Thomas Gärtner

TU Wien

Names

How do you usually write your name as author of a paper? Also add any other names you have authored papers under.

Thomas Gärtner (Preferred)
,
Thomas E Gartner

Emails

Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments.

****@ais.fraunhofer.de
,
****@nottingham.ac.uk
,
****@tuwien.ac.at

Education & Career History

Enter your education and career history. The institution domain is used for conflict of interest detection and institution ranking. For ongoing positions, leave the end field blank.

Full Professor
TU Wien (tuwien.ac.at)
2019Present
 
Full Professor
University of Nottingham (nottingham.co.uk)
20152019
 
Postdoc
University of Bonn (uni-bonn.de)
20052015
 
Postdoc
Fraunhofer Institute IAIS, Fraunhofer IAIS (iais.fraunhofer.de)
20052015
 

Advisors, Relations & Conflicts

Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.

PhD Advisee
Shankar Vembu
****@gmail.com
Present
 
PhD Advisee
Olana Missura
****@gmail.com
Present
 
PhD Advisee
Daniel Paurat
****@gmail.com
Present
 
PhD Advisee
Dino Oglic
****@kcl.ac.uk
Present
 
PhD Advisee
Michael Kamp
****@monash.edu
Present
 
Coworker
Nicolo' Navarin
****@unipd.it
Present
 
Postdoc Advisee
Anna Sepliarskaia
****@tuwien.ac.at
Present
 
PhD Advisee
Joe Redshaw
****@redshaw.org.uk
Present
 
PhD Advisee
David Penz
****@tuwien.ac.at
Present
 
PhD Advisee
Maximilian Thiessen
****@tuwien.ac.at
Present
 
PhD Advisee
Katrin Ullrich
****@iwu.fraunhofer.de
Present
 
PhD Advisor
Stefan Wrobel
****@cs.uni-bonn.de
20002005
 

Expertise

For each line, enter comma-separated keyphrases representing an intersection of your interests. Think of each line as a query for papers in which you would have expertise and interest. For example: deep learning, RNNs, dependency parsing

kernel methods
Present
 
machine learning
Present
 
learning with structured data
Present