جست‌وجوی استاد و پژوهشگر

Konrad Kowalczykمشاهده پروفایل

دانشیار

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

• Signal Processing
• Electronics
• Biomedical Engineering
• +۵ مورد دیگر

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.

Szymon Cyganمشاهده پروفایل

استادیار

• Strain Imaging Methods for Echocardiography
• Ultrasonic Imaging
• Biomedical Engineering
• +۵ مورد دیگر

Szymon Cygan, PhD, is an Assistant Professor and Deputy Dean for Students Affairs and Promotion at the Institute of Metrology and Biomedical Engineering , part of the Faculty of Mechatronics at Warsaw University of Technology. His work focuses on strain imaging methods for echocardiography, ultrasonic imaging, and biomedical engineering applications. Education: M.Sc. (2003) in Automatics and Robotics with Biocybernetics and Biomedical Engineering specialization; Ph.D. (2011) on displacements and strain estimation for ultrasonic strain imaging. Research: Specializes in speckle tracking echocardiography, left ventricular phantom modeling, finite element simulations, and validation of strain estimation algorithms using synthetic and physical models. Awards: No specific scientific awards explicitly mentioned. Projects: Involved in 2 projects, including development of low-cost gait analysis systems and synthetic cardiac phantom validation. Labs: Part of Krzysztof Kaluzynski's Lab, focusing on biomedical engineering and cardiac imaging.

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.

Agnieszka Jędrzejewskaمشاهده پروفایل

پژوهشگر

• Structural Engineering
• Concrete Technology
• Sustainability in Civil Engineering
• +۳ مورد دیگر

Agnieszka Jędrzejewska is a researcher at the Department of Structural Engineering , Faculty of Civil Engineering , Silesian University of Technology , Poland. She specializes in the analysis of cracking in concrete structures due to thermal, shrinkage, and imposed strains, with a focus on sustainability and robustness of cement-based materials. Research Interests : Thermal cracking, early-age concrete behavior, sustainable materials (e.g., self-healing concrete, 3D-printed mortar), numerical modeling, crack prevention techniques. Awards : Recipient of the Scientific Award of the 4th Division of the Polish Academy of Sciences (2024) for her series of works on 'Cracking of Special Structures Under the Action of Imposed Deformations.' Her recent publications highlight interdisciplinary approaches to improving concrete sustainability (e.g., alccofine 1203, GGBS, kaolin) and advancing computational models for crack prediction. She contributes to standardization efforts (e.g., prEN 1992-1-1) and collaborates on international projects (e.g., RILEM TC 287-CCS, COST TU1404).

Lyudmyla Kirichenkoمشاهده پروفایل

استادیار

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

• Small Area Statistics
• Economic Statistics
• Robust Estimation Methods
• +۳ مورد دیگر

Grażyna Dehnel is an Assistant Professor at the Department of Statistics within the Institute of Informatics and Quantitative Economics at Poznań University of Economics. She earned her doctorate in 1999 and became a Professor Assistant in 2010, focusing on statistical methodologies and their applications in economics. Current affiliation: Poznań University of Economics Department: Statistics Discipline: 25% Economics and Finance, 75% Management and Quality Studies Her research centers on small area statistics , robust estimation , and indirect methods for economic analysis. She specializes in integrating register and survey data for microenterprise studies and social cohesion metrics. Recent publications highlight applications of dynamic relative taxonomy to assess EU strategies, wage estimation models, and social cohesion in Polish and Visegrad regions. Her work bridges statistical theory with practical economic challenges. Dehnel has supervised 3 promoted theses and contributed to 46 publications. She participates in international collaborations and conferences, though specific awards are not detailed in the provided texts.

• Applied Mathematics
• Numerical Methods
• Mathematical Modelling
• +۶ مورد دیگر

Kailash C. Patidar is a Professor of Applied Mathematics whose primary appointment is at the University of the Western Cape (South Africa), where he has been based since 2006. Previously he held faculty roles at the Indian Institute of Technology Kanpur (2001–2010) and shorter visiting or research positions at Western University (Canada), University of Pretoria , and Eberhard Karls Universität Tübingen (Germany). His research agenda centres on the design and analysis of robust numerical methods for complex mathematical models, with particular emphasis on singularly perturbed differential equations , stochastic fractal processes , convection-diffusion phenomena , and mathematical oncology . By integrating rigorous mathematical theory with efficient computational techniques, Prof. Patidar bridges the gap between abstract analysis and practical applications in finance and biology. Across more than 100 peer-reviewed publications spanning two decades, his work repeatedly addresses layer phenomena, adaptive grids, non-standard finite-difference schemes, and high-order convergence. Recent outputs focus on magnetohydrodynamic flows and tumour-immune interaction models, evidencing a deliberate expansion into multi-disciplinary modelling. Scientific sponsorship has been provided by leading agencies including the Japan Society for the Promotion of Science , National Institutes of Health , National Research Foundation (South Africa) , Medical Research Council , Associazione Italiana per la Ricerca sul Cancro , Taif University , Norges Forskningsråd , and others, underscoring strong international collaboration and cross-disciplinary impact. While no specific doctoral students are listed in the supplied material, Prof. Patidar’s extensive co-authorship network and sustained grant activity imply active supervision and mentoring of postgraduate researchers.

Jakub Klikowskiمشاهده پروفایل

استادیار

• Machine Learning
• Data Stream Classification
• Imbalanced Data
• +۱۲ 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.

• Geodesy
• GNSS Technology
• Remote Sensing
• +۳ مورد دیگر

Jacek Rapiński is a Professor at the University of Warmia and Mazury (UWM) in the Department of Geoengineering. His research focuses on geodesy, GNSS technology, LiDAR data processing, and structural monitoring, particularly in mining and environmental contexts. He has supervised doctoral students at UWM and UPC and contributed to numerous publications. Over 58 publications in WoS/Scopus 307 citations (WoS/Scopus) H-index 12 (WoS/Scopus), 14 (Google Scholar) Supervised 1 doctoral student at UWM and 2 total His recent research explores GNSS multipath mitigation , low-cost sensor integration , and machine learning applications for infrastructure monitoring. Projects include innovative tools for GNSS mission planning and climate-focused land consolidation strategies. While no explicit awards are listed, his work involves advanced methodologies like Msplit estimation and MEMS sensor fusion . He emphasizes promoting doctoral research with available funding and infrastructure at UWM.

Hammed Mojeedمشاهده پروفایل

مدرس

• Machine Learning
• Software Engineering
• Biometric Security
• +۳ مورد دیگر

Hammed Mojeed serves as an Assistant lecturer at Gdańsk University of Technology in the Department of Computer System Architecture within the Faculty of Electronics, Telecommunications and Informatics. His academic position places him at the intersection of theoretical computer science and practical industry applications. Mojeed's research interests span multiple domains where machine learning meets real-world challenges. His primary focus areas include software project management optimization , particularly in overtime estimation and allocation, where he bridges the gap between algorithmic solutions and project managers' intuitive knowledge. He also conducts significant research in biometric security systems , specifically developing more robust finger vein recognition technologies resistant to presentation attacks. Additionally, his work in telecommunications analytics focuses on customer churn prediction using advanced ensemble learning techniques. Mojeed's publication trends reveal a consistent pattern of applying machine learning methodologies to solve industry-specific problems. His recent work (2023-2025) shows increasing sophistication in model development, moving from basic tree-based classification to more complex heterogeneous stacking ensembles and cross-validation approaches. His publications span both computer science and telecommunications domains, demonstrating interdisciplinary expertise. While no specific scientific awards are documented in the available materials, his publications in reputable journals like IEEE Access and Scientific African indicate recognition within his research communities. Mojeed actively collaborates with an international research network including R. Szłapczyński, A.O. Balogun, and L.F. Capretz. His research appears to be self-funded or supported through university channels, with no specific grant information provided in the available materials. His work demonstrates strong industry relevance, particularly in software development and telecommunications sectors.

Grzegorz Mzykمشاهده پروفایل

استاد

• System identification
• Adaptive control
• Statistical learning
• +۵ مورد دیگر

Grzegorz Mzyk is a Professor at Wrocław University of Science and Technology, specializing in control systems and data analysis. His research focuses on developing advanced methodologies for nonlinear system identification and statistical learning, with particular expertise in Hammerstein-Wiener systems, kernel methods, and adaptive control techniques. His core research interests span: System identification : Developing nonparametric methods for complex nonlinear systems Adaptive control : Creating robust algorithms for time-varying environments Statistical learning : Applying kernel methods and entropic metrics to data analysis Cyclostationary analysis : Innovating excitation techniques for dimensionality reduction Recent publications demonstrate strong focus on: Advancing identification techniques for Wiener-Hammerstein systems Implementing adversarial training for secure system modeling Developing error detection methods for databases using machine learning Optimizing procurement strategies through demand forecasting These works integrate control theory with contemporary machine learning approaches.

• bioinformatics
• neurodegenerative diseases
• machine learning
• +۶ مورد دیگر

Jarosław Chilimoniuk is an Assistant Professor at the Clinical Research Center of the Medical University of Bialystok. His work bridges computational methods with biomedical applications, focusing on neurodegenerative diseases and metabolomics. Research Interests: Bioinformatics, neurodegenerative diseases, machine learning, amyloid interactions, protein aggregation, metabolomics, and antimicrobial peptide prediction. His recent publications emphasize database development (e.g., AmyloGraph), metabolomics data analysis (imputomics, ML-based models), and computational tools for DNA damage and peptide design. Labs & Teams: Affiliated with the Clinical Research Center, contributing to interdisciplinary projects in neurodegenerative diseases and infectious disease proteomics.

Paweł Wachelمشاهده پروفایل

پژوهشگر

• Control Theory
• System Identification
• Signal Processing
• +۵ مورد دیگر

Paweł Wachel is a researcher at the Department of Control Systems and Mechatronics within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He is actively engaged in research and academic activities, with recent publications spanning from 2022 to 2024. Research Interests: His primary research areas include control theory, system identification, signal processing, and nonlinear systems. He specializes in the modelling and identification of complex systems such as Wiener, Hammerstein, and infinite memory nonlinear systems, employing advanced techniques like kernel-based methods, aggregative modelling, and exponential excitations. The recent publications indicate a strong focus on both theoretical and applied aspects of control systems, with increasing integration of learning-based and data-driven approaches. Topics such as safe learning, decentralized diffusion, and dual averaging algorithms reflect modern trends in adaptive and robust control. The interdisciplinary reach extends into neural networks and materials science, particularly in using fractal analysis for material hardness estimation. Scientific Awards: No awards or fellowships are mentioned in the provided text. Advising and Grants: While no specific students or grant funding are listed, Paweł Wachel collaborates extensively with researchers such as Krzysztof R Kowalczyk, Cristian R Rojas, Koen Tiels, and others, suggesting active participation in research teams and potential supervision roles. His consistent publication output indicates sustained research activity, likely supported by institutional or project-based funding. Labs and Teams: As a member of the Department of Control Systems and Mechatronics, he is likely involved in research laboratories focused on control systems, system identification, and mechatronic applications, though specific lab names or team structures are not detailed in the provided content.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Tomasz Kajdanowicz serves as an Associate Professor in the Department of Artificial Intelligence at Wrocław University of Science and Technology's Faculty of Information and Communication Technology. His research bridges theoretical machine learning with practical cybersecurity and natural language processing applications. His primary research interests focus on graph neural networks , large language model optimization , and interpretable cybersecurity systems . Notable contributions include the MIRAD framework for ransomware detection and PUGG dataset development for Polish language processing. His work demonstrates consistent innovation in graph representation learning, particularly through multiplex graph fusion techniques and knowledge graph enrichment strategies. Analysis of his recent publications reveals a strong trajectory toward trustworthy AI systems with emphasis on hallucination detection (FactSelfCheck), latent space optimization (LatentPrompt), and synthetic data generation for medical applications. His cybersecurity research shows increasing sophistication in interpretable threat detection mechanisms. His academic supervision includes multiple doctoral and master's students evidenced through co-authorship patterns, particularly in graph-based learning and NLP projects. Current collaborations span biomedical signal processing (fNIRS analysis) and efficient LLM deployment strategies. His laboratory work centers on the AI Security Lab within the Department of Artificial Intelligence, focusing on adversarial robustness testing and real-time network monitoring systems. Key initiatives include the development of Polish-language NLP benchmarks and cross-domain graph embedding frameworks.