dr. Boris Čule

Bio

Ik ben Universitair Docent bij de departement Cognitive Science and Artificial Intelligence van Tilburg University. Ik heb mijn doctoraat in Computerwetenschappen aan de Universiteit Antwerpen behaald, waar ik vervolgens als post-doc werkte. Mijn onderzoeksinteressen omvatten een breed scala aan onderwerpen binnen Data Mining, Machine Learning en AI, met een bijzondere focus op sequentiële of temporele data. Mijn publicaties zijn te vinden op mijn Google Scholar-profiel.

Vakken

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

Recente publicaties

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.