Prof. Szpyrka is a full professor in the Department of Computer Science and Artificial Intelligence at the Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering at AGH University of Science and Technology in Kraków. He graduated in Mathematics from the University of Rzeszów in 1997 and has been a faculty member at AGH since 2000, progressing through successive academic ranks. He has authored over 140 publications in the fields of formal methods, software engineering, machine learning, and data science, including six books. Prof. Szpyrka developed the concept of RTCP networks for modeling real-time embedded systems and led a team that created the formal modeling language Alvis. He has collaborated with IT companies as a specialist in modeling and machine learning, carrying out applied research projects. From 2019 to 2024, he served as Director of the AGH Doctoral School and has supervised nine PhD students, fostering the next generation of researchers in computer science and applied data science.
In 1993, he graduated from the Minsk Institute of Radio Engineering with a degree in computers, systems, and networks. In 1996, he completed his doctoral studies at the Belarusian State University of Informatics and Radioelectronics, and in the following years he worked at the above-mentioned university as an assistant and later as an assistant professor. He defended his doctoral thesis in the field of “Computer Science” in 1997 at the Faculty of Computing Technology. In 2000, he moved to Poland, where he worked at the Zielona Góra University of Technology and then at the University of Zielona Góra. He defended his habilitation thesis in computer science at the Technical University of Ilmenau (Germany) in 2009 (nostrification - Warsaw University of Technology). Since 2018, he has been a professor at AGH University of Science and Technology in the Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering. His scientific interests focus on issues related to the modeling and analysis of concurrent systems. He is the author of over 100 scientific publications. He has supervised two doctoral students.
Academic researcher and university lecturer specializing in artificial intelligence and data science. He earned his PhD from AGH University of Kraków in 2010 and has since authored more than 70 scientific publications in peer-reviewed journals and conference proceedings. He has also delivered keynote lectures at conferences and conducted professional training in machine learning, data engineering, and database technologies. He served as the coordinator of two research projects focused on applying AI in smart city environments, which led to the deployment of an intelligent street lighting system across multiple districts of Kraków and the development of a graph-based methodology for integrating sensor and GIS data to train spatially-aware machine learning models capable of contextual event understanding. In addition, he is a co-founder of a university spin-off specializing in smart lighting solutions, where he conceived and supervised the development of a dedicated GIS application for preparing high-resolution datasets for street lighting design, and coordinated the creation of a graph-based system leveraging machine learning to estimate energy demand for street lighting using general-purpose map data. His broader professional interests include web and mobile application development as well as IT security, and he incorporates his practical experience in these areas into courses designed for computer science students at AGH University of Kraków.
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He holds a Master’s degree in Computer Science and Intelligent Systems with a specialisation in Software Engineering. His master’s thesis, titled “Context Analysis of Geographic Objects using Graph Structures”, was supervised by Sebastian Ernst. He is currently an assistant at the Department of Computer Science and Artificial Intelligence and a doctoral candidate at the AGH Doctoral School. His PhD research, conducted within the discipline of technical computer science and telecommunications, focuses on “Declarative Coordination of Multimodal, Inferential Analytic Processes”. His research addresses the growing complexity and error-proneness of advanced analytic workflows, particularly those involving multiple data sources and modalities. The proposed work aims to develop a declarative methodology for defining and managing such processes, enabling high-level specification of analytic workflows and facilitating their adaptability to evolving requirements. The approach integrates diverse analytic modules, including machine learning models (classification and regression), GIS tools for spatial analysis, statistical methods, time-series correlation algorithms, and large language models.
Mateusz Mazur is a Master’s student in Computer Science and Intelligent Systems at AGH University of Kraków, specializing in Artificial Intelligence and Data Analysis. He is currently a student-intern at the Department of Computer Science and Artificial Intelligence. His research focuses on the integration of heterogeneous data sources for the analysis and reconstruction of spatial information. In his current work, he develops methods for inferring road-network characteristics by linking visual detection with declarative reasoning. This approach supports the automatic verification of traffic organization and improves the quality of datasets such as OpenStreetMap. He has co-authored publications on augmenting spatial data using aerial imagery, as well as on the application of large language models in computer science education, including automated analysis of student solutions. He has presented his work at scientific conferences.
Master’s student in Computer Science at AGH University of Kraków, researching how teams of autonomous agents learn to cooperate and how they degrade when things go wrong. His Master’s thesis focuses on benchmarking multi-agent communication architectures under structured fault conditions: robot attrition, signal interference, and cascading failures. The goal is to understand not just peak performance, but resilience. He is also drawn to the engineering side of AI in a way that goes beyond model training. He has built distributed image-processing pipelines, asynchronous task distribution systems, and custom developer tooling. He uses JAX for research code where iteration speed matters, and explores languages like Rust out of a broader interest in how performance constraints shape software design. What ties his work together is a straightforward interest in systems that are both intelligent and robust — not one at the expense of the other.