dr. Boris Čule

Bio

I am Assistant Professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University. I obtained my Ph.D. in Computer Science at the University of Antwerp, where I subsequently worked as post-doc. My research interests cover a wide range of topics within Data Mining, Machine Learning and Artificial Intelligence, with a particular focus on sequential or temporal data. My publications can be found on my Google Scholar profile.

Courses

• Master thesis/Data Science in Action
• RS: Big Data
• Analysis of Customer Data (Fall)
• Analysis of Customer Data (Spring)
• Cognitive Science 1

Recent publications

1. Unveiling the Power of ARIMA, Support Vector and Random Forest Regres…

   Gajewski, P., Čule, B., & Ranković, N. (2023). Unveiling the Power of ARIMA, Support Vector and Random Forest Regressors for the Future of the Dutch Employment Market. Journal of theoretical and applied electronic commerce research, 18(3), 1365-1403.

2. Leveraging Sequential Episode Mining for Session-Based News Recommend…

   Karimi, M., Čule, B., & Goethals, B. (2023). Leveraging Sequential Episode Mining for Session-Based News Recommendation. In F. Zhang, H. Wang, M. Barhamgi, L. Chen, & R. Zhou (Eds.), Web Information Systems Engineering – WISE 2023 - 24th International Conference, Proceedings (pp. 594-608). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14306 LNCS).

3. Efficiently Mining Frequent Representative Motifs in Large Collection…

   Rotman, S., Čule, B., & Feremans, L. (2023). Efficiently Mining Frequent Representative Motifs in Large Collections of Time Series. In Efficiently Mining Frequent Representative Motifs in Large Collections of Time Series (pp. 66-75)

4. PETSC: pattern-based embedding for time series classification

   Feremans, L., Čule, B., & Goethals, B. (2022). PETSC: pattern-based embedding for time series classification. Data Mining and Knowledge Discovery, 36(3), 1015-1061.

5. Face in the Game - Using Facial Action Units to Track Expertise in Co…

   Guglielmo, G., Mavromoustakos Blom, P., Klincewicz, M., Čule, B., & Spronck, P. (2022). Face in the Game: Using Facial Action Units to Track Expertise in Competitive Video Game Play. In 2022 IEEE Conference On Games (CoG) (pp. 112-118). IEEE.