning. Research questions concern for instance: To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.