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Machine Learning E194-06

  • A Research Unit of The Institute of Information Systems Engineering

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

Research Unit Machine Learning

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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.

Sabine Andergassen
Sabine Andergassen S. Andergassen

Assistant Professor
Asst.Prof. Dr.

Thomas Gärtner
Thomas Gärtner T. Gärtner

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

Tamara Drucks
Tamara Drucks T. Drucks

PreDoc Researcher
DI

Patrick Indri
Patrick Indri P. Indri

PreDoc Researcher
MSc

Anice Jahanjoo
Anice Jahanjoo A. Jahanjoo

PreDoc Researcher

Fabian Jogl
Fabian Jogl F. Jogl

PreDoc Researcher
DI / BSc

David Penz
David Penz D. Penz

PreDoc Researcher
DI / BA

Maximilian Thiessen
Maximilian Thiessen M. Thiessen

PreDoc Researcher
MSc

Ülkühan Kacmaz
Ülkühan Kacmaz UE. Kacmaz

Office Services

Martina Trauner
Martina Trauner M. Trauner

IT Services

Leonard Galustian
Leonard Galustian L. Galustian

Student Staff
BSc

Rudolf Mayer
Rudolf Mayer R. Mayer

Lecturer
Mag. DI

2022W

2023S

 

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once APIs for the new database have been released, everything will be up to date again.

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 .