جستجوی استاد راهنمای دانشگاهی

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

• Network Optimization
• Machine Learning
• Traffic Prediction
• +۷ مورد دیگر

Paweł Trajdos is a researcher at the Department of Systems and Computer Networks within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on machine learning, multi-label classification, and ensemble systems, with applications in biomedical engineering and pattern recognition. Over the years 2015–2018, he co-authored significant publications on classifier chains, fuzzy confusion matrices, and hybrid learning models. Research Interests: Multi-label learning frameworks Fuzzy logic in classifier evaluation Ensemble system design Bioprosthetic control algorithms Dynamic data processing Decision-theoretic optimization

• Machine Learning
• Pattern Recognition
• Artificial Intelligence
• +۳ مورد دیگر

Dariusz Adam Żelasko is an Assistant Professor in the Department of Computer Science at Cracow University of Technology. His research focuses on Quality of Service (QoS) management in computer networks, particularly through the development of the Pay&Require multi-agent system. His work integrates machine learning with network management, developing ensemble classifiers and genetic algorithm-neural network hybrids for transmission quality prediction. Research validates concepts through NS3 network simulations and addresses routing optimization in dynamically configured networks. Recent publications demonstrate innovations in decentralized routing protocols, variable cost algorithms, and QoS assurance mechanisms using electronic auction systems. Applications extend to IoT parking systems and motor fault detection using computational intelligence methods. Teaching interests include network programming with GPS data and policy-based routing implementations.

• Computer Networks
• Quality of Service
• Multi-Agent Systems
• +۲ مورد دیگر

Prof. Robert Burduk is a full-time professor at the Faculty of Information and Communication Technology , Wrocław University of Science and Technology , specializing in artificial intelligence and machine learning. His research focuses on supervised classification, ensemble learning, and decision support systems with applications in photovoltaic energy forecasting, insect rearing monitoring, and software bug assignment. Key research areas: Artificial Intelligence, Machine Learning, Business Intelligence Department: Systems and Computer Networks Recent Publications (2024-2023): Burduk's work addresses classifier fusion, photovoltaic power optimization, and agricultural monitoring using advanced AI techniques like convolutional neural networks and domain shift detection. Notable themes include ensemble learning strategies, energy system modeling, and interdisciplinary applications.

• Artificial Intelligence
• Machine Learning
• Supervised Classification
• +۳ 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

• machine learning
• pattern classification
• feature extraction and selection
• +۱۲ 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.

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

Joanna Komorniczak is an Assistant Professor in the Department of Computer Systems and Networks at the Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology. She is actively engaged in research and teaching, with a focus on machine learning and data stream analysis. Research Interests: Data stream processing and concept drift detection Dataset and problem complexity in supervised learning Open set recognition and imbalanced data classification Development of machine learning software tools and libraries Her recent work centers on challenges in dynamic and evolving data environments, particularly in the context of real-world data stream applications. She contributes to both algorithmic development and practical implementations, as evidenced by her open-source library problexity . Publication Trends: Her publications from 2021–2023 focus on detecting and adapting to concept drift, managing imbalanced data streams, and assessing learning problem complexity. These works appear in reputable venues such as Knowledge-Based Systems , Neurocomputing , and IJCNN, indicating strong engagement with the machine learning research community. Scientific Awards: Winner of the 5th Secundus Competition She is involved in significant research projects including IDSTREAM and MOO , and is a member of the Machine Learning Team and Advanced Data Analysis Methods Team. She supervises student theses and teaches courses such as Databases and Methods of Artificial Intelligence. Her work combines theoretical rigor with practical implementation, particularly in Python-based environments using tools like scikit-learn and stream-learn.

• Data Stream Processing
• Concept Drift Detection
• Dataset Complexity Analysis
• +۷ مورد دیگر

Dr. Lyudmyla Kirichenko serves as a Research and Teaching Assistant Professor in the Insurance and Capital Markets Department at Lodz University of Technology, Poland. Her academic profile includes active research publication through 2025, with contact details listing room 167 and email lyudmyla.kirichenko@p.lodz.pl. She maintains collaborative research ties with institutions in Ukraine and international conferences. Her research spans Machine Learning, Time Series Analysis, and Cybersecurity with specialized focus on fractal dynamics and computational finance applications. She develops advanced methodologies including wavelet transforms, recurrence plots, and LSTM autoencoders for classifying complex time series data. Her work bridges theoretical computer science with practical implementations in intrusion detection systems, financial market analysis, and computer vision security applications. The departmental affiliation with Insurance and Capital Markets indicates applied research directions in e-commerce forecasting and risk modeling. Analysis of her 15 most recent publications (2022-2025) reveals dominant trends in machine learning classification of fractal time series, with increasing interdisciplinary applications. Her 2023-2025 work shows expansion into public health modeling (respiratory infections in conflict zones) and crisis response optimization, while maintaining core expertise in wavelet-based feature extraction and neural network approaches for time series problems. The publications demonstrate consistent methodological innovation across computer science, physics, and operations research domains. Scientific Awards No scientific awards were documented in the provided materials Advising and grant activities cannot be confirmed from the available information. The publication record indicates extensive collaboration with Ukrainian researchers including Tamara Radivilova and Sergiy Yakovlev, but no student supervision or funding details are specified. Her research appears supported through institutional affiliations and conference participations. Laboratory resources or dedicated research teams are not explicitly described, though her methodology-focused publications suggest computational laboratory work involving machine learning frameworks and signal processing tools. The departmental context implies integration with financial data analysis infrastructure.

• Machine Learning
• Time Series Analysis
• Data Mining
• +۴ 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.

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

Jakub Klikowski is an Assistant Professor in the Department of Computer Systems and Networks at the Faculty of Electronics, Wrocław University of Science and Technology. He has been employed since October 2018 and earned his PhD in engineering and technical sciences in March 2022 for his dissertation on ensemble methods for imbalanced data stream classification. He is actively involved in research and teaching, contributing to multiple research teams including the Machine Learning Team and Advanced Data Analysis Methods Team. Research Interests: Classification of imbalanced and streaming data Ensemble learning and one-class classification Natural language processing (NLP) Concept drift detection and adaptive learning Graph neural networks and spam/disinformation detection Multi-criteria optimization and evolutionary algorithms His research is primarily conducted within the IDSTREAM and GEOM projects, focusing on developing robust classifiers for challenging data environments. His recent work emphasizes hybrid preprocessing, ensemble weighting, and drift detection mechanisms. Publication Trends: His publications from 2019 to 2022 reveal a consistent focus on improving classification performance in imbalanced data streams using ensemble techniques, preprocessing strategies, and drift detection. Key themes include weighted bagging, Hellinger distance, centroid analysis, and genetic optimization, all applied to real-world decision-making tasks. Scientific Awards: Rector of Wrocław University of Science and Technology Award (2020) Distinction in the Secundus program for young scientists (2023) Honorable Mention for doctoral dissertation (2022) Advising and Grants: Dr. Klikowski supervises diploma theses on topics such as NLP, text summarization, hate speech detection, and graph neural networks. He is involved in research projects including IDSTREAM and GEOM, which support his work in data stream classification and optimization. He collaborates with leading researchers like Prof. Michał Woźniak and Prof. Robert Burduk. Labs and Teams: He is an active member of the Machine Learning Team, Advanced Data Analysis Methods Team, and other research groups at the Department of Computer Systems and Networks. These teams foster interdisciplinary research in AI, data science, and network optimization.

• Machine Learning
• Data Stream Classification
• Imbalanced Data
• +۱۲ مورد دیگر

Przemysław Rokita is a full-time Professor at the Institute of Computer Science within the Faculty of Electronics and Information Technology at Warsaw University of Technology. He holds a PhD (1993), DSc (2000), and has been a Professor since 2014. His work spans computer graphics, image processing, and machine learning applications in particle physics simulations. MSc (1985), PhD (1993), DSc (2000) Academic roles: Professor (2014-present), Professor Assistant (2000-2014) Rokita's research interests include: Computer graphics algorithms and visualization Digital image processing techniques Machine learning for detector simulations High-energy physics data analysis Interactive software tools for scientific computing His recent publications focus on AI-driven particle collision simulations , real-time data visualization , and deepfake detection . He has collaborated with institutions in Germany, Japan, and the UK, including Max-Planck-Institut für Informatik and The University of Aizu. Scientific Awards: Minister of Education Award (1995) Rector’s Award in Science (2001, 2019) Golden Chalk Distinction for teaching excellence (2005, 2006) Professional Leadership: Head of Computer Graphics Division (2013-present) Member of Faculty Council and its Scientific Research Committee Active in program committees for IEEE, ACM Siggraph, and Eurographics

• Computer Science
• Digital Image Processing
• Computer Graphics
• +۳ مورد دیگر

Prof. Olgierd Unold is a faculty member at the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, where he is affiliated with the Department of Computer Engineering. He holds the academic rank of Professor and conducts research in machine learning, computational intelligence, and bioinformatics. Research Interests: Machine Learning, particularly Grammatical Inference Computational Intelligence: Evolutionary Algorithms, Learning Classifier Systems, Fuzzy Rule Systems Bioinformatics and DNA Computing Natural Language Processing and Pattern Recognition His recent publications demonstrate a strong focus on applying AI techniques to bioinformatics, log analysis, formal language inference, and optimization problems. The work spans from theoretical algorithm development to practical applications in software reliability and biological data classification. Scientific Awards: No awards listed in the provided text. Advising and Grants: Prof. Unold has supervised or collaborated with numerous researchers and students, including Wojciech Wieczorek, Norbert Kozłowski, and Łukasz Śmierzchała, on projects involving classifier systems, grammatical inference, and bioinformatics. While specific grants are not mentioned, his consistent publication output suggests active research funding. Labs and Research Teams: He is part of the research ecosystem within the Department of Computer Engineering, contributing to projects in AI, machine learning, and computational intelligence, likely involving student-led and collaborative research initiatives.

• Machine Learning
• Grammatical Inference
• Computational Intelligence
• +۷ مورد دیگر

Professor Andrzej Kasprzak is a faculty member at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, serving as Head of the Department of Systems and Computer Networks. His research spans machine learning, network optimization, and data stream processing, with applications in telecommunications, healthcare, and industrial systems. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Department of Systems and Computer Networks Email: andrzej.kasprzak@pwr.edu.pl Kasprzak’s research focuses on integrating machine learning with network optimization, particularly in elastic optical networks and data stream environments. His work addresses challenges in multi-agent pathfinding, fake news detection, and 3D printing automation, emphasizing practical experimentation systems. Recent publications highlight trends in ensemble learning for real-time data streams, domain adaptation in AI systems, and innovative optimization algorithms for network traffic and scheduling problems. These contributions bridge theoretical advancements with industrial applications. As Head of Department, Kasprzak leads initiatives in systems and computer networks, fostering interdisciplinary collaboration in artificial intelligence and telecommunications. His career reflects a commitment to advancing computational methodologies in complex environments.

• Machine Learning
• Computer Science
• Network Optimization
• +۳ مورد دیگر

Dariusz Jankowski is a researcher at the Department of Systems and Computer Networks within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on machine learning, data stream mining, and big data analysis with applications in medical diagnostics and industrial processes. Email: dariusz.jankowski@pwr.edu.pl Office: Room P.2.9, Building C-16, Wrocław Office Hours: Tuesday 11-13, Thursday 13-15 Research interests include machine learning, data stream classification with concept drift, and big data analysis. He has applied these techniques to medical diagnosis (migraine) and industrial applications (dental milling, cotton textile industry). Recent publications focus on decision tree algorithms for data streams, random forest optimization with MapReduce, hyperspectral image segmentation, and ensemble classifier systems. His work spans both theoretical machine learning and practical implementations across diverse domains. Current affiliations include participation in academic conferences like IDEAL, CORES, and HAIS. While no awards are explicitly mentioned, his collaborative publications suggest active involvement in research networks.

• Machine Learning
• Data Stream Mining
• Data Stream Classification
• +۳ مورد دیگر

Piotr Sobolewski is an academic researcher at the Wrocław University of Science and Technology , affiliated with the Faculty of Information and Communication Technology and working in the Department of Systems and Computer Networks . His research focuses on: Cognitive processes during gaming Neurochemical responses in players AI-driven game experience personalization Concept drift detection in dynamic environments Statistical methods for adaptive systems Key trends in his recent publications (2010-2014) include: Development of algorithms for concept drift detection Applications of ensemble learning techniques Statistical approaches to streaming data analysis Exploration of AI's potential in game design His work bridges computational methods with cognitive science, particularly in gaming contexts.

• Cognitive Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر