جست‌وجوی استاد و پژوهشگر

Paweł Pilarczykمشاهده پروفایل

دانشیار

• dynamical systems
• computational topology
• rigorous numerics
• +۲ مورد دیگر

Paweł Pilarczyk is an Associate Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology, where he has been employed since 2018. His research spans multiple mathematical disciplines with applications across various scientific fields. Dr. Pilarczyk's research interests include dynamical systems , computational topology , rigorous numerics , and applications of advanced computational techniques . His work bridges theoretical mathematics with practical applications in neuroscience, cardiology, ecology, and epidemiology. Analyzing his publication record from 2025 back to 2007 reveals a consistent focus on rigorous mathematical approaches to understanding complex systems. His recent work shows increasing interdisciplinary applications, particularly in medical diagnostics (sleep apnea detection, heart rate variability analysis) and neuroscience (neuron modeling), while maintaining strong foundations in pure dynamical systems theory. As project manager for the OPUS-funded "Topological and numerical methods in dynamical systems" (since 2022), he leads significant research initiatives at the Department of Differential Equations and Mathematical Applications. Dr. Pilarczyk maintains an active research program with collaborators across multiple institutions, evidenced by his extensive publication record and research data sets available through Gdańsk Tech's Bridge of Knowledge platform.

Jacek Banasiakمشاهده پروفایل

استاد

• Mathematical Modeling
• Population Dynamics
• Fragmentation-Coagulation Processes
• +۵ مورد دیگر

Professor Jacek Banasiak holds a prestigious DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering at the University of Pretoria, Department of Mathematics and Applied Mathematics. He also maintains strong academic ties with Lodz University of Technology in Poland where he serves as a research professor in the Department of Mathematical Modeling. His career spans several decades with extensive contributions to mathematical modeling, particularly in population dynamics, fragmentation-coagulation processes, and epidemiological modeling. Professor Banasiak's research interests center on mathematical modeling of biological and physical processes, with particular expertise in singular perturbation theory, semigroup theory, and transport equations on networks. His work bridges theoretical mathematics with practical applications in epidemiology, ecology, and population biology. He has developed sophisticated mathematical frameworks for understanding fragmentation-coagulation phenomena, malaria transmission dynamics, and savanna ecosystem modeling. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on climate-based malaria models, multiscale epidemiological systems, and mathematical analysis of growth-fragmentation equations. His publications appear in top-tier journals across mathematical analysis, epidemiology, and mathematical biology fields, demonstrating the interdisciplinary nature of his work. DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering Author of numerous influential publications spanning from 1984 to 2025 Editor of special issues and author of several books including 'Introduction to Mathematical Methods in Population Theory' (2025) Professor Banasiak has supervised over 15 PhD students, many of whom have gone on to successful academic careers. His mentoring spans topics including fragmentation-coagulation with transport effects, telegraph systems on networks, and mathematical modeling of malaria transmission. His research has attracted significant funding, particularly through his SARChI Chair position which supports advanced mathematical research in biosciences and bioengineering.

Igor Wojnickiمشاهده پروفایل

استاد

• Smart City Systems
• Graph-Based Computational Methods
• Energy-Efficient Lighting Control
• +۴ مورد دیگر

Igor Wojnicki is a Professor at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, where he serves as Vice-Dean of the Faculty of Cooperation and Education. His primary affiliation is with the Department of Applied Informatics, where he maintains an active research laboratory focused on knowledge engineering and smart systems. His research spans multiple domains with evolving focus: Early career: Deductive databases and rule-based inference engines (PhD thesis on "A Rule-based Inference Engine Extending Knowledge Processing Capabilities of Relational Database Management Systems") Mid-career: Graph-based knowledge representation and Tabular Trees (XTT predecessor) Current focus: Smart city applications, particularly energy-efficient lighting control systems and graph-based urban data integration His recent publications demonstrate a clear trajectory toward applied urban computing, with over 15 significant papers in the last five years addressing smart city infrastructure optimization. Key themes include dynamic street lighting control, graph-based computational methods for urban environments, and energy conservation in public infrastructure. Wojnicki actively contributes to academic-practical collaboration through initiatives like the Green AGH Campus Project and IBM academic partnerships. His technical leadership includes development of the ReDaReS system for relational database knowledge processing and the Jelly View technology for advanced database queries. His laboratory maintains strong industry connections, particularly with IBM through student internship programs and technology transfer initiatives. The team produces both theoretical frameworks and practical implementations, with notable outputs including the Osiris GUI system and Magellan GPS software for Poland.

Jan Majkutewiczمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Knowledge Representation
• +۳ مورد دیگر

Jan Majkutewicz 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 academic position focuses on advancing computational methodologies in natural language processing and knowledge representation systems. His research spans Natural Language Processing, Machine Learning, and Knowledge Representation with specific expertise in Large Language Models and graph-based embeddings. Current work investigates human-AI alignment through historical text analysis and neural representations of semantic hierarchies. Methodological innovations include cost-effective preference dataset generation and category graph embedding techniques that preserve structural relationships in knowledge bases. Publication analysis reveals consistent specialization in NLP and knowledge representation. The 2025 EditPrefs framework leverages historical text edits to address dataset scarcity in preference learning, while the 2021 Wikipedia category embedding research demonstrates practical applications for taxonomy navigation and parent category prediction through neural graph representations.

Mauricio Ayala Rincónمشاهده پروفایل

استاد

• Term Rewriting Systems
• Automated Reasoning
• Theorem Proving
• +۱۳ مورد دیگر

Mauricio Ayala Rincón is a Full Professor at Universidade de Brasília, affiliated with the Department of Computer Science and the Department of Mathematics. He is a leading researcher in computational logic, formal methods, and term rewriting systems, and heads the Theory of Computation research group (GTC/UnB). Research Interests: His work focuses on the formalization of mathematical and computational theories using proof assistants like PVS. Key areas include term rewriting systems, equational and rewrite-based deduction, automated reasoning, unification, nominal logic, formal verification, and applications in genomics and evolutionary algorithms. He also explores ethics in AI and mechanized mathematics. Recent Publication Trends: His recent articles (2023–2024) reflect a strong emphasis on formalizing algebraic and logical theories in PVS, advancing nominal equational reasoning, anti-unification over algebraic theories, and applying evolutionary algorithms to computational biology. The work is highly theoretical yet applied in verification and combinatorics. Scientific Awards: Best Paper Award at CICM 2023 for "Nominal AC-matching" Advising and Grants: He actively seeks PhD students in algorithmics, formal methods, theorem proving, and AI ethics. He has led numerous research projects, evidenced by extensive publications and editorial roles. He has not listed specific grants, but his continuous output suggests sustained funding. Labs and Teams: He leads the Grupo de Teoria da Computação (GTC/UnB) , which develops PVS libraries for term rewriting (TRS), nominal theories, and evolutionary algorithms. The group maintains public repositories and contributes to the NASA PVS library.

• Quantum Computing
• Hybrid Quantum-Classical Algorithms
• Workflow Scheduling
• +۳ مورد دیگر

Katarzyna Rycerz is a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. She holds the academic rank of Lecturer as part of the assistant professors group, with her office located at D-17, ul. Kawiory 21, room II, 3.54. Her research spans quantum computing, hybrid quantum-classical algorithms, and distributed systems, with significant contributions to workflow scheduling and complex network optimization. Dr. Rycerz's primary research focuses on quantum optimization techniques, particularly quantum annealing and variational algorithms applied to combinatorial problems. Her work bridges theoretical quantum computing with practical high-performance computing challenges, including load rebalancing in HPC systems and community detection in complex networks. She has developed software tools like QHyper for hybrid quantum-classical optimization and explored quantum solutions for thermoelectric properties in graphene and quantum game theory paradoxes. Her 15 most recent publications (2019-2025) reveal a dominant trend toward quantum-classical hybrid methods, with 80% focused on quantum annealing applications for workflow scheduling, network analysis, and optimization problems. Key themes include D-Wave system implementations, quantum game theory extensions, and thermoelectric material simulations, demonstrating a consistent shift from earlier multiscale simulation work toward cutting-edge quantum computing applications. No scientific awards are documented in the available information. Details regarding student advising, research grants, laboratory affiliations, or collaborative teams are not provided in the source materials.

• Mutation Testing
• Model Validation
• Software Engineering
• +۹ مورد دیگر

Dr. Eng. Joanna Strug is a lecturer at the Department of Automation and Computer Science within the Faculty of Electrical and Computer Engineering at Cracow University of Technology. Her research focuses on software testing methodologies, machine learning applications in test automation, and database technologies. Specialization in mutation testing and fault injection Expertise in UML/OC validation and business process testing Active researcher in relational and non-relational database modeling Recent research trends indicate a focus on database technology performance analysis, including: Comparative benchmarking of relational vs. NoSQL databases Cost optimization in mutation testing using machine learning Structural similarity applications for test classification Validation frameworks for service-oriented architectures Model-based testing of business processes Bytecode-level mutant classification Her work appears in prominent venues including: Information Systems Architecture and Technology Artificial Intelligence and Soft Computing Journal of Engineering International Conference on Software Engineering (ENASE)

• Computational Chemistry
• Structural Biology
• Biophysics
• +۳ مورد دیگر

Tamar Schlick is a Professor at New York University, affiliated with the Courant Institute of Mathematical Sciences and the Department of Chemistry. She holds roles as an Associate (Lead) Editor for the International Journal for Multiscale Computational Engineering and has contributed to numerous journals. Education: B.S. in Mathematics, Wayne State University (1982) M.S. and Ph.D. in Applied Mathematics, Courant Institute of Mathematical Sciences, NYU (1984 and 1987) Research Interests: Tamar Schlick focuses on biomolecular modeling, chromatin dynamics, RNA structure prediction, and computational methods for understanding biological systems. Her work integrates computational simulations with experimental data to study complex systems like DNA repair mechanisms, histone interactions, and RNA folding pathways. Her research bridges disciplines such as biophysics, biochemistry, and applied mathematics. Articles Trends: Her recent publications emphasize multiscale modeling in chromatin organization, RNA structural prediction using graph theory, and energy landscapes in biomolecular interactions. These studies highlight advancements in computational tools and their applications in understanding fundamental biological processes. Awards: Over 20 awards, including the John Simon Guggenheim Fellowship (2000-2001), American Physical Society Fellow (2005), and Aspen Health Forum Fellow (2007). Recognitions for contributions to computational chemistry, RNA science, and biomolecular modeling. Advising & Grants: While specific student advisees are not listed, her research groups collaborate on projects funded by NIH, NSF, and other agencies. She leads teams advancing computational frameworks like RAGTOP and CHSalign for RNA structure analysis. Labs/Teams: Active in NYU’s Courant Institute and NYU-ECNU Center for Computational Chemistry, focusing on interdisciplinary research in computational biophysics and molecular modeling.

• Computer Graphics
• Network Programming
• 3D Modeling
• +۴ مورد دیگر

Dr. Wojciech Palacz is a Lecturer at Jagiellonian University, specializing in Computer Graphics, Network Programming, and 3D Modeling. He teaches courses including Computer Graphics, Network Programming, Web Application Design, and Introduction to 3D Modeling. His research focuses on graphical interfaces, mobile networks, and advanced computer graphics techniques. He actively publishes in areas of graph transformation, collaborative design systems, and robot navigation algorithms. His teaching involves both full-time and part-time studies, with courses delivered remotely and asynchronously. He emphasizes practical projects, such as web application design and network programming assignments, requiring students to implement REST APIs and client-server architectures. His courses often involve team projects, simulations, and user interface prototyping. Palacz's recent work includes studies on indoor robot navigation using BIM/IFC models and the application of hierarchical graph systems in creative design. He maintains an active presence in academic collaborations, with publications in journals like Machine GRAPHICS & VISION and conferences like ICAISC.

• Statistical Physics
• Network Science
• Social Dynamics
• +۳ مورد دیگر

Anna Chmiel serves as an Assistant Professor at the Faculty of Physics, Warsaw University of Technology, where her research bridges statistical physics with social dynamics through computational modeling of complex systems. Her work demonstrates significant contributions to understanding collective phenomena in networked environments, particularly through interdisciplinary applications of physical models to social science questions. Her primary research domains include: Statistical Physics of Social Dynamics Multiplex and Polarized Network Analysis Opinion Dynamics and Phase Transitions Collective Emotions in Online Communities Epidemic-Opinion Co-evolutionary Systems Anomalous Diffusion Processes Analysis of her 15 most recent publications (2016-2025) reveals a methodological trajectory from foundational network studies toward sophisticated multiplex frameworks. Key innovations include modeling temperature-noise interplay in opinion dynamics (2025), documenting phase transition 'zoology' in asymmetric q-voter systems (2020), and pioneering cyberemotions research (2016-2017). Her work consistently examines critical phenomena in coupled dynamical processes, with recent emphasis on time-scale dependencies in epidemic-opinion co-dynamics (2022) and structural properties of layered networks (2018). No scientific awards or major honors are documented in available institutional records. Available materials indicate no formal graduate student supervision roles or externally funded research grants. Her institutional profile focuses exclusively on research outputs without mention of mentorship activities or grant leadership. Dr. Chmiel's theoretical work appears integrated within the Faculty of Physics' broader computational research initiatives. No dedicated laboratory or named research team is specified, suggesting collaborative engagement with university-wide physics and interdisciplinary research groups focused on complex systems.

Marcin Pietranikمشاهده پروفایل

استادیار

• Ontology Alignment and Evolution
• Semantic Web Technologies
• Fuzzy Logic Applications
• +۳ مورد دیگر

Marcin Pietranik is an Assistant Professor at the Department of Applied Informatics within the Faculty of Information and Communication Technology at Wroclaw University of Technlogy . His work focuses on ontology alignment, evolution, and semantic web technologies, with applications in artificial intelligence and data integration. Research Interests : Ontology alignment, fuzzy logic frameworks, automatic knowledge integration, and semantic distance metrics. Affiliation : Wroclaw University of Technlogy, Faculty of Information and Communication Technology, Department of Applied Informatics. Article Trends (15 most recent): Ontology alignment methods using fuzzy logic and semantic attributes Applications of machine learning to fake news detection Business rule validation against domain specifications Deep learning for agricultural tasks (grapevine growth stages) Consensus-building algorithms in multi-agent systems

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.

• Graph Grammars
• Artificial Intelligence
• Algorithms and Data Structures
• +۳ مورد دیگر

Dr. Anna Paszyńska is a Lecturer at Jagiellonian University, specializing in computational science and graph-based algorithms. Her research focuses on graph grammars, artificial intelligence, and numerical methods for solving complex engineering problems. She has contributed to advanced simulations in environmental modeling, tsunami prediction, and adaptive finite element methods. Her work integrates graph theory with computational techniques to optimize solver performance and address real-world challenges like pollution tracking and disaster simulations. Affiliation: Jagiellonian University Key Research Areas: Graph grammars, AI-driven simulations, computational fluid dynamics, and numerical algorithms. Her recent publications highlight innovations in physics-informed neural networks (PINNs) for solving PDEs and optimizing tsunami and pollution models. She actively collaborates on interdisciplinary projects involving environmental science, engineering, and computer science.

• Virtual Reality
• 3D Web Technologies
• Ontology Engineering
• +۵ مورد دیگر

Jakub Flotyński serves as a Professor in the Department of Information Technology within the Institute of Informatics and Quantitative Economics at Poznan University of Economics and Business. His research spans computer science with a strong focus on virtual reality, 3D web technologies, and semantic modeling, combining 75% information and communication technology with 25% management and quality studies. His research interests center on Virtual Reality , Extended Reality (XR) , and Semantic Web technologies , specializing in explorable environments, knowledge-based adaptation of interfaces, and ontology-driven 3D content modeling. His work bridges technical innovation with practical applications in industrial training, data visualization, and immersive experiences. Analysis of his 15 most recent publications (2020-2025) reveals a consistent focus on E-XR (Explorable Extended Reality) development frameworks, semantic annotation systems, and knowledge-based adaptation techniques. His research demonstrates strong interdisciplinary connections between computer science, semantic technologies, and practical business applications, with particular emphasis on industrial training scenarios and data visualization systems. Jakub Flotyński maintains an active research profile with 51 documented publications and a ministerial score of 1,270. His work is supported by two documented research projects and eight professional activities. He utilizes semantic technologies and knowledge representation systems to advance XR interface design and 3D content behavior modeling.

Agata Filipowskaمشاهده پروفایل

استادیار

• search systems
• profiling
• information extraction
• +۹ مورد دیگر

Agata Filipowska is an Assistant Professor at the Department of Information Systems, Institute of Informatics and Quantitative Economics, Poznań University of Economics and Business. Her work bridges computer science, semantic web technologies, and management studies, with a focus on data quality, profiling, and business process management. PhD in Computer Science (2010) and Economics (2009) Active in semantic technologies, machine learning, and art market analytics Key projects include FP7 LOD2 and USE-ME.GOV . Research Interests: Search systems, profiling, information extraction, business process management, and semantic web applications. Her interdisciplinary approach addresses challenges in data quality, user behavior, and art market analysis. Article Trends: Recent work emphasizes knowledge graph ensembles (2024), art market data enrichment, and mobility-based behavioral biometrics. Earlier studies focus on BPM, social telco applications, and Polish language NLP. Activities: She contributes to semantic interoperability, microgrid energy forecasting, and telecom data analysis. Her collaborations span 50 co-authors, including Anna Fensel, Dominik Filipiak, and Piotr Kałużny.