جست‌وجوی استاد و پژوهشگر

Bogdan Pankiewiczمشاهده پروفایل

دانشیار

• Environmental Monitoring
• Sensor Technology
• Machine Learning
• +۳ مورد دیگر

Dr. Bogdan Pankiewicz serves as Associate Professor in the Department of Microelectronic Systems at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His research develops autonomous sensor platforms for real-time air pollution monitoring in urban environments. His primary research focuses on environmental sensor technology and machine learning applications for air quality assessment. Specializing in nitrogen dioxide (NO 2 ) and particulate matter (PM) monitoring, he pioneers calibration techniques for low-cost sensors to achieve laboratory-grade accuracy in community-based atmospheric studies. His work bridges microelectronics engineering with public health applications through cost-efficient measurement systems. Recent publications demonstrate consistent innovation in machine learning-based sensor calibration (2024-2026), featuring techniques like time-series alignment and global data correlation enhancement. These studies target real-world deployment of autonomous platforms like HAPADS for traffic-related pollution monitoring, emphasizing practical solutions for urban air quality management. Dr. Pankiewicz collaborates within a research team developing the HAPADS (Highly Accurate and Autonomous Programmable Platforms for providing Air Pollution Data Services) system, which integrates microelectronic sensor arrays with AI-driven calibration for public-facing pollution data services in Polish urban environments.

Andrzej Kwiatkowskiمشاهده پروفایل

استادیار

• Gas Sensing
• Nanomaterials
• Sensor Technology
• +۳ مورد دیگر

Andrzej Kwiatkowski serves as an Assistant Professor in the Department of Metrology and Electronic Systems at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His academic role centers on advancing gas sensing technologies through nanomaterials and innovative signal processing methodologies. His research expertise includes: Development of nanomaterial-based gas sensors (graphene, WS2, MoS2, Cu2O) Fluctuation-enhanced sensing via low-frequency noise analysis UV and visible light-activated sensor systems Machine learning integration for gas mixture identification Ink-printing fabrication techniques for sensor layers Atomic force microscopy for surface characterization Recent publications (2024-2025) reveal a concentrated research trajectory in light-activated gas sensing using two-dimensional materials. Key trends include leveraging UV irradiation to enhance sensor sensitivity for organic vapors, combining ink-printed nanomaterials with advanced noise analysis, and applying machine learning to interpret complex gas mixture responses. This work bridges materials science, electrical engineering, and data science to solve critical detection challenges. Scientific awards: None mentioned in available sources. Student advising and research grant details were not provided in the source materials.

Tomasz Bielińskiمشاهده پروفایل

استادیار

• Remote Sensing
• Hydrology
• Machine Learning
• +۴ مورد دیگر

Tomasz Bieliński serves as an Assistant Professor in the Department of Geoinformatics Systems within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic profile demonstrates strong expertise in applying remote sensing technologies to hydrological research problems, particularly in flood monitoring and coastal mapping. His research interests span multiple interconnected domains including Remote Sensing , Hydrology , Machine Learning , and Geospatial Analysis . Bieliński's work focuses on developing innovative methods for processing satellite imagery, with particular emphasis on Synthetic Aperture Radar (SAR) technology for environmental monitoring applications. Analysis of his publication record reveals consistent contributions to flood mapping methodologies, bathymetry estimation, and satellite image processing techniques. His work shows increasing sophistication in integrating machine learning approaches with traditional remote sensing methods, particularly evident in his 2023 publication on Sentinel-2 derived bathymetry. Bieliński actively contributes to research projects including the Marine Port Surveillance and Observation System (MPSs), where he serves as a senior software developer working with mobile unmanned research units under the Norwegian and EEA Funds - Applied Research Program.

Marek Wójcikowskiمشاهده پروفایل

دانشیار

• Microelectronics
• Sensor Systems
• Air Pollution Monitoring
• +۳ مورد دیگر

Dr. Marek Wójcikowski serves as Associate Professor and Head of the Department of Microelectronic Systems within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology, where he leads academic and research initiatives in microelectronics and sensor development. His research specializes in creating low-cost autonomous platforms for environmental monitoring, with emphasis on nitrogen dioxide (NO 2 ) and particulate matter (PM) detection in urban settings. He pioneers machine learning techniques for sensor calibration and correction, while extending expertise to radiation-tolerant electronics for space applications like satellite quantum key distribution systems. This interdisciplinary work bridges environmental science, embedded systems engineering, and data analytics to address critical air quality challenges affecting public health. Analysis of his 2024-2026 publications reveals consistent focus on real-world sensor deployment, machine learning optimization for field correction, and space-grade FPGA implementations. Key trends include urban air quality monitoring for drivers/public, integration of global data correlation methods, and development of cost-efficient measurement infrastructure targeting respiratory health impacts. Scientific Awards: No awards were documented in the source material. Advising and Grants: The provided text contains no information regarding graduate students, research grants, or funded projects under Dr. Wójcikowski's supervision. Labs and Teams: While his departmental leadership role implies oversight of Microelectronic Systems research infrastructure, specific laboratory names, team compositions, or collaborative groups are not disclosed in the available content.

Shahid Khanمشاهده پروفایل

استادیار

• Wireless Communications
• Antenna Design
• 5G/6G Networks
• +۵ مورد دیگر

Dr. Shahid Khan serves as an Assistant Professor in the Department of Microelectronic Systems at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His academic appointment is firmly situated within the engineering faculty, where he contributes to research and teaching in microelectronics and telecommunications systems. Research Interests: Dr. Khan's primary research spans wireless communications for 5G and beyond (B5G), with a concentrated focus on Internet of Things (IoT) applications. His work encompasses the design of advanced antenna systems including MIMO configurations for millimeter-wave frequencies, beam steering techniques, and the integration of machine learning for mobility management in autonomous vehicle networks. He also investigates electromagnetic structures such as frequency selective surfaces and metamaterials to enhance wireless coverage and reduce path loss in advanced communication systems. Publication Trends: His 2025 publications demonstrate a cohesive research trajectory centered on innovative solutions for 5G IoT infrastructure. Key themes include compact antenna array design for millimeter-wave bands, port isolation techniques using metamaterials, and machine learning algorithms for seamless handovers in vehicular networks. These contributions appear in high-impact journals including IEEE Internet of Things Journal and Scientific Reports, reflecting his specialization in advancing wireless communication technologies for next-generation applications. Scientific Awards: No specific awards, fellowships, or medals were mentioned in the provided information. Advising and Grants: The available text contains no details regarding graduate students supervised by Dr. Khan or research grants secured for his projects. His academic profile emphasizes publication output rather than mentoring or funding activities. Laboratory Affiliations: While specific laboratory facilities aren't explicitly detailed, his research on 3D-printed dielectric lenses and metamaterial integration suggests active involvement with antenna fabrication and testing facilities within the Department of Microelectronic Systems.

Michał Kassjańskiمشاهده پروفایل

مدرس

• Artificial Intelligence
• Hearing Impairment Diagnosis
• Medical Informatics
• +۲ مورد دیگر

Michał Kassjański serves as an Assistant lecturer at the Department of Geoinformation Systems within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. Based in room EA 329 of Building A, he maintains active research while fulfilling teaching responsibilities, with contact details michal.kassjanski@pg.edu.pl and phone 58 347 18 11. His research centers on Artificial Intelligence applications in medical diagnostics , specifically focusing on hearing impairment analysis through audiometry data. Key interests include developing AI-driven classification systems for hearing loss types, creating open-source mobile diagnostic tools, and optimizing patient referral pathways using machine learning techniques in audiology. Analysis of his publication trajectory reveals consistent innovation in AI-audiology integration, with emphasis on practical clinical implementations. His work demonstrates progression from foundational classification models (2022) to comprehensive evaluation frameworks (2024) and full mobile application development (2025), addressing critical gaps in accessible hearing diagnostics. No information regarding scientific awards, graduate student supervision, or research funding is documented in the available materials.

Henryk Krawczykمشاهده پروفایل

استاد

• Cloud Computing
• Internet of Things
• Artificial Intelligence
• +۴ مورد دیگر

prof. dr hab. inż. Henryk Krawczyk serves as a Professor at Gdańsk University of Technology within the Faculty of Electronics, Telecommunications and Informatics. He is based at the Task Computing Centre (Centrum Informatyczne Task) in the Building of Electronics, Telecommunications and Informatics (room 520), with contact email hkrawk@pg.edu.pl and phone 583471018. His research spans critical intersections of cloud infrastructure and computational intelligence: Intelligent cloud service engineering with resilience-focused development frameworks Rural IoT deployment solutions for underserved telecommunications environments Machine learning optimization for constrained edge devices GPU-accelerated computational biology simulations DevOps/CI/CD integration in cloud service lifecycles Analysis of his 2024-2025 publications reveals a strategic emphasis on practical implementations where cloud platforms bridge theoretical AI advances with real-world engineering challenges. His work particularly addresses infrastructure limitations in rural IoT deployment through intelligent data cleaning mechanisms and develops quality assessment metrics for next-generation cloud services. The protein simulation research demonstrates cross-disciplinary application of high-performance computing techniques. As a core member of Gdańsk Tech's Task Computing Centre, Professor Krawczyk leverages institutional high-performance computing resources to advance scalable cloud architectures and computational biology methodologies. No information regarding student supervision or research grants was provided in available materials.

Mariusz Kaczmarekمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Medical Sensors
• Thermal Imaging
• +۴ مورد دیگر

Dr. Mariusz Kaczmarek serves as Associate Professor and Vice-Rector for Education at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, with office contact markaczm@pg.edu.pl and phone (58) 347 26 78. His research spans biomedical engineering with emphasis on medical sensor development, thermal imaging diagnostics, and AI integration in healthcare systems. Key projects include foot-sampled pulse monitoring via bathroom scales, active dynamic thermography for peripheral artery disease detection, and 3D imaging for breast reconstruction volume calculation. He additionally contributes to academic education through Gdańsk Tech's Center for Innovative Education. Analysis of his 2024-2025 publications reveals a clear trajectory toward engineering-driven medical solutions, particularly merging telecommunications expertise with clinical applications through sensor innovation and machine learning-enhanced diagnostics. Scientific Awards: No awards documented in available materials. Advising and Grants: Student supervision and funding details absent from current documentation. Labs and Teams: Research infrastructure information not specified in provided content.

Julian Szymańskiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Blockchain Technology
• +۷ مورد دیگر

Dr. hab. inż. Julian Szymański serves as Associate Professor at the Department of Computer Architecture within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. He concurrently holds the position of Deputy Director at the Industrial Doctoral School, demonstrating dual leadership in academic instruction and doctoral program administration. His research spans artificial intelligence applications across diverse domains including sustainable agriculture, healthcare monitoring, and privacy-preserving systems. Key focus areas include natural language processing, blockchain implementation for sensitive data services, and multi-modal signal analysis. His work bridges theoretical computer science with practical applications in agritech, medical diagnostics, and secure service provision. Recent publications reveal a strong trend toward interdisciplinary AI solutions, particularly in sustainability (2025 smart farming chapter), human-AI alignment techniques, and privacy frameworks compliant with GDPR. His respiratory rhythm classification research demonstrates methodological rigor through multi-sensor data fusion. Academic contributions include: ERASMUS+ funded Modena MoltiMondi project as project manager Development of EditPrefs methodology for cost-effective preference dataset creation Gold-standard multi-genre datasets for entity linking evaluation As an educator, he has taught 185 courses since 2018 across artificial intelligence, deep learning, and intelligent information systems, mentoring students through diploma projects. His laboratory work focuses on IoT-based healthcare frameworks and blockchain implementations for privacy-sensitive services, with current projects exploring AI-driven sustainability solutions.

Andrzej Sobeckiمشاهده پروفایل

استادیار

• Blockchain
• Distributed Systems
• Internet of Things (IoT)
• +۳ مورد دیگر

Andrzej Sobecki serves as an Assistant Professor at Gdańsk University of Technology , affiliated with the Task Informatics Center . His contact details include email andrzej.sobecki@pg.edu.pl and phone +48 58 347 12 30 . He manages the SOA SOWI plagiarism detection system project funded by Poland's Operational Programme Knowledge Education Development. His research spans critical domains in modern computing: Blockchain applications for industrial infrastructure monitoring and healthcare data security Distributed systems integration in untrusted multi-vendor environments (IoMT) Advanced natural language processing for legal information retrieval Knowledge representation using neural embeddings of semantic graphs Software engineering challenges in service-oriented deployment pipelines Analysis of his 2020-2023 publications reveals consistent innovation in overcoming security and interoperability barriers through cryptographic methods and neural language models. Key contributions address real-time data processing in industrial contexts, trust frameworks for medical IoT, and efficient legal document analysis – demonstrating strong interdisciplinary collaboration across computer science, law, and healthcare engineering. Scientific recognition: No awards or fellowships documented in source materials Academic service and funding: Principal investigator for SOA SOWI project (Operational Programme Knowledge Education Development) No student advisement records or additional grant details provided Institutional context: Active member of Gdańsk Tech's Task Informatics Center research group Collaborates with multiple academic and industry partners as evidenced by co-authored publications

Natalia Kowalczykمشاهده پروفایل

مدرس

• Biomedical Engineering
• Machine Learning in Healthcare
• Digital Twin Applications
• +۳ مورد دیگر

Natalia Kowalczyk serves as an Assistant Lecturer at the Department of Biomedical Engineering within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. Her academic profile demonstrates a strong focus on applying advanced computational techniques to solve critical healthcare challenges, particularly in maternal health monitoring and biomedical signal processing. Her research interests span multiple interdisciplinary domains including biomedical engineering, machine learning applications in healthcare, digital twin technology for medical diagnostics, and federated learning systems for privacy-preserving healthcare AI. Kowalczyk's work particularly emphasizes maternal health monitoring systems, respiratory signal analysis, and the application of synthetic data in medical diagnostics. Analysis of her recent publications reveals a consistent trajectory toward developing AI-driven healthcare solutions with practical clinical applications. Her research demonstrates expertise in machine learning algorithm development, particularly XGBoost and federated learning approaches, applied to obstetric healthcare challenges including preeclampsia prediction and gestational diabetes diagnosis. She also contributes to computer vision applications in biomedical contexts and edge computing for healthcare infrastructure. Natalia Kowalczyk collaborates extensively with researchers including J. Rumiński, M. Mazur-Milecka, and T. Kocejko across multiple healthcare technology projects. Her work bridges theoretical machine learning advancements with practical healthcare implementations, particularly in maternal health monitoring and pandemic response systems.

Konrad Zaworaمشاهده پروفایل

مدرس

• Computer Vision
• Deep Learning
• Neural Networks
• +۲ مورد دیگر

Konrad Zawora serves as an Assistant lecturer at the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology, specializing in applied artificial intelligence and computer vision research. His core research domains include: Computer Vision Deep Learning Neural Networks Pattern Recognition Machine Learning Zawora's 2022 publications demonstrate expertise in developing deep learning architectures for image classification tasks, spanning theoretical frameworks for classifier evolution and practical implementations for specialized object recognition like LEGO brick sorting. His work emphasizes real-world applications of state-of-the-art neural network models in complex visual recognition scenarios. No scientific awards were documented in the source materials. Information regarding student advisement and research funding remains unavailable in the provided documentation.

Robert Benkeمشاهده پروفایل

مدرس

• Graph Neural Networks
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Robert Benke serves as an Assistant Lecturer at the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research focuses on graph-based machine learning methodologies and their practical implementations. His core research domains include: Graph Neural Networks Machine Learning Natural Language Processing Graph Analytics Deep Learning Hardware Acceleration Recent publications reveal concentrated efforts in optimizing Graph Convolutional Networks for specialized hardware architectures (Intel PIUMA), addressing critical challenges in memory efficiency and computational scalability. His work bridges theoretical graph analytics with real-world applications in text classification, demonstrating how graph structures capture complex dependencies in unstructured data through neural network approaches.

Izabela Lubowieckaمشاهده پروفایل

استاد

• Biomechanics
• Computational Mechanics
• Medical Device Optimization
• +۲ مورد دیگر

Prof. Izabela Lubowiecka serves as a Professor at the Department of Structural Mechanics within the Faculty of Civil and Environmental Engineering at Gdańsk University of Technology. Her office is located in the Main Building (room 467A) with contact details including email lubow@pg.edu.pl and phone (58) 348 64 08. She maintains an active research profile with current projects and recent publications through 2025. Her research spans computational biomechanics with specific focus on abdominal wall mechanics, hernia repair systems, and temporomandibular joint analysis. She integrates structural engineering principles with medical applications through advanced computational modeling, uncertainty quantification, and machine learning techniques. Key methodologies include Self-Organising Maps for biomechanical data clustering and digital image correlation for in vivo strain analysis, targeting improvements in medical device design and surgical outcomes. Recent publications (2023-2025) demonstrate consistent interdisciplinary work bridging engineering and clinical medicine. Core themes include mechanical compatibility optimization of hernia implants, failure analysis of surgical mesh systems, and machine learning applications for identifying biomechanically similar tissue regions. This research emphasizes patient-specific modeling through uncertainty quantification and sensitivity analysis to address clinical challenges like hernia recurrence. Prof. Lubowiecka secures significant research funding through national programs: Mechanics of anterior abdominal wall in optimisation of hernia treatment : OPUS project (UMO-2017/27/B/ST8/02518) since 2018 3D-JAW : TMJ modeling for dental applications (POIR.04.01.02-00-0029/17-00) since 2017 Geometric-strength analysis of historical carpentry joints : OPUS project (UMO-2015/17/B/ST8/03260) since 2016 She leads a research team collaborating with medical professionals on translating computational biomechanics into clinical solutions for hernia repair and dental prosthetics, with emphasis on practical implementation of research findings.

Anna Jakubczyk-Gałczyńskaمشاهده پروفایل

استادیار

• Structural Engineering
• Machine Learning Applications
• Vibration Analysis
• +۳ مورد دیگر

Dr. Anna Jakubczyk-Gałczyńska serves as an Assistant Professor in the Department of Civil Engineering at the Faculty of Civil and Environmental Engineering, Gdańsk University of Technology. Her academic work bridges traditional civil engineering with advanced computational techniques, focusing on practical applications that address contemporary challenges in structural analysis and construction management. Her research interests span multiple interdisciplinary domains including structural engineering, machine learning applications, vibration analysis, and construction management optimization. Dr. Jakubczyk-Gałczyńska has developed expertise in applying metaheuristic optimization methods and machine learning algorithms to solve complex engineering problems, particularly in seismic analysis and building vibration assessment. Analysis of her recent publications (2024-2025) reveals a strong trend toward integrating artificial intelligence with traditional civil engineering practices. Her work demonstrates how machine learning can enhance predictive capabilities in structural engineering, particularly in vibration analysis and seismic response prediction, while metaheuristic methods show promise for optimizing construction management processes and decision-making. Dr. Jakubczyk-Gałczyńska has established productive research collaborations with colleagues including M. Mikielewicz, R. Jankowski, A. Siemaszko, and M. Poltavets, resulting in publications in reputable journals such as Applied Sciences-Basel, Bulletin of the Polish Academy of Sciences, and Engineering Applications of Artificial Intelligence.