Kurt Driessens (K.)
Kurt Driessens' main research focus lies in the field of Machine Learning. He started his academic career working on reinforcement learning, focussing on representational issues. Since then, he broadened his scope to general machine learning techniques and is currently highly interested in the automatic discovery of data representations and data set relations.
Expertises
- Data Science
- Machine Learning
- Reinforcement Learning
- Relational/Logical Learning
- Transfer Learning
- Data Representation
Career history
Kurt received his degree in Engineering with a major in Computer Science from the Katholieke Universiteit Leuven (Belgium) in 1997 and his Ph.D. in Applied Sciences from the same university in May, 2004. During this time and since then he has been lucky enough to be a visiting lecturer at the University of Zambia for the course 'Machine Architecture & Operating Systems' (in March 1999 and November 2001), a visiting researcher at the Institut Jozef Stefan, Ljubljana, Slovenia (July 2004 - September 2004), a post-doctoral researcher at the University of Waikato, Hamilton, New Zealand (October 2004 - September 2005) and a post doctoral research fellow of the FWO (Research Foundation - Flanders) (October 2007 - October 2011). At the K.U.Leuven, he was also a board member of the Faculty of Applied Sciences magazine: GeniaaL (in Dutch).
He is a member of the editorial board of the Machine Learning Journal and the Journal of Artificial Intelligence Research. From February 2011, he joined the department of Data Science and Knowledge Engineering at Maastricht University. Between January 2014 and December 2016, he was Director of Studies for the Bachelor on Knowledge Engineering, the Master on Artificial Intelligence and the Master on Operations Research. He was responsible for the move towards a Data Science for Decision Making master to replace this last one.
He was a core team member for the development of the Bachelor Computer Science that started in 2023.
Recently he has taken over coordination of the KE@Work honours programme and started a similar programme called CS@Work in the before-mentioned bachelor.