Research unit profile

TU-Wien > Institute of Information Systems Engineering >Faculty of Informatics >Institute of Information Systems Engineering >Research Unit of Machine Learning

Concept (German) 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.

Concept (English) 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.

Website https://www.ml.tuwien.ac.at/

People

Title	Surname	First name
Associate Prof. Dr.rer.nat.	Andergassen	Sabine
Projektass. MSc	Baraah Adil Mohammed Sidahme	Baraah Adil Mohammed Sidahme
Assistant Prof.	Bellec	Guillaume
Projektass.(FWF) Dipl.-Ing. BSc MSc	Bernhart	Matthias
Projektass.(FWF) Dipl.-Ing. BSc	Blohm	Peter
Assistant Prof. PhD	Chavdarova	Tatjana
Univ.Ass. MSc	De Santis	Francesco Flaviano
Univ.Ass. MSc	Domizio	Francesco
Projektass. Dipl.-Ing.in	Drucks	Tamara
BSc	Haberger	David
BSc	Haberl	Alexander
Projektass.(FWF) MSc	Indri	Patrick
	Kacmaz	Ülkühan
Projektass.(FWF) MSc	Khodadadian	Ehsan
Projektass.(FWF) Dr.rer.nat. MSc	Khodadadian	Amirreza
Projektass.(FWF) MSc	Krämer	Marcel Nico
Univ.Ass. MSc	Ludwig	Harald Michael
Projektass. MSc	Lüderssen	Sebastian Johannes
Univ.Ass. MSc PhD	Malhotra	Sagar
Projektass. MSc	Marty	Laurine Marie Alena
Assistant Prof. Dr.techn. BSc MSc	Neumann	Stefan
Univ.Ass.in Dott.ssa mag.	Patricolo	Miriam
Projektass.(FWF) Dipl.-Ing. B.A.	Penz	David
Univ.Ass. MSc	Sandrock	Christoph
Projektass. MSc	Singh	Ashwin

Area of expertise according to Statistik Austria

Code	Area of expertise (German)	Area of expertise (English)
1122	Artificial Intelligence	Artificial intelligence

Keywords

Keyword (German)	Keyword (English)
Maschinelles Lernen	Machine Learning
Data Mining	Data Mining