Szymon is interested in intelligent data analysis especially including explainability of AI models. Recently he has been involved in projects in Industry 4.0

Bio

Szymon Bobek, PhD (szymon.bobek@uj.edu.pl, https://szymon.bobek.re) holds a position of an assistant professor at the Jagiellonian University in Krakow, Poland, Faculty of Physics, Astronomy and Applied Computer Science.

He received his degree PHD at AGH UST in 2016 in the field of Computer Science (Artificial Intelligence) and science then he works as a member of GEIST research team. His interests includes most recently areasof eXplainable AI, machine learning and knoweldge engineering.

He participated in national and international research projects as an co-investigator and principle investigator, and also cooperated with several companies in applied projects, especially with massive big data processing using machine learning methods. 

Recently, he has been leading a team that developed a series of original tools in the area of eXplainable AI for the applications in industrial AI. He is a co-organizer of Practical Applications of Explainable Artificial Methods special session (https://praxai.geist.re) and Semantic Data Mining Workshop (https://sedami.geist.re)

Publications

 

1. Bobek S., Nalepa G.J. (2021). Augmenting Automatic Clustering with Expert Knowledge and Explanations. In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12745. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-77970-2_48.
2. Bobek S., Bałaga P., Nalepa G.J. (2021). Towards Model-Agnostic Ensemble Explanations. In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-77970-2_4.
3. Bobek S., Mozolewski M., Nalepa G.J. (2021). Explanation-Driven Model Stacking. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12747. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-77980-1_28.
4. Bobek S., Nalepa G.J. (2021). Introducing Uncertainty into Explainable AI Methods. In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12747. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-77980-1_34.
5. Jakubowski J., Stanisz P., Bobek S., Nalepa G.J. Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations. Sensors. 2022; 22(1):291. DOI: https://doi.org/10.3390/s22010291.
6. Kutt K., Drążyk D., Bobek S., Nalepa G.J. Personality-Based Affective Adaptation Methods for Intelligent Systems. Sensors. 2021; 21(1):163. DOI: https://doi.org/10.3390/s21010163.
7. Nalepa G.J., Bobek S., Kutt K., Atzmueller M. Semantic Data Mining in Ubiquitous Sensing: A Survey. Sensors. 2021; 21(13):4322. DOI: https://doi.org/10.3390/s21134322.

Human in the Loop Clusterign with Knowledge Augmentations (HuLCKA)

The project aims at investigating methods for combining background knowledge with clustering algorithms and eXplainable Artificial Intelligence (XAI) methods in order to provide comprehensive framework for human-in-the loop data analysis.

HuLCKA grant is funded from the Priority Research Area (Digiworld) under the Strategic Programme Excellence Initiative at the Jagiellonian University.

Other Activities

He is a co-organizer of Practical Applications of Explainable Artificial Methods special session (https://praxai.geist.re) and Semantic Data Mining Workshop (https://sedami.geist.re).

He is the scientific secretary of the AIRA seminar.