Thomas Gärtner has been Professor of Machine Learning at TU Wien since October 2019. From July 2015 to September 2019 he was Professor of Data Science at the University of Nottingham. Before that, he was leading a research group jointly hosted by the University of Bonn and the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS. During this time he received an award in the prestigious Emmy-Noether programme of the DFG. His main research interest is on efficient and effective machine learning and data mining algorithms. His work spans theoretical aspects and practical algorithms with real-world applications. He is an editor of the Machine Learning journal and regularly serves in senior academic roles for international flagship conferences on machine learning. He co-organised the leading European conference on machine learning and data mining (ECMLPKDD) in various roles and was a member of its steering board.
Prizes, awards, memberships
Member of the steering board of ML2R
Member of the Ellis society
Emmy Noether Award’10
Best paper award ECMLPKDD’06
Student best algorithmic paper award, ILP’03
My main research interests are efficient and effective machine learning and data mining algorithms. Machine learning considers the problem of extracting useful functional or probabilistic dependencies from a sample of data. Such dependencies can then, for instance, be used to predict properties of partially observed data. Data mining is often used in a broader sense and includes several different computational problems, for instance, finding regularities or patterns in data. By efficiency I mean on the one hand the classical computational complexity of decision, enumeration, etc. problems but on the other hand also a satisfactory response time that allows for effectiveness. By effectiveness I mean how well an algorithm helps to solve a real world problem. My recent focus is on challenges relevant to the constructive machine learning setting where the task is to find domain instances with desired properties and the mapping between instances and their properties is only partially accessible. This includes structured output prediction, active learning/search, online learning/optimisation, knowledge-based learning and related areas. I am most interested in cases of this setting where at least one of the involved spaces is not a Euclidean space but for instance the set of graphs. My approach in many cases is based on kernel methods where I have focussed originally on kernels for structured data, moved to semi-supervised / transductive learning, and am currently looking at parallel/distributed approaches as well as fast approximations. The most recent knowledge-based kernel method was for instance focussing on interactive visualisations for data exploration. Application areas which I am often considering when looking for novel machine learning challenges are chemoinformatics and computer games.