Biography

I am an assistant professor in the Department of Advanced Computing Sciences at Maastricht University. Below are keywords of research topics which are of interest to me:

artificial intelligence (ai), deep learning, digitalization, domain knowledge integration, industrial ai, industry 4.0, knowledge graphs, machine learning, neuro-symbolic ai, smart manufacturing

Publications

• Pietrasik, Marcin, Anna Wilbik, Nikola Prianikov, Cristina Afanasii, Stefan Deelen, Adolfo Santamónica, Sebastian Schönleben, Mirunalini Sekar, Kim Ragaert, and Sin Yong Teng. "Data-driven Fingerprinting Plastic Waste Material Using Low-cost Spectroscopy With Data Fusion." In Digital Chemical Engineering (2026): 100305. [paper]
• Prianikov, Nikola, Evelyne Janssen-van Dam, Marcin Pietrasik, and Charalampos Kouzinopoulos. "Machine Learning for Pattern Detection in Printhead Nozzle Logging." In IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1443-1447. 2025. [paper]
• Pietrasik, Marcin, Marek Reformat, and Anna Wilbik. "Hierarchical blockmodelling for knowledge graphs." Semantic Web Journal 16.5 (2025): 22104968251377338. [paper] [code]
• Athanasopoulos, Athanasios, Matúš Mihalák, and Marcin Pietrasik. "Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines." In IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 1-6. 2025. [paper] [code] [data]
• Parii, Dan, Evelyne Janssen, Guangzhi Tang, Charalampos Kouzinopoulos, and Marcin Pietrasik. "Predicting the Lifespan of Industrial Printheads with Survival Analysis." In IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 1-6. 2025. [paper]
• Mommers, John, Lazar Barta, Marcin Pietrasik, and Anna Wilbik. "MASSISTANT: A Deep Learning Model for De Novo Molecular Structure Prediction from EI‑MS Spectra via SELFIES Encoding." Journal of Chromatography A (2025):466216. [paper]
• Pietrasik, Marcin, Anna Wilbik, Yannick Damoiseaux, Tessa Derks, Emery Karambiri, Shirley de Koster, Daniel van de Velde, Kim Ragaert, and Sin Yong Teng. "Capturing Variability in Material Property Predictions for Plastics Recycling via Machine Learning." Digital Chemical Engineering (2025):100239. [paper]
• Pietrasik, Marcin, Marek Reformat, and Anna Wilbik. "Non-Parametric Path Based Model for Taxonomy Induction in Knowledge Graphs." Preprint 2024. [paper] [code]
• Härmä, Aki, Marcin Pietrasik, and Anna Wilbik. "Empirical Capacity Model for Self-Attention Neural Networks." Preprint. 2024. [paper]
• Pietrasik, Marcin, Anna Wilbik, and Paul Grefen. "The enabling technologies for digitalization in the chemical process industry." Digital Chemical Engineering 12 (2024): 100161. [paper]
• Pietrasik, Marcin, Anna Wilbik, Paul Grefen, Christian Wagner, and Luis Magdalena. "Breaking Boundaries Initiative: Workshop on Industry-Academia Collaboration [Technical Activities]." IEEE Computational Intelligence Magazine 19, no. 1 (2024): 16-19. [paper]
• Grefen, Paul, Irene Vanderfeesten, Anna Wilbik, Marco Comuzzi, Heiko Ludwig, Estefania Serral, Frank Kuitems, Menno Blanken, and Marcin Pietrasik. "Towards Customer Outcome Management in Smart Manufacturing." Machines 11, no. 6 (2023): 636. [paper]
• Pietrasik, Marcin, and Marek Z. Reformat. "Probabilistic coarsening for knowledge graph embeddings." Axioms 12, no. 3 (2023): 275. [paper]
• Zhang, Yujia, Marcin Pietrasik, Wenjie Xu, and Marek Reformat. "Hierarchical Topic Modelling for Knowledge Graphs." In European Semantic Web Conference, pp. 270-286. 2022. Acceptance rate (research track): 19.5%. [paper] [code]
• Pietrasik, Marcin, and Marek Reformat. "Path Based Hierarchical Clustering on Knowledge Graphs." Preprint. 2021. [paper] [code]
• Pietrasik, Marcin, and Marek Reformat. "Neural Blockmodeling for Multilayer Networks." In 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. 2021. Acceptance rate: 59.3%. [paper] [code]
• Pietrasik, Marcin, and Marek Reformat. "A simple method for inducing class taxonomies in knowledge graphs." In European Semantic Web Conference, pp. 53-68. Cham: Springer International Publishing, 2020. [paper] [code]
• Singh, Abhineet, Marcin Pietrasik, Gabriell Natha, Nehla Ghouaiel, Ken Brizel, and Nilanjan Ray. "Animal Detection in Man-made Environments." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1438-1449. 2020. Acceptance rate: 34.5%. [paper] [supplementary] [code] [wacv video] [atv video] [labelling tool demo] [datasets]
• Yu, Zheng, Xuhui Fan, Marcin Pietrasik, and Marek Z. Reformat. "Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6704-6711. 2020. Acceptance rate: 20.6%. [paper] [code]
• Yu*, Zheng, Marcin Pietrasik*, and Marek Reformat. "Deep Dynamic Mixed Membership Stochastic Blockmodel." In IEEE/WIC/ACM International Conference on Web Intelligence, pp. 141-148. 2019. Acceptance rate (full paper): 18.4%. [paper]

Asterisk (*) denotes equal contribution.

Presentations

• September 16, 2025: Invited talk on "Data-driven Model for Fingerprinting Plastic Waste Material Using Low-cost Spectroscopy" at Colloquium Brightsite in Geleen, The Netherlands. [slides]
• May 14, 2025: Presentation on "Predicting the Lifespan of Industrial Printheads with Survival Analysis" at ICPS 2025 in Emden, Germany. [slides]
• May 14, 2025: Presentation on "Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines" at ICPS 2025 in Emden, Germany. [slides]
• April 17, 2025: Poster presentation on "Capturing Variability in Material Property Predictions for Plastics Recycling via Machine Learning" at the 2025 Brightsite Meet our Scientists event in Geleen, The Netherlands. [poster]
• April 16, 2025: Poster presentation on "Anomaly Detection for Predictive Maintenance in Industrial Robots" at ICT Open 2025 in Utrecht, The Netherlands. [poster]
• February 25, 2025: Invited talk on "The Enabling Technologies for Digitalization in the Chemical Process Industry" at the 2025 Brightlands Science Lecture in Geleen, The Netherlands. [slides]
• November 29, 2024: Poster Presentation on "The Enabling Technologies for Digitalization in the Chemical Process Industry" at ChemAI 2024 in Amsterdam, The Netherlands. [poster]
• November 20, 2024: Poster Presentation on "Non-Parametric Path Based Model for Taxonomy Induction in Knowledge Graphs" at BNAIC/BeNeLearn 2024 in Utrecht, The Netherlands. [poster]
• June 20, 2024: Invited talk on "The Enabling Technologies for Digitalization in the Chemical Process Industry" at the Chemskills Webinar attended virtually. [slides]
• May 16, 2024: Poster presentation on "The Enabling Technologies for Digitalization" at the 2024 Brightsite Meet our Scientists event in Geleen, The Netherlands. [poster]
• May 11, 2023: Poster presentation on "Digitalization Roadmap for the Process Industry" at the 2023 Brightsite Meet our Scientists event in Geleen, The Netherlands. [poster]
• December 1, 2022: PhD Seminar on "Learning Hierarchies from Knowledge Graphs" at the Universtiy of Alberta in Edmonton, Canada.
• June 24, 2022: Seminar talk on "Statistics for Knowledge Graph Modelling" at TU Dresden in Dresden, Germany. [slides]
• June 2, 2022: Seminar talk on "Blockmodelling Knowledge Graphs" at TU Dresden in Dresden, Germany. [slides]
• March 3, 2022: Candidacy Seminar on "Learning Hierarchies From Knowledge Graphs" at the Universtiy of Alberta in Edmonton, Canada. [slides] [report]
• July 20, 2021: Poster presentation on "Neural Blockmodeling for Multilayer Networks" at IJCNN 2021 virtual conference. [slides]
• August 20, 2020: Workshop presentation on "Path Based Hierarchical Clustering on Knowledge Graphs" at the ASI Workshop in Edmonton, Canada (attended virtually). [slides]
• June 4, 2020: Conference talk on "A Simple Method for Inducing Class Taxonomies in Knowledge Graphs" at ESWC 2020 in Heraklion, Greece (attended virtually). [slides]
• March 6, 2020: Poster presentation on "A Simple Method for Inducing Class Taxonomies in Knowledge Graphs" at the Inaugural ASI Workshop in Edmonton, Canada. [poster]
• October 16, 2019: Conference talk on "Deep Dynamic Mixed Membership Stochastic Blockmodel" at WI 2019 in Thessaloniki, Greece. [slides]

Internship Experience

• May 2022 - August 2022: Research Intern at the TU Dresden Institute of Theoretical Computer Science. Supervisor: Prof. Markus Krötzsch.
• February 2021 - September 2021: Research Intern at FIND AI. Supervisors: Prof. Petr Musilek, Prof. Marek Reformat, Alireza Haghnegahdar.
• September 2018 - December 2018: Research Intern at Granify. Supervisors: Prof. Denilson Barbosa, Prof. Marek Reformat, Dr. Marcin Mizianty.
• May 2018 - August 2018: Product Development Support in Machine Learning at ACAMP. Supervisors: Dr. Nehla Ghouaiel, Ken Brizel.

Teaching Experience

• February 2024 - Present: Course Coordinator for KEN 4423, Data Fusion, at Maastricht University.
• September 2023 - Present: Course Coordinator for PRA 3021, Topics in Scientific Computing, at Maastricht University.
• January 2022 - April 2022: Teaching Assistant for ECE 493, Software Systems Design Project, at the Universtiy of Alberta.
• September 2021 - December 2022: Teaching Assistant for ENCMP 100, Computer Programming for Engineers, at the Universtiy of Alberta.
• January 2021 - April 2021: Teaching Assistant for ECE 720, Introduction to Artificial Intelligence, at the Universtiy of Alberta.