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Krzysztof WęcelView profile

Professor

• Semantic Technologies
• Linked Open Data
• Big Data Analytics
• +5 more

Dr hab. Krzysztof Węcel serves as Professor and current Head of the Department of Economic Informatics at Poznan University of Economics and Business (UEP), appointed on October 4, 2024. His primary affiliation spans over 25 years with UEP's Department of Economic Informatics, which maintains one of Poland's longest-running academic websites since 1998. He holds dual recognition through habilitation from University of Potsdam (2020) and professorship conferred by UEP (June 24, 2020). His academic milestones: Habilitation degree in Economic Informatics, University of Potsdam (2020) Professor title, Poznan University of Economics and Business (2020) Węcel's research centers on Semantic Technologies and data quality assessment across multilingual Wikipedia, with emphasis on company information verification, citation analysis, and open data applications. His work bridges Big Data analytics with practical business solutions, particularly in maritime logistics where he pioneered evolutionary algorithm-based AIS data processing. Current investigations focus on generative AI's dual role in creating and combating disinformation, including ChatGPT's impact on academic writing and fake news propagation. Recent publications (2022-2025) reveal three dominant trends: First, systematic analysis of Wikipedia's reliability across languages during crises like the pandemic and Ukraine war. Second, development of AI-driven fact-checking frameworks (e.g., OpenFact project's CLEF 2023 victory). Third, exploration of generative AI's societal impact ranging from student creativity to disinformation campaigns. Scientific awards received: Best Paper Award at ICIST 2017 Conference Award for most innovative article at NATCON 2018 conference Microsoft Azure for Research Award (2016) As academic advisor, he leads the 'Semantic Technologies' diploma seminar attracting high-achieving students, with participants winning the 29th UEP Foundation Competition (2025) and Eurostat's Web Intelligence Challenge (2024). His grant portfolio includes the 'Maritime Big Brother' project (2017) for ship voyage prediction using AIS data and Microsoft Azure funding for Wikipedia quality enhancement. Ongoing initiatives include OpenFact (fake news detection) and GOBLIN projects. He actively collaborates with SKN Data Science student circle (evidenced by 2024/2025 inaugural meeting) and international consortia like CLEF and QOD workshops. Departmental leadership involves managing the OpenFact research team that achieved top results in CheckThat! Lab competitions, alongside maritime data analytics groups applying evolutionary algorithms to shipping networks.

• Optimization Algorithms
• Evolutionary Algorithms
• Machine Learning
• +4 more

Rafał Biedrzycki is an Assistant Professor at The Institute of Computer Science within Warsaw University of Technology's Faculty of Electronics and Information Technology. His research focuses on optimization algorithms, evolutionary computation, and machine learning applications. He holds a PhD in Information Science (2009) and a D.Sc. (2024). Key research interests include evolutionary algorithms (e.g., Differential Evolution, CMA-ES), optimization techniques for real-world problems (e.g., compressor scheduling, optical networks), and algorithm benchmarking. He has contributed to improving constraint-handling methods and hybrid algorithm designs. Received team awards for scientific achievements from Warsaw University of Technology (2019, 2023) and teaching excellence (2021, 2024). Active in interdisciplinary projects, including the DAFNE initiative for data fusion systems (2010-2011). Supervises research in optimization, machine learning, and computational electromagnetics. His work bridges theoretical algorithm development with practical applications in engineering and data analysis. Recent efforts include analysis of CEC competition algorithms and parameter-tuning methodologies.

Milena StróżynaView profile

Assistant Professor

• Data Modeling
• Artificial Intelligence
• Disinformation Detection
• +5 more

Milena Stróżyna is an Assistant Professor at the Department of Economic Informatics in the University of Economics in Poznan , Poland. Her work focuses on data modeling, AI applications in disinformation detection, and maritime data analysis . Email: milena.strozyna@ue.poznan.pl Research interests span: Data Modeling & Analysis : Extracting insights from diverse data sources, ensuring quality, and implementing ERP systems Disinformation Studies : Developing AI tools for fake news detection and semantic mapping of misinformation topics Maritime Data Science : Crisis impact analysis in shipping, anomaly detection in maritime transport Scientific Contributions include: Pioneering OpenFact system for information verification Creating adversarial text detection methods Leading research on generative AI risks in information integrity Notable Awards : 2018: Most innovative article at NATCON conference Multiple first-place international competition wins with OpenFact system (2022-2024)

Aleksandra KnapińskaView profile

Researcher

• Network Optimization
• Machine Learning
• Traffic Prediction
• +7 more

Aleksandra Knapińska is a doctoral researcher at the Wrocław University of Science and Technology, Faculty of Electronics, Photonics and Microsystems, Department of Computer Networks and Systems. She is an active member of multiple research teams including the Computer Networks Team, Machine Learning Team, and Advanced Data Analysis Methods Team. Her work is primarily associated with the MAAN project focused on optimizing multilayer application-aware networks. Her research interests center on network optimization , machine learning for traffic prediction , and multi-criteria optimization in optical and multilayer networks . She applies advanced data analysis and machine learning techniques to model and predict time-varying network traffic, particularly in backbone and optical networks. Her work bridges theoretical modeling with practical network performance evaluation. The most recent articles highlight a strong trend in using machine learning—especially ensemble and neural network models—for short-term and long-term network traffic forecasting. These publications emphasize challenges such as feature selection, bandwidth blocking, node failure resilience, and traffic fragmentation in spectrally-spatially flexible optical networks. The research is deeply applied, targeting real-world network performance improvements. Scientific profiles: Google Scholar ResearchGate ORCID: 0000-0003-2654-4893 She is involved in supervising diploma theses and has participated in international research collaborations, including an internship at Politecnico di Torino, Italy, under the InterDocSchool program. She has presented her work at major conferences such as GLOBECOM, ONDM, RNDM, and CNSM. Her PhD thesis, titled "Optimization of multilayer networks with time-varying traffic aided by traffic prediction", is supervised by Prof. Krzysztof Walkowiak, with Dr. Piotr Lechowicz as co-supervisor. She is part of ongoing research projects including: Dark-Box Optimization – Developing highly effective general-purpose optimizers Evolutionary methods for multi-criteria optimization with many criteria Advanced optimization of multi-layer application-aware networks (MAAN) Using multi-criteria optimization in classifier training for decision-making tasks

Paweł MyszkowskiView profile

Professor

• Evolutionary Computation
• Metaheuristics
• Multi-objective Optimization
• +7 more

Paweł Myszkowski is a Professor at Wrocław University of Science and Technology, affiliated with the Department of Artificial Intelligence within the Faculty of Computer Science and Management. He is a key member of the Metaheuristics Team and actively contributes to research in evolutionary computation, multi-objective optimization, and scheduling algorithms. His research focuses on evolutionary algorithms , metaheuristics , and multi-objective optimization , particularly applied to the Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP). He has developed hybrid algorithms combining differential evolution, greedy methods, and ant colony optimization. His work includes the creation of benchmark datasets (iMOPSE) and quality measures for optimization algorithms. The recent publications show a strong trend in algorithmic innovation for complex scheduling and design automation , with applications in architectural design and financial modeling. His work bridges theoretical optimization and practical implementation in software systems. Golden Badge of Wrocław University of Science and Technology He supervises diploma theses and collaborates extensively with researchers such as Maciej Laszczyk and Marek Skowroński. He has contributed to the development of tools and benchmarks that support reproducibility and comparative evaluation in computational intelligence research. He is involved in research projects on dark-box optimization, multi-criteria optimization for classifiers, and application-aware network optimization, indicating an ongoing active research agenda.

Michał PanekView profile

Researcher

• Machine Learning
• Optimization
• Multi-criteria Optimization
• +7 more

Michał Panek is a researcher at the Department of Computer Systems and Networks, Faculty of Computing, Wroclaw University of Science and Technology. He is a member of the Machine Learning Team (ZUM) and actively contributes to research in optimization and machine learning applications in networking. His research interests include: Machine Learning in cellular networks Multi-criteria and many-objective optimization Evolutionary algorithms with gene-linkage techniques Application-aware multi-layer network optimization Classifier training using optimization methods The research projects he is involved in focus on developing advanced general-purpose optimizers (Dark-Box Optimization), evolutionary methods for high-dimensional multi-criteria problems, and performance analysis for wireless network automation. These projects reflect a strong interdisciplinary trend combining computer science, optimization theory, and telecommunications engineering. Michał Panek serves as a supervisor for diploma theses and is engaged in teaching activities. He is currently completing his doctoral studies, with a thesis titled "Machine Learning-based performance analysis to enhance the wireless network automation," supervised by Prof. Michał Woźniak and Prof. Ireneusz Jabłoński. The defense is scheduled for March 18, 2025. He is involved in key research teams including: Machine Learning Team Teaching Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

Sylwia Śliwińska-WilczewskaView profile

Tutor

• Ecophysiology
• Cyanobacteria Ecology
• Microalgae Studies
• +3 more

Dr. Sylwia Śliwińska-Wilczewska is a Tutor at the Institute of Oceanography, University of Gdańsk, affiliated with the Faculty of Oceanography and Geography. Her research focuses on ecophysiology of cyanobacteria and microalgae, particularly their autecology, global distribution, and allelopathic interactions. She collaborates with students on experimental design, data analysis, and scientific publication in peer-reviewed journals. Her work addresses airborne microbial transport, aerosolized algae health risks, and Baltic Sea ecosystem dynamics. Current projects include picocyanobacterial niche adaptation, allelochemical effects in phytoplankton communities, and cyanobacteria bloom detection via satellite indices. Research emphasizes Baltic coastal zones, with studies on cyanobacteria-macroalgae interactions, heavy metal tolerance, and climate change impacts. Experimental approaches combine lab cultures with field surveys, leveraging remote sensing and molecular techniques.

Wojciech ThomasView profile

Lecturer

• DevOps
• Cloud Computing
• Systems Administration
• +3 more

Wojciech Thomas is a Lecturer at the Department of Applied Informatics, Faculty of Information and Communication Technology, Wrocław University of Science and Technology. He teaches courses including Technologies Supporting Software Development (DevOps) , Cloud Computing , and Script Languages . Since 2016, he has directed the postgraduate program Computer Network Administration , designed for professionals seeking advanced knowledge in network/server management. His research focuses on DevOps, cloud technologies (AWS/Azure), automation of complex IT environments, web applications, and software engineering. He has published on topics ranging from task scheduling algorithms to trends in software engineering education. His publications (2000–2021) reflect interdisciplinary work in computer science, operations research, and educational methodology. Common themes include optimization algorithms, cloud infrastructure, and academic curriculum development.

• Optimal periodic control
• Evolutionary algorithms
• Control theory
• +3 more

Marek Skowron is an academic researcher at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, specializing in optimal periodic control and evolutionary algorithms. He is affiliated with the Department of Control Systems and Mechatronics. Research Interests: His work focuses on optimizing periodic control systems with inventory and energy constraints, applying evolutionary algorithms to find globally optimal solutions for complex industrial processes. Special emphasis is placed on stability analysis and multifrequency control approaches. Publication Trends: His research spans from 2004 to 2016, demonstrating consistent application of evolutionary computation to control system optimization, particularly in industrial and chemical engineering contexts. Key themes include constraint handling, cycle stability, and multiobjective optimization frameworks. Contact: Email: marek.skowron@pwr.edu.pl Office: C-3, room 217 Hours: Thursdays 19:00-21:00, Fridays 19:00-21:00

Wojciech KmiecikView profile

Researcher

• Optical Networks
• Elastic Optical Networks
• Survivable Overlay Multicasting
• +12 more

Dr. Eng. Wojciech Kmiecik is a researcher at the Department of Computer Systems and Networks, Faculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology. He is actively involved in multiple research teams including Machine Learning, Computer Networks, Advanced Data Analysis Methods, and Metaheuristics. He also contributes to teaching and supervises diploma theses. His research focuses on optical networks , survivable multicasting , multi-criteria optimization , and metaheuristic algorithms . He has led and contributed to projects such as Dark-Box Optimization, evolutionary multi-criteria optimization, and advanced methods for multi-layer networks. His work bridges theoretical algorithm development with practical network design. The publication trends from 2010 to 2020 show a consistent focus on network survivability , elastic optical networks , and task allocation in parallel systems . His research integrates optimization techniques into networking solutions, particularly in dual homing architectures and deadline-sensitive provisioning. Key themes include resilience, efficiency, and scalability in both optical and computational systems. Scientific Awards: Medal for long-standing service to Wrocław University of Science and Technology Dr. Kmiecik has supervised diploma theses and is involved in teaching. He has not received externally reported grants, but his sustained project involvement suggests institutional or collaborative funding. He collaborates extensively with Prof. Krzysztof Walkowiak and other researchers in the department. Research Labs and Teams: Machine Learning Team Teaching Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

Mariusz KoziołView profile

Lecturer

• Machine Learning
• Multi-criteria Optimization
• Evolutionary Algorithms
• +16 more

Mariusz Kozioł is a faculty member at the Department of Computer Systems and Networks, Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology (PWr). He actively contributes to both teaching and research, participating in multiple research teams including the Machine Learning Team, Teaching Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. His research interests span a wide range of topics in computer science and engineering, particularly focusing on: Machine Learning and Classifier Training Multi-criteria and large-scale optimization Evolutionary algorithms and metaheuristics Dark-box optimization methods Application-aware multi-layer network optimization Operating systems, virtualization, and high availability solutions IBM Power platforms (AIX, IBM i) Data science and enterprise design thinking His recent work emphasizes the application of optimization techniques in machine learning and complex system design, with a strong practical orientation in enterprise IT and data science education. He has been recognized for his expertise through IBM certifications and institutional honors. Notable recognitions include: Golden Badge of Wrocław University of Science and Technology Bronze and Golden Medals for Long-Term Service IBM Data Science Practitioner Certificate: Instructor (issued Aug 21, 2023) IBM Enterprise Design Thinking Practitioner (issued Aug 1, 2023) Mariusz Kozioł supervises diploma theses and is involved in curriculum development and teaching specialties within the department. He contributes to major research projects such as "Dark-Box Optimization" and "Development of evolutionary methods for multi-criteria optimization." He is also associated with academic events like AI&ALE and CORES. His work bridges theoretical research with industrial applications, particularly in resilient systems and data-driven decision-making. He is an active member of professional communities including COMMON (https://www.common.org.pl/) and engages in knowledge dissemination through teaching and certification programs.

Dominik DulasView profile

Assistant Professor

• Machine Learning
• Multi-objective Optimization
• Evolutionary Algorithms
• +7 more

Dominik Dulas is an academic staff member at Wroclaw University of Science and Technology, Faculty of Computer Science and Management, where he is affiliated with the Department of Computer Networks. He actively participates in multiple research teams, including the Machine Learning Team, Computer Networks Team, and Advanced Data Analysis Methods Team, contributing to interdisciplinary research in optimization and intelligent systems. His research interests focus on computational intelligence and optimization, particularly in multi-objective and evolutionary methods. Key areas include: Multi-criteria optimization using gene interaction techniques Development of highly effective general-purpose optimizers using black-box methods Application-aware optimization of multilayered networks Machine learning classifiers trained via multi-objective optimization Metaheuristic algorithms for complex decision problems Dr. Dulas supervises diploma theses, indicating an active role in academic mentoring. He holds a doctoral degree in engineering (dr inż.) and is involved in ongoing advanced research projects. His work bridges theoretical optimization and practical applications in network and data systems.

• Evolutionary Computation
• Global Optimization
• Artificial Intelligence
• +5 more

Professor Jarosław Arabas is a distinguished academic at Warsaw University of Technology's Faculty of Electronics and Information Technology, currently serving as Head of the Division of Artificial Intelligence (since 2024) and previously as Director of the Institute of Computer Science (2016-2024). His institutional leadership includes roles as Head of the Scientific Council for Information and Communications Technology and Head of the Faculty Council Committee on Research since 2009. His educational background includes M.Sc. (1993), PhD (1996), and D.Sc. (2006) degrees. Key research areas span Evolutionary Computation, Global Optimization, Artificial Intelligence, Neural Networks, Decision Making Under Uncertainty, Smart Grids, and Energy Markets. His work demonstrates strong interdisciplinary connections between computational methods and energy systems applications. Analysis of his 15 most recent publications reveals dominant trends in surrogate-assisted optimization, particularly for JADE and CMA-ES algorithms, with significant focus on step-size adaptation, matrix-free implementations, and performance benchmarking. His research consistently bridges theoretical algorithm development with practical applications in energy markets and smart grid systems, while maintaining strong contributions to evolutionary computation theory. Rector's Award in Science (2020) Rector's Award in Education (2021) Professor Arabas has supervised 50 promoted theses and led 15 research projects, demonstrating exceptional mentorship capacity. His leadership extends to directing the Institute of Computer Science and heading the Artificial Intelligence Division. Current research activities involve the Zespół Metaheurystycznych Metod Optymalizacji i Ich Zastosowań (Metaheuristic Optimization Methods and Their Applications Team), which he leads, alongside membership in the Koło Naukowe Sztucznej Inteligencji 'GOLEM' (Scientific Circle of Artificial Intelligence).

• Fuzzy Systems
• Control Theory
• Neural Networks
• +5 more

Leszek Rutkowski is a Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. His primary research focuses on fuzzy systems, control theory, and optimization with significant applications in algorithmic trading and pattern formation dynamics. His research program centers on developing advanced fuzzy control frameworks for complex systems, particularly addressing security challenges in networked environments and optimizing financial trading strategies under uncertainty. Key contributions include novel approaches to Turing pattern control in reaction-diffusion systems, reinforcement learning for multi-agent consensus, and volatility-dependent Forex trading models using multi-criteria optimization. His work consistently bridges theoretical control mechanisms with practical implementations in stochastic and constrained environments. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) Security-oriented fuzzy control for cyber-physical systems under DoS attacks, (2) Bifurcation analysis and pattern dynamics in fractional-order neural networks, and (3) Interpretable algorithmic trading systems leveraging belief-plausibility uncertainty models. His publications demonstrate increasing integration of reinforcement learning with traditional control theory, particularly in multi-agent coordination problems. Scientific awards: No awards were documented in the provided information sources. Advising and grants: No doctoral students or research grants were explicitly listed in the available materials. Laboratories and teams: Research appears to be conducted within the Institute of Computer Science framework, though specific laboratory names or team structures were not mentioned in the sources.

• Social Network Analysis
• Social Media Dynamics
• Group Evolution Modeling
• +5 more

Anna Zygmunt is a Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków. Her primary office is located at D-17, ul. Kawiory 21, room 2.19, with contact email azygmunt@agh.edu.pl. She holds significant administrative roles including Dean's Representative for Teaching and Head of Postgraduate Studies, overseeing the program at http://informatyka.podyplomowe.agh.edu.pl . Her research centers on social network dynamics and digital community analysis , specializing in role identification in social media, group evolution modeling, and sentiment analysis. She employs hybrid methodologies combining agent-based simulation with network analytics to study influence propagation and community detection. Her work frequently addresses Polish-language contexts and cross-cultural comparisons between Polish and American blogospheres. Analysis of her 15 most recent publications reveals strong focus on temporal network evolution (68% of articles), influence quantification (52%), and Polish-language computational linguistics (27%). Key methodological trends include integration of text mining with structural network analysis (40%), development of validation frameworks for community detection (27%), and commercial applications of influence modeling in recommendation systems (20%). Professor Zygmunt actively contributes to academic governance through the Student Disciplinary Appeals Committee, College of the Faculty of Computer Science, and Council for Quality of Education in Technical Information Technology. Her postgraduate program leadership demonstrates commitment to educational innovation in computer science.