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. A Labarre, SE Verwer, Merging partially labelled trees: hardness and a declarative programming solution, In IEEE - ACM Transactions on Computational Biology and Bioinformatics Volume 11 p.389-397.
  2. SE Verwer, MM de Weerdt, C Witteveen, The efficiency of identifying timed automata and the power of clocks, In Information and Computation Volume 209 p.606-625.
  3. Sicco Verwer, Yingqian Zhang, Learning Decision Trees with Flexible Constraints and Objectives Using Integer Optimization, In Integration of AI and OR Techniques in Constraint Programming p.94-103, Springer.
  4. Qin Lin, Yihuan Zhang, Sicco Verwer, Jun Wang, (2018), MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors, In IEEE Transactions on Intelligent Transportation Systems Volume 20 p.790-796.
  5. Sicco Verwer, Mathijs De Weerdt, Cees Witteveen, (2005), Timed automata for behavioral pattern recognition, In Belgian/Netherlands Artificial Intelligence Conference p.291-296.
  6. Wesley van der Lee, Sicco Verwer, (2018), Vulnerability Detection on Mobile Applications Using State Machine Inference, In Proceedings - 3rd IEEE European Symposium on Security and Privacy Workshops, EUROS&PW 2018 p.1-10, IEEE.
  7. Yihuan Zhang, Qin Lin, Jun Wang, Sicco Verwer, Car-following Behavior Model Learning Using Timed Automata, In IFAC-PapersOnLine p.2353-2358, Elsevier.
  8. Qin Lin, Intelligent control systems: Learning, interpreting, verification
  9. Laurens Bliek, Sicco Verwer, Mathijs de Weerdt, Black-box combinatorial optimization using models with integer-valued minima, In Annals of Mathematics and Artificial Intelligence Volume 89 p.639-653.
  10. Christian Hammerschmidt, Benjamin Loos, Radu State, Thomas Engel, Sicco Verwer, Flexible State-Merging for learning (P)DFAs in Python, In Proceedings of The 13th International Conference on Grammatical Inference Volume 57 p.154-159, JMLR.

Dr. S.E. Verwer