Assistant Professor

Wouter Duivesteijn

I am incurably curious!

Group / Unit
Information Systems WSK&I
Floor / room

Research Profile

Wouter Duivesteijn is an Assistant Professor in Data Mining at the Technische Universiteit Eindhoven. Currently, his research interests revolve around SD and EMM in a wider scope. He is interested in combining concepts from Subgroup Discovery and ROC Analysis as well as SD and EMM on streams. Both SD and EMM attempt to find small portions of the data where the observed behaviour is notably different from that of the database as a whole. Initially this can take the relatively simple form of extracting a meaningful flat table representation of features from the stream, and observing the results that can be obtained by running out-of-the-box SD and EMM algorithms on that table. Wouter aims to eventually develop a full-fledged algorithm that allows to mine for interesting subgroups directly on the data stream.

Academic Background

Wouter Duivesteijn obtained his PhD in Computer Science from Leiden University. He also holds MScs in Applied Computing Science and Mathematical Sciences from Utrecht University. Before joining TU/e, Wouter worked on the FORSIED project (FORmalising Subjective Interestingness in Exploratory Data mining) at the University of Ghent and the University of Bristol to. Before that, he worked as a Wissenschaftlicher Mitarbeiter at the Collaborative Research Center SFB 876 at the Technische Universität Dortmund and at the Data Mining group of LIACS, Leiden University. Wouter is chairman of the board of student association A–Eskwadraat for Mathematics, Physics, Computer Science and Information Science, as well as a member of the science faculty council, mathematics department council, mathematics department board of education and mathematics education council at Utrecht University. He has acted as Conference chair of Benelearn 2017 and workshop chair of Silver 2012, collocated with ECML PKDD in Bristol, UK. He has been a program committee member of a wide range of events and reviewed for numerous leading journals.

Educational Activities

  • Web analytics
  • Foundations of data mining
  • Data analytics for engineers

Ancillary Activities

No ancillary activities