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



Web: https://ml-tuw.github.io/
Physical: Gußhausstraße 27-29 (CA03), 1040 Vienna, Austria

News

  1. 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)
  2. 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)
  3. Our paper on Active Learning Convex Halfspaces on Graphs (supplementary material) has been accepted at NeurIPS 2021. Congratulations to Maximilian Thiessen for his first NeurIPS paper! (Sept '21)
  4. 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)
  5. Our very own David Penz won the Distinguished Young Alumn award of TU Wien Informatics. Congratulations! (June '21)
  6. FFG has aggreed to fund our proposal on Artificial Intelligence for Advanced SAR Processing! (May '21)
  7. TU Wien has agreed to fund our doctoral college on Secure and Intelligent Human-Centric Digital Technologies ! (July '20)
  8. Founding of the Machine Learning research unit. (Feb '20)
  9. BBSRC has aggreed to fund our proposal on Bacteriphage to control Salmonella in pigs! (October '19)

Topics

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.