جست‌وجوی استاد و پژوهشگر

• Spatial Data Infrastructure (SDI)
• GIS Applications
• Machine Learning in Geoinformatics
• +۳ مورد دیگر

Iwona Kaczmarek is a Professor in the Department of Systems Research at the Wrocław University of Science and Technology's Faculty of Spatial Management and Landscape Architecture. Her work focuses on advancing spatial data infrastructure (SDI), geoinformatics, and machine learning applications in urban and regional planning. She leads interdisciplinary research integrating GIS, natural language processing (NLP), and graph neural networks to address challenges in spatial planning, policy analysis, and socio-economic modeling. Research interests include: Spatial data integration and interoperability Machine learning for geospatial analysis Semantic web technologies for open data Urban sustainability and transit-oriented development Policy text mining and stakeholder analysis Recent work emphasizes: 1. Combining media sentiment analysis with economic indicators during crises (2025) 2. GeoAI-driven quality of life predictions using graph neural networks (2023) 3. Knowledge graph extraction from spatial development plans (2023) 4. Harmonizing local planning documents via NLP (2022) Collaborations include projects like the EYE initiative analyzing remote sensing for economic trends and SDI implementations in academic contexts. Over 75 documented outputs highlight her contributions to geospatial innovation and policy-relevant research.

Krzysztof Brzostowskiمشاهده پروفایل

استاد

• Nonlinear Signal Processing
• Biomedical Signal Processing
• Time-Frequency Signal Representation
• +۳ مورد دیگر

Krzysztof Brzostowski, DSc, PhD, Eng. is a Professor at the Wrocław University of Science and Technology, affiliated with the Faculty of Information and Communication Technology. He holds a position in the Department of Computer Science and Systems Engineering, located at ul. Z. Janiszewskiego 11/17, building C-3, room 15. His research focuses on nonlinear signal processing, biomedical signal processing, time-frequency signal representation, sparsity techniques, system identification, and multi-sensor fusion. Brzostowski's work integrates advanced signal processing methodologies with practical applications in biomedical engineering, sensor networks, and data-driven decision systems. He has contributed significantly to noise reduction techniques, real-time signal fusion, and adaptive systems for physical effort planning. His research often bridges theoretical signal analysis with clinical and engineering applications, addressing challenges in electromechanical delay measurement, gait analysis, and medical imaging enhancement. Notable contributions include a monograph on signal processing in data stream fusion (2020) and pioneering studies on MRI denoising, gyroscope signal enhancement, and hybrid reality data sharing protocols. His articles span journals like Biomedical Signal Processing and Control , Applied Intelligence , and conference proceedings in systems engineering and artificial intelligence. Brzostowski's academic service includes office hours on Wednesday, Thursday, Friday, and Saturday mornings, and he maintains active participation in academic conferences and editorial roles. While no specific awards are listed, his extensive publication record reflects sustained excellence in interdisciplinary signal processing research.

• Information Security
• Digital Signal Processing
• Cybersecurity
• +۳ مورد دیگر

Grzegorz Kozieł is a Senior Lecturer at Lublin University of Technology, affiliated with the Faculty of Electrical Engineering and Computer Science and the Department of Computer Science. His research focuses on Information Security, Digital Signal Processing, and Cybersecurity, with additional interests in steganography, database systems, and network security. He has been involved in numerous projects, including the Integrated Development Programme of Lublin University of Technology and initiatives like Moodle ANMC and JCSI Resources. His academic contributions span over 50 publications, emphasizing comparative analyses of technologies such as smart home systems, programming languages, and database frameworks. Notable recent work includes studies on cybersecurity awareness among young learners, watermarking techniques for 3D models, and the application of Fourier transforms in sensor data analysis. Current projects include advancements in digital twin models for visually impaired individuals and participation in LubGame Conference 2025. Dr. Kozieł has received recognition through awards such as the Merit Medal for the City of Lublin (though explicitly awarded to departmental professors, it reflects his collaborative contributions). His teaching and research emphasize practical applications, including WordPress-based student projects and assessments of foreign language learning portals. He actively contributes to the development of IT education curricula, focusing on bridging academic training with industry needs.

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

Marian Chudy is a full Professor at the Faculty of Cybernetics of the Military University of Technology (WAT) in Warsaw. His academic position includes habilitation, indicating advanced scholarly credentials. He is affiliated with the editorial office of WAT Publishing House and maintains an office in building 100, room 254b. His research focuses on optimization algorithms, mathematical modelling, and their applications in military systems and computational problems. Key areas include weapon target assignment, attrition process control, fuzzy data analysis, and dimensionality reduction techniques. He has authored books such as Selected Optimization Algorithms (2014) and contributed to edited volumes like IT Systems for State Security (2019). His publication trends emphasize discrete optimization problems, algorithm design for 0-1 optimization, and error assessment in computational systems. Notable work includes developing polynomial procedures for specific optimization challenges and creating attrition models incorporating decoy effects. His research bridges theoretical optimization with practical applications in cybernetic systems and military strategy. No scientific awards or major grants are explicitly mentioned in the provided materials. He has advised no doctoral/master’s students listed here, though his publications suggest collaborative research activities. His work contributes to the development of computational tools for decision-making under uncertainty, with implications for national security systems and economic restructuring models.

• Econometrics
• Regional Development
• European Cohesion Policy
• +۴ مورد دیگر

Dr. Dorota Jegorow is an Assistant Professor at the Department of Econometrics and Statistics , Institute of Economics and Finance , within the Faculty of Social Sciences at the John Paul II Catholic University of Lublin , Poland. Specializes in Econometrics , Regional Development , and European Cohesion Policy Active in Entrepreneurship , Public Finance , and Digital Technology Applications in socio-economic contexts Recipient of 2022 National Award for research on cryptocurrency volatility

Andrzej Dudekمشاهده پروفایل

استاد

• Econometrics
• Data Analysis
• Machine Learning
• +۳ مورد دیگر

Prof. Andrzej Dudek is a faculty member at the Department of Econometrics and Informatics, Wrocław University of Economics – Jelenia Góra Branch. His research focuses on advanced data analysis methodologies, including symbolic data analysis, machine learning applications, and statistical modeling. Dudek has contributed to the development of R packages such as IFMCDM and mdsOpt, which support multi-criteria decision making and multidimensional scaling. His work addresses challenges in outlier detection, clustering, and predictive modeling across domains like tourism, healthcare, and finance. Recent studies explore consumer behavior shifts during the pandemic, AI-driven medical modeling, and robust regression techniques for noisy data. Key research areas include econometric modeling, computational statistics, and the integration of machine learning with traditional statistical methods. Dudek’s interdisciplinary approach spans from theoretical algorithm development to practical applications in industry and public health. He actively contributes to academic conferences and publishes in leading journals, demonstrating expertise in both methodological innovation and real-world problem-solving.

Izabela Michalska-Dudekمشاهده پروفایل

استاد

• Customer Loyalty
• Customer Relationship Management
• Consumer Behavior
• +۳ مورد دیگر

Dr. Izabela Michalska-Dudek is a Professor and Head of the Department of Marketing and Tourism Management at Wrocław University of Economics’ Jelenia Góra Branch. She specializes in customer loyalty mechanisms, consumer behavior in tourism, and digital transformation impacts on travel agencies. Her work emphasizes empirical studies on CRM systems, post-pandemic travel decision-making, and predictive modeling for loyalty programs. Research Interests: Customer Relationship Management (CRM), Virtualization in Tourism, Pandemic Impact Analysis, Retail Tourism (ROPO), and Machine Learning applications in loyalty analytics. She actively publishes in peer-reviewed journals and organizes international conferences like the 'Current Trends in Spa, Hotel and Tourism' series. Publications focus on analyzing socio-economic factors influencing travel behavior, crisis management strategies, and technological adoption in the tourism sector. Recent work explores how machine learning can uncover consumer loyalty motives and quantify pandemic-related behavioral shifts. No scientific awards listed. She supervises doctoral students and is open to socio-economic collaborations. Consultations available via MS Teams on Fridays.

Radosław Pietrzykمشاهده پروفایل

استاد

• Financial Risk Management
• Cryptocurrency Markets
• Econometrics
• +۳ مورد دیگر

Dr hab. Radosław Pietrzyk, prof. UEW, is a Professor at the Department of Financial Investments and Risk Management within the University of Economics in Wrocław. His research focuses on financial risk management, household financial planning, cryptocurrency markets, and econometric modeling. He specializes in developing integrated risk models for households, evaluating Value at Risk (VaR) forecasts, and analyzing market dynamics during crises such as the subprime mortgage collapse, the global pandemic, and geopolitical conflicts like the Russo-Ukrainian war. His work often intersects with practical applications, including stress testing financial instruments, optimizing investment portfolios, and assessing mutual fund performance in Polish and global markets. Pietrzyk’s methods emphasize statistical rigor, with contributions to Divisia Index applications for stock exchange analysis and multi-objective optimization frameworks for household financial goals. Recent articles highlight his exploration of green bonds’ governance effectiveness and cryptocurrency exchange rate volatility. His research frequently addresses longevity risk, risk aversion in retirement planning, and the integration of environmental factors into financial decision-making. Pietrzyk maintains active academic engagement, offering consultations via email (radoslaw.pietrzyk@ue.wroc.pl) and through Microsoft Teams. His consultancy hours are Monday 8:00-9:30 AM. He collaborates with external institutions and contributes to the academic community through platforms like ResearchGate and ORCID.

• LIDAR technology
• Point cloud analysis
• Big data algorithms
• +۹ مورد دیگر

Adrian Widłak is a Teaching Assistant at the Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology. His research focuses on information and communication technology (ICT) with specialization in point cloud analysis, visibility mapping, and network security. Specializes in LIDAR technology and Digital Surface Model (DSM) processing for viewshed generation Conducts research in big data algorithms, cryptography, and artificial intelligence Active in edge computing optimization and intelligent transportation systems His work spans multiple interdisciplinary domains including environmental monitoring, biomedical applications, and blockchain security. Recent publications demonstrate a focus on: 3D spatial analysis techniques using point cloud data Machine learning applications in ecological modeling Cybersecurity challenges in modern network architectures Optimization of edge computing infrastructures Blockchain consensus mechanisms and security frameworks

Michela Tauferمشاهده پروفایل

دانشیار

• Heterogeneous Computing
• Cloud Computing
• Volunteer Computing
• +۴ مورد دیگر

Michela Taufer is the David L and Beverly JC Mills Chair of Computer and Information Sciences and an Associate Professor at the University of Delaware. She earned her Masters degrees in Computer Engineering from the University of Padova (Italy) and her doctoral degree in Computer Science from the Swiss Federal Institute of Technology (Switzerland). Her research interests include scientific applications and their advanced programmability in heterogeneous computing (i.e. multi-core and many-core platforms, GPUs); performance analysis; modeling and optimization of multi-scale applications on heterogeneous computing; cloud computing and volunteer computing; numerical reproducibility and stability of large-scale simulations on multi-core platforms; and big data analytics and MapReduce. Dr. Taufer has published research on protein folding, molecular dynamics, and workflow execution on cloud and grid platforms. Her work often focuses on performance characterization and optimization of scientific applications on distributed and parallel systems. From 2003 to 2004 she was a La Jolla Interfaces in Science Training Program (LJIS) Postdoctoral Fellow at the University of California San Diego (UCSD) and The Scripps Research Institute (TSRI) From 2005 to 2007, she was an Assistant Professor at the Computer Science Department of the University of Texas at El Paso (UTEP) She joined the University of Delaware in 2007 as an Assistant Professor and was promoted to Associate Professor with tenure in 2012

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

Jacek Młynarskiمشاهده پروفایل

استاد

• Asymmetric Catalysis
• Organic Synthesis
• Artificial Intelligence in Chemistry
• +۳ مورد دیگر

Professor Jacek Młynarski is affiliated with the Institute of Organic Chemistry, Polish Academy of Sciences . He leads a research group focused on asymmetric catalysis, bioactive compound synthesis, and AI-driven reaction discovery. Asymmetric catalysis with zinc/magnesium Lewis acids AI-guided catalyst optimization Stereoselective synthesis of pharmaceuticals/natural products Organocatalysis and redox chemistry His recent work bridges artificial intelligence with asymmetric catalysis, enabling systematic discovery of reaction pathways. He specializes in chiral zinc/magnesium catalysts, organocatalytic Michael additions, and computational reaction planning. His group includes Prof. Sławomir Jarosz , Prof. Zbigniew Pakulski , and PhD student Paulina Baczewska . He holds the OPUS NCN 2021/41/B/ST4/01617 grant titled "Artificial Intelligence meets Asymmetric Catalysis: a new pathway for catalyst optimization and discovery" .

• Computer Science
• Artificial Intelligence
• Data Mining
• +۳ مورد دیگر

Krzysztof Heller serves as a research assistant 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 office is located at D-17, ul. Kawiory 21. His research spans foundational and applied computer science disciplines, with emphasis on: Artificial intelligence systems development Large-scale data mining methodologies Machine learning algorithm optimization Robust software engineering practices Secure computer network architectures Distributed computing frameworks Dr. Heller maintains active academic engagement through his institutional role and correspondence via kjheller@agh.edu.pl .

• Power Electronics
• Building Automation
• Energy Conversion Systems
• +۳ مورد دیگر

Marcin Jachimski serves as a lecturer at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. His academic role involves teaching and research focused on energy-efficient technologies and advanced control systems, with particular emphasis on practical applications in building automation and industrial processes. Dr. Jachimski holds both a PhD and an engineering degree (Eng.), establishing his foundation in electrical engineering principles. His educational credentials, while not detailed in available sources, underpin his expertise in power electronics and automation systems that drive his current academic work. His research spans multiple critical domains including Power Electronics for efficient energy conversion, Building Automation systems optimizing energy usage in structures, and Energy Conversion Systems for sustainable power management. He also investigates Electrical Energy Consumption monitoring techniques and develops advanced Control Systems for industrial applications, with growing interest in event-based data acquisition methods and energy-saving lighting technologies. Analysis of Dr. Jachimski's publication trends (2006-2020) reveals consistent focus on energy efficiency within building automation systems, particularly event-based electricity metering and LED/CFL lamp consumption analysis. His work demonstrates strong practical orientation through applications in lime processing control systems, industrial machine monitoring, and hardware implementations for safety functions, with increasing integration of LonWorks and TCP/IP technologies in building management solutions. No scientific awards or fellowships are documented in available sources, indicating his primary contributions occur through academic publications and teaching activities rather than formal recognitions. As a lecturer, Dr. Jachimski likely mentors undergraduate and graduate students, though specific advisees are not listed in available information. Details regarding research grants secured through competitive funding mechanisms are also absent from current documentation. His departmental affiliation suggests active participation in research teams developing energy conversion solutions and automation systems, though specific laboratory names or team structures are not explicitly mentioned in available materials. The department's focus implies collaborative work on industrial automation projects and energy monitoring systems.