TU Wien Informatics


Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning.

Research questions concern for instance:

  • learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space
  • incorporation of invariances as well as expert domain knowledge in learning algorithms
  • computational, sample, query, and communication complexity of learning algorithms
  • constructive machine learning scenarios such as structured output prediction
  • learning with small labelled data sets and large unlabelled data sets
  • adverserial learning with mistake and/or regret bounds
  • parallelisation/distribution of learning algorithms
  • approximation of learning algorithms
  • scalability of learning algorithms
  • reliability of learning algorithms
  • extreme learning

To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.

The research Unit Machine Learning is part of the Institute of Information Systems Engineering.

Thomas Gärtner
Thomas Gärtner T. Gärtner

Head of Research Unit
Univ.Prof. DI(BA) Dr. / MSc

Tamara Drucks
Tamara Drucks T. Drucks

PreDoc Researcher

Patrick Indri
Patrick Indri P. Indri

PreDoc Researcher

David Penz
David Penz D. Penz

PreDoc Researcher

Maximilian Thiessen
Maximilian Thiessen M. Thiessen

PreDoc Researcher

Rudolf Mayer
Rudolf Mayer R. Mayer

Mag. DI

Soon, this page will include additional information such as reference projects, conferences, events, and other research activities.

Until then, please visit Machine Learning’s research profile in TISS .