جست‌وجوی استاد و پژوهشگر

• Computational Neuroscience
• Quantitative Electroencephalography (qEEG)
• Brain-Computer Interfaces (BCI)
• +۶ مورد دیگر

Grzegorz Marcin Wójcik is a Professor and Head of the Department of Neuroinformatics and Biomedical Engineering at the Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Poland. Additionally, he leads the Department of Intelligent Systems and Data Science at the Polish-Japanese Academy of Information Technology in Warsaw. His academic profile spans computational neuroscience, quantitative electroencephalography (qEEG), and biomedical engineering. University: Maria Curie-Sklodowska University School: Faculty of Mathematics, Physics and Computer Science Department: Neuroinformatics and Biomedical Engineering Research Interests: Dr. Wójcik specializes in computational neuroscience and quantitative EEG , with interdisciplinary work in artificial intelligence , parallel computing , and brain-computer interfaces . His research extends to optical fiber technology , including photonic crystal fibers , microstructured fibers , and semiconductor-quantum dot integration , addressing challenges in telecommunications and sensing applications . Publication Trends: His recent articles focus on optical fiber design , quantum communication , and sensing technologies . Key subfields include surface plasmon resonance , multi-core fiber systems , and thermal/spectroscopic analysis of fiber materials. Publications also highlight computational modeling in biomedical engineering and neuroscience . Contact: Email: grzegorz.wojcik@mail.umcs.pl Phone: +48 81 537 29 40 Address: ul. Akademicka 9/509, 20-033 Lublin, Poland

• Power Electronics
• Electrical Machines
• Magnetic Materials
• +۵ مورد دیگر

Zbigniew Szular is a Lecturer at the Department of Electrical Engineering within the Faculty of Electrical and Computer Engineering at Cracow University of Technology. His academic profile shows active research and teaching engagement in the field of electrical engineering with a focus on power electronics and magnetic materials. Dr. Szular's research interests center on power electronics, particularly soft switching technologies for voltage source inverters, magnetic properties of electrical steel sheets, and hysteresis modeling. His work addresses critical challenges in energy conversion systems, transformer design, and electrical machine efficiency. The research spans both theoretical modeling and practical implementation of power electronic systems. Analysis of his publication record from 2016-2025 reveals a consistent research trajectory focused on improving power conversion efficiency through innovative soft switching techniques for various inverter topologies. His work spans three-phase systems, two-level and three-level inverters, and neutral-point-clamped configurations. A parallel research thread examines magnetic properties of electrical steel, with emphasis on hysteresis modeling, anisotropy effects, and crystallographic influences on magnetic behavior. Dr. Szular maintains an active research collaboration network, primarily with Witold Mazgaj and Bartosz Rozegnał, resulting in numerous joint publications in high-impact journals including IEEE Transactions on Power Electronics and Energies. His research demonstrates both theoretical depth in magnetic material modeling and practical engineering solutions for power electronic systems.

Mariusz Matuszekمشاهده پروفایل

استادیار

• High-Performance Computing
• Parallel Computing
• Energy Efficiency in Computing Systems
• +۳ مورد دیگر

Mariusz Matuszek serves as an Assistant Professor at Gdańsk University of Technology within the Department of Computer Systems Architecture, Faculty of Electronics, Telecommunications and Informatics. His research focuses on energy-efficient computing systems and high-performance parallel architectures. His primary research interests span High-Performance Computing , GPU Acceleration , and Energy Efficiency in Computing Systems . Current work examines power-capped optimization for deep neural network training, hardware/software energy measurement methodologies for CPU+GPU systems, and resource-aware problem formulations using ILP, greedy algorithms, and evolutionary approaches. His research bridges theoretical optimization with practical implementation in multi-GPU environments. Analysis of his 2022-2025 publications reveals strong emphasis on measuring and optimizing energy consumption in parallel computing systems, with particular focus on deep learning workloads. Key methodologies include professional hardware metering (Yokogawa WT-310E), Intel RAPL, and NVIDIA NVML interfaces across multi-GPU architectures. His work demonstrates significant performance-energy improvements through strategic power capping. Matuszek actively contributes to computer science education through curriculum development in parallel programming using MPI, OpenMP, and CUDA frameworks. His teaching case studies address the growing demand for HPC expertise in modern machine learning infrastructure development.

Jan Dobrosolskiمشاهده پروفایل

مدرس

• Energy-Efficient Computing
• Deep Learning
• Parallel and Distributed Systems
• +۳ مورد دیگر

Jan Dobrosolski serves as an Assistant lecturer at the Department of Computer Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic work focuses on performance-energy optimization in high-performance computing systems, with particular expertise in power management constraints. His research spans energy-efficient computing methodologies across multiple domains: deep learning acceleration, natural language processing tokenization, and reinforcement learning applications. Key specializations include power capping techniques for multi-core CPUs and multi-GPU architectures, neural network training optimization, and logic-based agent development using declarative programming frameworks. Dobrosolski's 2024 publications reveal a cohesive research trajectory centered on sustainable computing practices. His work systematically examines energy-performance trade-offs in tokenizer algorithms, deep neural network training, and neural agents for logic tasks—all under controlled power constraints. This integrated approach demonstrates significant contributions to resource-efficient computing across both CPU and GPU hardware platforms. Contact: jandobro@pg.edu.pl

Małgorzata Gordziej-Zagórowskaمشاهده پروفایل

استادیار

• Structural Engineering
• Steel Structures
• Cold-Formed Steel Systems
• +۴ مورد دیگر

Dr. Małgorzata Gordziej-Zagórowska serves as an Assistant Professor at the Department of Building Engineering within the Faculty of Civil and Environmental Engineering at Gdańsk University of Technology. Her office is located in Building A of the Faculty (Room 232), with contact details including email malgor@pg.edu.pl and phone +48 58 347 29 75. Her research specializes in advanced structural engineering with emphases on steel structure stability , cold-formed steel systems , and innovative girder design . Key methodologies combine experimental testing with numerical simulation to address critical challenges in plate girder web stability, cold-formed steel joint behavior, and wind load analysis. Recent work investigates variable web thickness solutions to replace traditional stiffeners and examines bolt spacing effects in built-up columns. Publications from 2020-2024 reveal consistent focus on structural failure mechanisms and innovative solutions for steel components. Her work bridges theoretical modeling with practical construction applications, particularly in optimizing cold-formed steel trusses and analyzing wind load discrepancies between standard procedures and digital simulations. Notable contributions include replacing longitudinal stiffeners through geometric innovation and validating Eurocode compliance for industrial structures. Dr. Gordziej-Zagórowska leads the MINIATURA 2023 project 'Local stability analysis of a plate girder with variable thickness of the web' (agreement 2023/07/X/ST8/00613) within the Department of Building Structures and Material Engineering. She also contributes to the parallel MINIATURA project 'Stability and failure mechanism tests of a plate girder with a Y-shaped web' managed by Dr. Wojciech Migda.

Mariusz Uchrońskiمشاهده پروفایل

استاد

• Discrete Optimization
• High-Performance Computing (HPC)
• Parallel Algorithms
• +۳ مورد دیگر

Professor Mariusz Uchroński is affiliated with Wrocław University of Science and Technology (WUST) as a faculty member in the field of discrete optimization and high-performance computing. His research focuses on advanced computational methods including parallel and distributed algorithms, quantum computing, and task scheduling optimization. Research Interests: He specializes in developing efficient algorithms for complex computational problems, with applications in high-performance computing environments and emerging quantum computing paradigms. His work addresses optimization challenges in parallel/distributed systems and task scheduling methodologies. Contact Information: Email: mariusz.uchronski@pwr.edu.pl Phone: +48 71 320 2456 Office: Z. Janiszewskiego Street 11/17, Wrocław, building C-3, room 214 Consultations: Mondays 16:00-18:00

Paweł Ksieniewiczمشاهده پروفایل

استاد

• Pattern Recognition
• Data Streams
• Hyperspectral Image Processing
• +۷ مورد دیگر

Paweł Ksieniewicz is a Professor at Wrocław University of Science and Technology, affiliated with the Faculty of Electronics and the Department of Computer Systems and Networks. He is a member of the Computer Networks Team and the Machine Learning Team, and serves as a project manager for SWAROG (2021–2025) and contributor to IDSTREAM and GEOM projects. His research interests include: Pattern Recognition Data Streams Hyperspectral Image Processing Imbalanced Data Classification Fake News Detection Classifier Ensembles Ksieniewicz's recent publications focus on challenges in data stream mining, particularly in dynamically imbalanced environments, prior probability estimation, and ensemble methods. His work spans applications in cybersecurity (fake news, IoT attack detection) and healthcare (glaucoma diagnosis). He has contributed to open-source tools like the Stream-learn Python library, emphasizing practical and reproducible machine learning research. His scientific awards include: Minister of Education and Science Scholarship for Outstanding Young Scientists Award of the Polish Society for Artificial Intelligence for Best Doctoral Thesis Multiple Rector's Awards for scientific achievements (2019–2022) Best Paper Award at ICAISC 2021 Winner of the Secundus and Primus research incentive programs Appointment to Academia Iuvenum (2024–2026) Ksieniewicz actively supervises students and has collaborated with researchers such as Michał Woźniak, Paweł Zyblewski, and Robert Burduk. He has secured research grants through national projects and serves on the Discipline Council for Technical Computer Science and Teleinformatics. He has also participated in public outreach, including lectures during Science Week and World Information Society Day. He leads and contributes to multiple research teams, including: Machine Learning Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

• Quantum Computing
• Machine Learning
• Optimization
• +۳ مورد دیگر

Maryam Zomorodi Moghaddam is an Associate Professor in the Department of Computer Science at Cracow University of Technology's Faculty of Computer Science and Telecommunications. Her research focuses on quantum computing, machine learning, and optimization techniques, with applications in distributed systems and medical informatics. Research interests include: Quantum circuit optimization and teleportation Distributed quantum systems architecture Machine learning applications in healthcare Evolutionary computation and genetic algorithms Natural language processing using quantum techniques Recent publications demonstrate strong focus on quantum computing optimizations and AI medical applications, with quantum circuit efficiency and healthcare AI emerging as dominant themes. Notable contributions include distributed quantum gate protocols and medical classification systems using neuroevolution.

• Formal Methods
• Interactive Theorem Proving
• Proof Assistants
• +۷ مورد دیگر

Chantal Keller is a Lecturer in the Computer Science Department at IUT d'Orsay, University of Paris-Saclay, and a member of the Formal Methods Laboratory (LMF), a joint unit involving CNRS and ENS Paris-Saclay. Her work bridges theoretical computer science and practical formal verification, with a strong focus on interactive theorem proving and proof assistant technologies. University: University of Paris-Saclay School: IUT d'Orsay Department: Computer Science Department Research Lab: Laboratoire Méthodes Formelles (LMF) Her research centers on enhancing the power and usability of proof assistants like Coq. She has developed tools such as SMTCoq , which integrates SMT solvers into Coq for automated reasoning, and HOLLIGHTCOQ , enabling interoperability between HOL-Light and Coq. Her PhD focused on formal proofs with SMT, and she continues to contribute to foundational aspects of type theory, lambda calculus, and logical frameworks. The 15 most recent articles reflect deep engagement with formal logic, programming language semantics, and automated reasoning. Key trends include proof interoperability, termination analysis, constraint programming, and the formalization of mathematical and computational systems in proof assistants. Her work spans theoretical developments and practical implementations in Coq, Agda, and OCaml. Chantal Keller has served on numerous program committees, including ITP, CPP, POPL, TAP, and JFLA, highlighting her active role in the formal methods community. She has held teaching and research positions since 2007, including roles at École Polytechnique and the University of Nottingham. 2025: Co-PC Chair, ITP 2022: Chair, JFLA 2021: Vice-Chair, JFLA; Co-Chair, PxTP 2017–2022: Coordinator, Digicosme UPSCaLe working group She has advised on workshops and school programs, including the EasyCrypt-F*-CryptoVerif school in 2014. She has not received any explicitly mentioned scientific awards in the provided texts. She is actively involved in teaching programming, algorithms, and mobile development at IUT d'Orsay.

Andrzej Maziewskiمشاهده پروفایل

استاد

• Magnetism
• Magnetic Materials
• Spintronics
• +۷ مورد دیگر

Prof. Andrzej Maziewski is a full Professor at the Department of Physics of Magnetism within the Faculty of Physics at the University of Białystok, Poland. He has held academic positions since 1974, founded and led the Department of Physics of Magnetism since 1976, served as Deputy Rector (1998-99), and directed the Institute of Experimental Physics since 2005. As co-founder of the National Centrum of Nanophysics and Spintronics (SPINLAB), he has coordinated major infrastructure projects enhancing research capabilities in spintronics and nanomagnetism. His educational background includes an MSc from Warsaw University (1974), followed by academic training, PhD (1979), and Dr. hab. (1990) from the Institute of Physics, Polish Academy of Sciences. His habilitation thesis focused on Magnetization processes in layers with strong magnetic-after effect , establishing his expertise in magnetic phenomena. Maziewski's research spans statics and dynamics of magnetic domain structures, magnetization processes in ultrathin films, and effects of ion/light irradiation on magnetic properties. He pioneered magnetooptical magnetometers with digital image processing and investigated garnet films exhibiting room-temperature photomagnetism and magnetic shape memory. His work integrates spintronics , nanomagnetism , and materials engineering to manipulate magnetic anisotropy and domain structures through geometric design, ion irradiation, and interfacial effects. Key innovations include tailoring Dzyaloshinskii-Moriya interactions and perpendicular anisotropy in Pt/Co/Pt systems. His publication trend (2019-2002) reveals a sustained focus on ion irradiation for tuning magnetic properties in nanostructures, with breakthroughs in Dzyaloshinskii-Moriya interaction mapping (2019) and irradiation-induced lattice distortions (2012). The 2007 GloLab paper highlights his parallel commitment to physics education through remote laboratories. His scientific recognition includes: Knight’s Cross of the Order of Polonia Restituta (2019) Medal of the National Education Commission (2016) Medal of Polish Physical Society for popularization (2007) Golden Cross of Merit (2001) Multiple awards from Polish Ministry of Science and University Rectors Maziewski has supervised 7 PhD theses and secured major grants including EU Marie-Curie ToK NANOMAG-LAB (2004-2009), FANTOMAS (FP7, 2008-2012), and Polish National Science Centre projects (2013-2024). His leadership in bilateral German (DAAD) and French (POLONIUM) collaborations advanced ion-irradiation techniques for spin-reorientation studies. As co-founder of the Regional Computer Laboratory for Teaching Natural Science and Podlaski Festival of Science, he bridges advanced research with educational outreach. His SPINLAB infrastructure enables cutting-edge experiments in magnonic crystals and ultrafast magnetism, with ongoing work (2020-2024) on synthetic layered structures for next-generation spintronic devices.

Mazuro Pawełمشاهده پروفایل

پژوهشگر

• Innovative Piston Engine Technologies
• Combustion Efficiency Optimization
• Opposed-Piston Engines
• +۳ مورد دیگر

Paweł Mazuro is a researcher at the Division of Aircraft Engines (Zakład Silników Lotniczych) at Warsaw University of Technology . He holds the academic rank of Researcher and has led/managed multiple research projects including: (1) Highly Efficient HCCI Engine for Distributed Energy (2015-2020), (2) HCCI in Innovative Barrel Engines (2014-2017), and (3) Non-Crankshaft Aircraft Engine Development (2007-2010). Biofuels & Reciprocating Engines Opposed-Piston Engine Mechanics Barrel Engine Design HCCI Combustion Optimization CFD Analysis for Scavenging Thermal/Mechanical Stress Analysis His recent publications focus on unconventional engine architectures and combustion processes, including works on opposed-piston engines, HCCI combustion control, and CFD modeling of thermal stresses. He has received two Rector’s Awards (2010, 2018) for engineering innovations and experimental research.

• Combustion Engineering
• Artificial Intelligence
• Numerical Simulations
• +۴ مورد دیگر

Dr inż. Mateusz Żbikowski is a researcher at the Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology. His research focuses on applying artificial intelligence and computational methods to combustion process optimization, with expertise in numerical simulations, high-performance computing, and data processing frameworks like Apache Spark and Hadoop. Teaching: Conducts courses on Cloud Computing, Combustion Methods (Cantera), and Computer Simulations (OpenFoam) Technologies: Works with MPI/MPJ, OpenFoam, and programming languages including Java, C++, Python, and R Research: Investigates AI-driven combustion optimization, numerical modeling of thermal processes, and distributed computing cluster applications

• Mathematical Modeling
• Differential Equations
• Dynamical Systems
• +۴ مورد دیگر

Maria Gokieli serves as a Lecturer at the School of Exact Sciences within the Faculty of Mathematics and Natural Sciences at Cardinal Stefan Wyszyński University in Warsaw. Her academic work focuses on theoretical and applied mathematical research with significant contributions to computational methods. Research Interests: Dr. Gokieli specializes in mathematical modeling with particular expertise in differential equations and dynamical systems. Her work spans applied mathematics, numerical analysis, and computational stability, with applications in parallel algorithms and finite element methods. Research keywords include Navier-Stokes equations, subdifferential calculus, and distributed systems. Publication Metrics: She has published 11 scholarly works with notable impact in her field. Her bibliometric indicators include an h-index of 3 according to Scopus citations and 2 from Web of Science, with a total SNIP score of 4.79 and ministerial score of 366. Her research appears across domains including computer systems, mathematical software, and signal processing. Professional Recognition: ORCID identifier: 0000-0002-8399-3196 Active Scopus profile with verified publications Academic Engagement: Dr. Gokieli maintains regular consultation hours during summer semester on Wednesdays from 1:15-2:45 PM in room 1227 (building 12) or via Microsoft Teams. Special arrangements are made for part-time students with advance email notification required by 4:00 PM the previous day.

• Tomography
• Image Reconstruction
• Machine Learning
• +۲ مورد دیگر

Robert Banasiak is Research Professor at Lodz University of Technology's Institute of Applied Informatics, specializing in industrial tomography and computational imaging. His research develops advanced algorithms for 3D electrical capacitance tomography (ECT) with applications in multiphase flow monitoring and process control. Recent innovations include machine learning classifiers for flow regime identification, GPU-accelerated reconstruction techniques, and 3D-printed sensor designs. Key research areas: Real-time 3D imaging for industrial processes Machine learning applications in tomography High-performance computing for image reconstruction Additive manufacturing of sensor systems Multiphase flow quantification Technological contributions include graph-based reconstruction methods, multi-GPU algorithms for tomographic processing, and development of hybrid ECT/ERT systems. Research addresses fundamental challenges in measurement resolution, computational efficiency, and sensor design for harsh industrial environments.

• Computer Science
• Pattern Matching
• Blockchain
• +۲ مورد دیگر

Robert Susik is a researcher at the Institute of Applied Computer Science within the Faculty of Electrical, Electronic, Computer and Control Engineering at Lodz University of Technology. His core research focuses on algorithm design, particularly in pattern matching, blockchain applications, and machine learning implementations for healthcare technology. Research interests span: Computational efficiency in algorithm design Blockchain-based academic certification systems Neural networks for mobile health monitoring Human-computer interaction optimizations Recent publication trends demonstrate: Progressive shift toward applied machine learning (2021-2025) Consistent focus on optimization techniques in pattern matching Emerging work in blockchain and cryptocurrency applications Laboratory engagement includes computational algorithm development and interdisciplinary collaborations bridging computer science with healthcare technology.