جست‌وجوی استاد و پژوهشگر

Paweł Myszkowskiمشاهده پروفایل

استاد

• Evolutionary Computation
• Metaheuristics
• Multi-objective Optimization
• +۷ مورد دیگر

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 Panekمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Multi-criteria Optimization
• +۷ مورد دیگر

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

Dominik Dulasمشاهده پروفایل

استادیار

• Machine Learning
• Multi-objective Optimization
• Evolutionary Algorithms
• +۷ مورد دیگر

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.

• Autonomous Systems
• User Modeling
• Personalization
• +۳ مورد دیگر

Marek Piasecki is a researcher at the Department of Computer Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His work focuses on software development for autonomous systems, user modeling, personalization, and mobile computing. His research in autonomous systems includes classical and reactive navigation, localization, and path planning for mobile robots, particularly in the RoboCup competition. He has extensively explored automatic recommendation systems and software development for mobile devices, blending both Android and iOS platforms. The articles he has published demonstrate strong engagement with robotics, autonomous systems, and mobile computing. Key areas include evolutionary algorithms, differential mapping, IoT applications, and educational robotics (LEGO Mindstorms). These works span theoretical development, software implementation, and pedagogical approaches. In teaching, he specializes in C++, C, and Java programming languages, object-oriented programming, and software engineering principles. He also leads seminars on constructing mobile robots and supervises diploma projects involving mobile robotics and software development.

Michał Przewoźniczekمشاهده پروفایل

استاد

• Evolutionary Computation
• Multi-objective Optimization
• Many-objective Optimization
• +۱۲ مورد دیگر

Michał Przewoźniczek is a Professor at Wrocław University of Science and Technology (PWr), actively contributing to the fields of evolutionary computation, optimization, and artificial intelligence. He is a key member of multiple research teams, including the Metaheuristics Team, Machine Learning Team, and Advanced Data Analysis Methods Team, reflecting his broad interdisciplinary impact. His research focuses on Evolutionary Computation , Multi- and Many-objective Optimization , Linkage Learning , Problem Decomposition , and Hybridization of metaheuristic methods, with applications in industrial process planning, network optimization, and real-world decision-making. He leads significant research projects such as greybox optimization (2023–2026) and multi-objective evolutionary methods with linkage learning (2021–2026), demonstrating sustained grant support and scientific leadership. The analysis of his recent publications reveals a consistent trend in advancing parameter-less evolutionary algorithms, empirical linkage learning, and scalable distributed implementations using containerization (e.g., Docker). His work bridges theoretical algorithm development with practical applications in optical networks, manufacturing, and distributed computing. His scientific honors include: Best Paper Nomination at the Genetic and Evolutionary Computation Conference (GECCO) Associate Editor, IEEE Transactions on Evolutionary Computation (since January 2023) Prof. Przewoźniczek supervises graduate students as a thesis advisor and collaborates extensively with researchers such as Piotr Dziurzanski, Marcin Komarnicki, Krzysztof Walkowiak, and Leandro Soares Indrusiak. He is actively involved in major conferences like GECCO, CEC, and CORES, and his work is indexed in Google Scholar, ResearchGate, and ORCID (0000-0003-2446-6473). He is affiliated with research groups focusing on Machine Learning, Computer Networks, Advanced Data Analysis, and Metaheuristics, indicating a strong collaborative and team-based research environment.

Mariusz Topolskiمشاهده پروفایل

استاد

• machine learning
• pattern classification
• feature extraction and selection
• +۱۲ مورد دیگر

Mariusz Topolski is a Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology. He received his habilitated doctor degree in Computer Science in 2024 and has been actively contributing to research and teaching in machine learning, data analysis, and optimization. He is a member of multiple research teams including the Machine Learning Team (ZUM), Advanced Data Analysis Methods Team, and Metaheuristics Team. Education: Habilitated Doctor in Computer Science, Wrocław University of Science and Technology, 2024 PhD in Technical Sciences (Computer Science), Wrocław University of Science and Technology, 2008 Master's and Engineering Degree in Technical Physics (Biomedical Engineering), Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 2003 His research interests include machine learning, pattern classification, feature extraction, explainable AI, multi-objective optimization, and data mining. His work applies advanced computational techniques to real-world problems in medicine, construction, pharmaceuticals, and logistics. He has developed novel methods in PCA modification, feature selection, and signal analysis, particularly for diagnostic applications in neurology and oncology. The recent publications reflect a strong trend in applying machine learning and optimization to medical diagnostics and industrial risk modeling. Key areas include PCA-based classification, multi-objective feature selection, and hybrid signal processing techniques. His interdisciplinary work bridges computer science with healthcare and civil engineering, demonstrating the versatility of AI in solving complex, multi-domain challenges. Scientific Awards: Degree of Habilitated Doctor in Computer Science (awarded February 28, 2024) Mariusz Topolski has led and participated in numerous national and international research projects, including collaborations with Jagiellonian University Collegium Medicum, Medical University of Warsaw, Gdańsk Medical University, and foreign institutions such as the National Mining University of Ukraine and Fundación ICIL in Spain. He supervises diploma theses and contributes to academic training through workshops and lectures. He is also engaged in industrial applications, developing optimization and machine learning methods for supply chain logistics. Research Teams: Machine Learning Team (ZUM) Advanced Data Analysis Methods Team Metaheuristics Team Computer Networks Team Teaching Team

Joanna Klikowskaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Multi-objective Optimization
• Imbalanced Data Classification
• +۷ مورد دیگر

Dr. Eng. Joanna Klikowska is a researcher at the Department of Computer Systems and Networks, Faculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology. She is actively involved in the Machine Learning Team and contributes to key research projects such as MOO, IDSTREAM, and Dark-Box Optimization. Her research focuses on machine learning, particularly in the areas of imbalanced data classification, ensemble learning, and multi-objective optimization. She applies evolutionary and optimization techniques to improve classifier performance in complex decision-making tasks. The recent publications highlight a strong trend in using multi-objective optimization for training classifiers and feature selection, especially in challenging data environments. Her work bridges theoretical optimization methods with practical applications in data classification and stream learning. Involved in projects: swarog, moo, idstream She supervises diploma theses and participates in teaching activities. She is also affiliated with scientific platforms including Google Scholar, ResearchGate, ORCID, and PBN.

Amgad Elsayedمشاهده پروفایل

پژوهشگر

• Machine Learning
• Ensemble Learning
• Optimization
• +۷ مورد دیگر

Amgad Elsayed is a researcher involved in advanced computational and optimization projects, affiliated with research teams focusing on Machine Learning, Computer Networks, and Metaheuristics. He contributes to key initiatives such as Dark-Box Optimization and multi-criteria evolutionary methods. His research interests span Machine Learning , Ensemble Learning , and Optimization , with a strong emphasis on developing novel algorithms for complex decision-making and network optimization tasks. His work integrates advanced data analysis and evolutionary computation techniques. The research projects he is involved in suggest a focus on high-impact computational intelligence, particularly in multi-objective optimization and classifier development. These efforts align with modern challenges in AI and intelligent systems. There are no scientific awards listed in the available information. Amgad Elsayed has been involved in supervising diploma theses, though specific student names are not provided. There is no mention of research grants. He is actively involved in multiple research teams, including the Machine Learning Team, Teaching Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team, indicating a collaborative and multidisciplinary research profile.

Olha Matsyiمشاهده پروفایل

استادیار

• Operations Research
• Mathematical Optimization
• Computer Science
• +۳ مورد دیگر

Olha Matsyi serves as an Assistant Professor (post-doc) within the Division of Applications of Contemporary Mathematical Analysis at Lodz University of Technology, with contact details including email olha.matsyi@p.lodz.pl and phone (+48) 42 631-36-17. Her research focuses on Operations Research and Mathematical Optimization, specializing in combinatorial algorithms for location theory, knapsack problems, community detection, and VLSI routing. She employs metaheuristic and bio-inspired methods to solve NP-hard optimization challenges, bridging theoretical mathematics with engineering applications in healthcare logistics and crisis management. Publications from 2020-2025 demonstrate increasing emphasis on real-world implementations like mobile medical service optimization and decision support systems, alongside theoretical advances in continuous coverage and constrained classification. Collaborative work with researchers such as Oksana Pichugina highlights her interdisciplinary approach to algorithm design. No scientific awards were documented. Information regarding student mentorship, research grants, or specific laboratory teams was not provided in the source material.

• Multi-Criteria Decision Making
• Operations Research
• Optimization Algorithms
• +۵ مورد دیگر

Grzegorz Filcek is a Researcher at the Department of Computer Science and Systems Engineering , Faculty of Information and Communication Technology , Wrocław University of Science and Technology, Poland. His work focuses on multi-criteria optimization , evolutionary algorithms , and scheduling with applications in transportation systems , logistics , and industrial automation . Email: grzegorz.filcek@pwr.edu.pl Office: Building C-3, Room 14, Janiszewskiego 11/17, 50-372 Wrocław His research spans multi-criteria decision-making , location-scheduling integration , and transportation network optimization . Recent work includes frameworks for Pareto optimal solutions in MIP and rescheduling trains under track closures . He also explores evolutionary computation for freight parking planning and carpooling systems . Key trends in his publications include multi-criteria optimization (2019-2024), evolutionary algorithms (2020-2021), and transportation/logistics applications (2016-2023). His work often combines mathematical programming with real-world problem-solving in supply chains , railways , and emergency power systems .

Piotr Szwedمشاهده پروفایل

مدرس

• Data Mining
• Software Engineering
• Computational Intelligence
• +۵ مورد دیگر

Dr inż. Piotr Szwed is a Lecturer in the Department of Applied Computer Science at AGH University of Science and Technology, Kraków. He is affiliated with the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering (EAIiIB), where he teaches courses ranging from imperative and object-oriented programming to data exploration and computational intelligence. Research Interests: Data mining and knowledge discovery Fuzzy cognitive maps and their applications in transportation, risk assessment, and security Software engineering methodologies, including agile and ontology-driven development Intelligent transportation systems (ITS) and real-time traffic management Natural language processing for Polish texts, stylometry, and authorship attribution Ontology engineering, enterprise architecture verification, and model checking His work often bridges theoretical computer science with practical applications in urban mobility, safety systems, and enterprise software. Scientific Contributions: Pioneered the integration of fuzzy cognitive maps with evolutionary algorithms to predict traffic flows. Developed rule-based and machine-learning approaches to determine speed limits from geospatial data. Contributed to the INSIGMA intelligent transportation system aimed at enhancing urban mobility in Polish cities. Created formal verification techniques for ArchiMate business processes using NuSMV model checking. Advanced stylometric analysis for Polish texts, introducing part-of-speech features for authorship attribution. Teaching & Academic Service: Regularly teaches Programming (C, C++, Java), Software Engineering, Data Exploration, and Computational Intelligence. Supervises engineering and master theses in applied computer science. Maintains a public wiki with course materials, lab exercises, and consultation schedules for students. Utilizes Git-based repositories to streamline code submission and continuous assessment in laboratory classes. Laboratory & Facilities: He is located in building C-2, room 403, at AGH’s main campus, al. Mickiewicza 30, 30-059 Kraków. The lab supports courses that emphasize practical software development, version control, and collaborative project work.

Joanna Szłapczyńskaمشاهده پروفایل

دانشیار

• Maritime Navigation
• Collision Avoidance
• Ship Routing
• +۳ مورد دیگر

Joanna Szłapczyńska is an Associate Professor at the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. Her contact details include email joanna.szlapczynska@pg.edu.pl and phone 583471702. Her research spans maritime navigation safety and software architecture, focusing on collision avoidance systems, ship routing optimization, and microservices design. She employs multi-objective evolutionary algorithms and decision support frameworks to address challenges in maritime operations and software engineering, with emphasis on environmental sustainability and practical implementation. Analysis of her 2024-2025 publications reveals integration of high-performance computing with real-world maritime practices, including declarative ship arena modeling, fishing route optimization for emission reduction, and empirical studies of microservices adoption patterns. Her work bridges theoretical optimization with industry applications in shipping and IT sectors.

Michał Kucharzakمشاهده پروفایل

پژوهشگر

• Optimization
• Metaheuristics
• Computer Networks
• +۱۵ مورد دیگر

Michał Kucharzak is an active researcher engaged in multiple interdisciplinary research teams including the Machine Learning Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. He contributes to significant research projects such as Dark-Box Optimization and the development of evolutionary methods for multi-criteria optimization. His work focuses on optimization techniques applied to complex systems, particularly in network design and decision-making models. His research interests span a wide range of topics in computational optimization and network technologies. These include metaheuristics, linear and integer programming, constraint programming, modeling and simulation, and advanced data analysis methods. He also investigates computer network architectures such as P2P, EON, FA, FCA, and NB-IoT, with a focus on application-aware and multi-layer network optimization. The trends in his research, though not supported by explicit publications in the provided text, indicate a strong emphasis on algorithmic innovation for solving high-dimensional, multi-objective problems in both theoretical and applied domains. His work bridges operations research, computer science, and engineering applications. Involved in the AEON research project Active contributor to interdisciplinary research initiatives There is no available information on formal advising roles or grant funding in the provided text. However, his involvement in multiple research teams suggests collaborative engagement across projects. He maintains a scientific presence through profiles such as ORCID, indicating ongoing scholarly activity. Michał Kucharzak is part of dynamic research groups focused on cutting-edge computational methods. These teams work on developing novel optimizers, improving classifier training via multi-criteria approaches, and advancing evolutionary computation techniques for real-world applications in networking and decision support systems.

Dominik Żelaznyمشاهده پروفایل

پژوهشگر

• Scheduling
• Evolutionary Algorithms
• Multi-objective Optimization
• +۳ مورد دیگر

Dominik Żelazny is a researcher at the Faculty of Information and Communication Technology, Department of Computer Engineering, Wrocław University of Science and Technology. He focuses on scheduling algorithms and multi-objective optimization using evolutionary computation methods. Current position: Researcher Primary affiliation: Department of Computer Engineering Research interests include: Development of evolutionary algorithms for scheduling problems Multi-criteria and multi-objective optimization GPU-accelerated computing using CUDA Parallel and distributed algorithm design Industrial applications of operational research

• Game Theory
• Evolutionary Algorithms
• Artificial Immune Systems
• +۶ مورد دیگر

Paweł Jarosz serves as an Assistant Professor in the Department of Computer Science at the Faculty of Computer Science and Telecommunications , Cracow University of Technology. His work bridges computational intelligence with engineering applications. Academic Rank: Assistant Professor Department: Computer Science Faculty: Computer Science and Telecommunications University: Cracow University of Technology Research focuses on game theory, evolutionary algorithms, and multi-objective optimization, particularly in: Development of immune-inspired game-theoretic optimization methods Multi-criteria decision-making systems MEMS design optimization Biologically-inspired algorithmic frameworks Sustainable transportation modeling Distributed computing environments His publications (2010-2024) demonstrate sustained research in combining game theory with artificial immune systems for complex optimization challenges across various domains including microactuator design and spatial data analysis.