Sicco Verwer

Sicco Verwer is an Associate Professor in machine learning with applications in cyber security and software engineering at TU Delft since 2014. Before this, he has been a postdoctoral researcher for several years at RU Nijmegen, KU Leuven, and TU Eindhoven.
He has worked on several topics in machine learning and is best known for his work in grammatical inference, i.e., learning state machines from trace data. He has researched and implemented several algorithms for learning such models including RTI, which is one of the first that is able to learn timed automata. In 2013, he received a VENI grant from STW to extend this work and apply it in cyber security. Other recent work include several methods for declarative modelling of machine learning problems using mathematical solvers, and making classifiers discrimination-aware.
He teaches two courses in the cyber security master at TU Delft: Cyber Data Analytics and Automated Software Testing and Reverse Engineering.
If you are interested in the research performed by his lab, or joining as PhD or MSc student, please have a look at Sicco's publications and past publicly available MSc and BSc theses.

  1. SE Verwer, MM de Weerdt, C Witteveen, Timed automata for behavorial pattern recognition, In BNAIC 2005 - Proceedings of the seventeenth Belgium-Netherlands conference on artificial intelligence p.291-296, KVAB.
  2. SE Verwer, MM de Weerdt, C Witteveen, Learning Driving Behavior By Timed Syntactic Pattern Recognition, In Proceedings of the International Joint Conference on Artificial Intelligence p.1529-1534, American Association for Artificial Intelligence (AAAI).
  3. SE Verwer, MM de Weerdt, C Witteveen, Efficiently learning simple timed automata, In Induction of Process Models (IPM 2008) p.61-68, University of Antwerp.
  4. SE Verwer, MM de Weerdt, C Witteveen, Polynomial distinguishability of timed automata, In ICGI p.238-251, Springer.
  5. SE Verwer, MM de Weerdt, C Witteveen, One-Clock Deterministic Timed Automata Are Efficiently Identifiable in the Limit, In Third International Conference, LATA 2009, Tarragona, Spain, April 2-8, 2009. Proceedings p.740-751, Springer.
  6. SE Verwer, MM de Weerdt, C Witteveen, A likelihood-ratio test for identifying probabilistic deterministic real-time automata from positive data, In Grammatical Inference: Theoretical Results and Applications p.203-216, Springer.
  7. CC Florêncio, SE Verwer, Regular inference as vertex coloring, In Theoretical Computer Science Volume 558 p.18-34.
  8. SE Verwer, Efficient Identification of Timed Automata: Theory and Practice
  9. G. Pellegrino, Learning Automata for Network Behaviour Analysis
  10. F Aarts, H Kuppens, J Tretmans, FW Vaandrager, SE Verwer, Improving active Mealy machine learning for protocol conformance testing, In Machine Learning Volume 96 p.189-224.

Dr. S.E. Verwer