Marek Kurzyński is a Professor actively engaged in research and teaching, associated with multiple interdisciplinary research teams including the Machine Learning Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. His work spans computational intelligence, optimization, and data-driven decision systems. His research focuses on developing novel optimization techniques such as Dark-Box Optimization and evolutionary methods for multi-criteria problems, with applications in network optimization and classifier training. These efforts reflect a strong integration of theoretical algorithm development and practical implementation in complex systems. The publication and project trends indicate a consistent focus on AI-based optimization, particularly in handling high-dimensional, multi-objective problems using gene-interaction modeling and application-aware architectures. He supervises diploma theses, contributing to academic training in advanced computing disciplines. While no formal awards are listed, his leadership in key research initiatives underscores significant scholarly impact. He is involved in collaborative technological innovation, including the development of a bionic prosthetic hand, demonstrating applied research in biomedical engineering contexts.
Krzysztof Walkowiak is a Professor at the Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology (PWr), where he also serves as the Dean of the Doctoral School. He leads the Computer Networks Team (ZSK) and is actively involved in multiple research projects, including Dark-Box Optimization and evolutionary methods for multi-criteria network design. A Senior Member of IEEE and IEEE ComSoc, he contributes to academic governance as a member of the Polish Academy of Sciences’ Committee on Electronics and Telecommunications. PhD in Computer Science (2000, with distinction) Habilitation in Computer Science (2008) Professor of Technical Sciences (2017) Dean of the Doctoral School, PWr (since 2020) Senior Member, IEEE and IEEE Communications Society Member, Committee on Electronics and Telecommunications, Polish Academy of Sciences His research spans computer network optimization, machine learning in networks, evolutionary algorithms, survivable optical networks, and intelligent computational techniques . He has pioneered the integration of AI methods in teleinformatics and leads initiatives in intent-based and cognitive networking. His work emphasizes multi-layer, application-aware network design and distributed processing systems. The 15 most recent publications reflect a strong trend in optimization under uncertainty, AI-driven networking, and metaheuristic algorithm development . There is a clear focus on survivability, scalability, and automation in modern network architectures, with increasing attention to edge intelligence via TinyML and cognitive systems. His recent work also addresses internationalization and pedagogical innovation in doctoral education. Fabio Neri Best Paper Award 2014 (Elsevier Journal of Optical Switching and Networking) Best Paper Award, DRCN2009 (Washington, USA) Best Paper Award, RNDM2015 (Munich, Germany) Medal of the National Education Commission (2011) Scientific Scholarship, Wrocław University of Science and Technology (2017) Star of Internationalization 2024 – Teaching Star Professor Walkowiak has supervised 10 completed PhD theses and currently mentors 5 doctoral students. He has led or managed 13 research projects funded by NCN (including 4 OPUS grants), EU, NAWA, and national agencies. His grant leadership includes the NAWA InterDocSchool project (2021–2023) and the Unite! Doctoral School curriculum development. He has reviewed over 300 journal submissions and served as session chair at 22 international conferences. He leads several research teams: Computer Networks Team (ZSK), Machine Learning Team, Advanced Data Analysis Methods Team, Metaheuristics Team, and Teaching Team . He has developed innovative educational programs such as the Research Skills course and Recent Research Trends , promoting international collaboration and doctoral training. He championed the transition to English-language instruction in the Doctoral School and promotes internationalization as a 'team sport' across institutional levels.
Tomasz Kozłowski is a researcher at Wrocław University of Science and Technology, actively involved in the Team of Advanced Data Analysis Methods (Zespół Zaawansowanych Metod Analizy Danych - ZZMAD). His work bridges engineering and data science, with a strong emphasis on industrial applications, particularly in mining and mechanical systems diagnostics. Research Interests: Non-invasive diagnostics of machinery using NDT techniques Modeling of conveyor belt systems in surface and underground mines using DEM Signal processing for industrial monitoring Development of optimization algorithms for multi-criteria and multi-level engineering problems His research projects focus on evolutionary optimization methods, multi-objective classifier training, and application-aware network optimization, reflecting a deep integration of computational intelligence with real-world engineering challenges. Scientific Awards: Rector's Award for Scientific Achievements (2017) Rector's Award for Scientific Achievements (2018) Recognition in the Mining Success of the Year competition, category Innovation (2018) Tomasz Kozłowski is engaged in advanced research projects involving black-box optimization, gene-inspired search techniques, and multi-layered network optimization. He contributes to the academic community through research platforms such as ORCID, ResearchGate, and the DONA scientific output system of PWr. Laboratory and Research Teams: Team of Advanced Data Analysis Methods (ZZMAD) Machine Learning Team Computer Networks Team Teaching Team Metaheuristics Team
Szymon Wojciechowski is an Assistant Professor at the Department of Computer Systems and Networks, Faculty of Electronics, Wrocław University of Science and Technology. He has been actively contributing to research and teaching since October 2019. He is a member of several research teams, including the Machine Learning Team, Metaheuristics Team, and Advanced Data Analysis Methods Team. Faculty of Electronics, Wrocław University of Science and Technology Department of Computer Systems and Networks Machine Learning Team Teaching Team Computer Networks Team His educational background includes a Bachelor of Engineering in Medical Information Systems (2018) and a Master of Engineering in Advanced Informatics and Control (2019), both from Wrocław University of Science and Technology. Dr. Wojciechowski's research focuses on machine learning , particularly in imbalanced data classification , multi-objective optimization , and data stream clustering . He explores data preprocessing techniques and evolutionary algorithms to enhance classifier performance in challenging decision-making scenarios. His work often integrates optimization methods into machine learning pipelines. The recent publications demonstrate a strong trend in applying evolutionary and hybrid AI techniques to solve problems in imbalanced data and network optimization . The research spans domains such as optical networking, semi-supervised clustering, and ensemble classification, indicating a cross-disciplinary approach combining computer networks and intelligent systems. He is actively involved in research projects including IDSTREAM and MOO , focusing on data stream analysis and multi-objective optimization. He completed a research internship at Universidad de Granada, Spain, where he worked on semi-supervised clustering with k-means. He supervises diploma theses and contributes to teaching in computer science and informatics. Dr. Wojciechowski is affiliated with the following research teams: Machine Learning Team Teaching Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team
Professor Andrzej Jaszkiewicz is a faculty member at the Faculty of Computer Science and Telecommunications, Poznań University of Technology, where he works in the Institute of Informatics. He holds the position of full professor with extensive experience in multiobjective optimization and evolutionary algorithms, as evidenced by his habilitation completed in 2001 and continuous research output through 2025. Professor Jaszkiewicz's research focuses on theoretical and practical aspects of multiobjective optimization, particularly addressing the challenges of many-objective problems. His work spans quality indicator development (hypervolume, R2), efficient data structures (ND-Trees), evolutionary algorithm design, and applications to combinatorial optimization problems. His publications demonstrate consistent theoretical rigor combined with practical implementation considerations. Recent publications (2022-2025) reveal continued productivity with multiple articles in top journals like IEEE Transactions on Evolutionary Computation and Physics of Fluids. These works explore theoretical properties of quality indicators, efficient calculation methods, and novel applications of optimization techniques to complex problems including fluid dynamics. Professor Jaszkiewicz has supervised doctoral research including Tarek Alkhaeir's 2021 dissertation on software quality metrics and Marek Kubiak's 2009 work on memetic algorithms. He has also reviewed numerous doctoral dissertations in related fields, demonstrating his standing in the academic community and commitment to graduate education. His scholarly output includes journal articles, conference papers, book chapters, and two books. The 2005 edited volume 'Advanced OR and AI methods in transportation' reflects his interest in practical applications of optimization techniques. His research demonstrates sustained scholarly activity from the 1990s to the present, with particular emphasis on advancing the state-of-the-art in multiobjective optimization theory and practice.
Dr. Tomasz Białaszewski serves as an Assistant Professor at Gdańsk University of Technology within the Department of Decision Systems and Robotics, Faculty of Electronics, Telecommunications and Informatics. He holds the position of Deputy Head of Department and maintains his office in Building A of the faculty. His research focuses on evolutionary multi-objective optimization, particularly developing quality criteria for highly dimensional optimization problems. Dr. Białaszewski has pioneered innovative approaches using genetic gender concepts to solve complex multi-objective optimization challenges. His work bridges theoretical computational methods with practical applications in decision systems and robotics. Analysis of his recent publications reveals consistent contributions to evolutionary computation methodologies, with particular emphasis on improving evaluation metrics for multi-objective optimization in high-dimensional spaces. His research demonstrates increasing sophistication in handling complex optimization problems through novel algorithmic approaches. Dr. Białaszewski maintains active research collaboration, primarily with Z. Kowalczuk, as evidenced by co-authored publications. His work contributes significantly to advancing computational optimization techniques within the academic community.
