Univ.Prof. Dipl.-Ing.(BA) Dr.rer.nat. / MSc
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 regularites 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 main research interests are efficient and effective machine learning and data mining algorithms. 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 such as 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.
- Machine Learning / VU / 184.702
- Machine Learning Algorithms and Applications / PR / 194.101
- Scientific Research and Writing / SE / 193.052
- Seminar on Theoretical Aspects of Machine Learning Algorithms / SE / 194.102
- Theoretical Foundations and Research Topics in Machine Learning / VU / 194.100
- Machine Learning for Chemical Synthesis / T. Gärtner, A. Haywood, J. Redshaw, A. Taylor, A. Mason, J. Hirst / The Royal Society of Chemistry, London, 2020, ISBN: 978-1-78801-789-3; 25 pages