Research interests

My main interests are machine learning and data science, including theoretical research and various applications in the life sciences. I am a member of the research unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling.

At this moment specific interests are deep learning, multi-target prediction, uncertainty quantification, sequence learning and time series analysis. I am also interested in applications that have a positive impact on our society, with a focus on domains such as molecular biology, medicine, environmental sciences, and climate analysis. In the last ten years I have been mainly active in the field of multi-target prediction. About this topic I have organized tutorials at ICML 2013 and ECML/PKDD 2018, and workshops at ECML/PKDD 2014 and ECML/PKDD 2015.

Publications

You can find an overview of my publications on Google Scholar with this link. The full text of most publications is available via this link.

Current PhD students

Thomas Mortier, Dimitris Iliadis, Nicolas Dewolf, Gaetan De Waele, Jelle Hustinx, Lauren Theunissen, Laure Van Den Bulcke, Friederike Mey

Former PhD students

Jim Clauwaert, Peter Rubbens, Christina Papagiannopoulou, Michiel Stock

Awards

The long-standing cooperation between members of KERMIT and members of the Department of Information Technology and the Turku Centre for Computer Science (University of Turku, Finland) has resulted in the 2015 IEEE Computational Intelligence Society Outstanding TFS Paper Award for their contribution "A kernel-based framework for learning graded relations from data" (W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets) which has appeared in the December 2012 issue of IEEE Transactions on Fuzzy Systems.

The Best Paper Award was awarded for the contribution An analysis of chaining in multi-label classification (K. Dembczynski, W. Waegeman and E. Huellermeier) at the European Conference on Artificial Intelligence (Montpellier, France, August 2012).

Our team has won the second price in the JRS 2012 Data Mining Competition on topical classification of biomedical research articles (W. Cheng, K. Dembczynski, E. Huellermeier, A. Jaroszewicz and W. Waegeman). The submission was based on our F-measure maximizing algorithm, which was published at NIPS 2011.