جست‌وجوی استاد و پژوهشگر

Magdalena Lemańskaمشاهده پروفایل

استادیار

• Graph Theory
• Domination Theory
• Discrete Mathematics
• +۲ مورد دیگر

Dr. Magdalena Lemańska is an Assistant Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology. She has established herself as a dedicated researcher in discrete mathematics with a specialized focus on domination theory in graphs. Dr. Lemańska obtained her doctoral degree (dr) in Mathematics on January 19, 2006, from the Faculty of Mathematics, Physics and Informatics of the University of Gdańsk, marking the beginning of her academic career focused on theoretical aspects of graph structures. Her research centers on advanced topics in graph theory, particularly domination parameters and their variations. She investigates mutual visibility sets, restrained differential, isolation numbers, and super domination across various graph structures including trees, unicyclic graphs, and block graphs. Her work often examines how graph operations like edge subdivision affect these parameters, contributing to the theoretical foundations of combinatorial optimization. Dr. Lemańska's publication record shows consistent scholarly output since 2014, with recent publications in 2024-2025 indicating active ongoing research. Her work appears in reputable journals such as Discrete Applied Mathematics, Discrete Mathematics, and Discussiones Mathematicae Graph Theory, demonstrating her contributions to advancing knowledge in graph theory. She is currently involved in the COVER project (Combinatorial Optimisation for Versatile Applications to Emerging Urban problems), part of the Horizon Europe program (agreement 101182819 — COVER — HORIZON-MSCA-2023-SE-01), which began on September 16, 2024, in the Department of Algorithms and Systems Modelling. Dr. Lemańska maintains substantial teaching responsibilities at Gdańsk University of Technology with 88 teaching entries recorded, indicating significant contribution to academic instruction alongside her research activities. Her office is located in Building B, room 516a, and she can be contacted at magleman@pg.edu.pl.

• Artificial Intelligence
• Optimization Algorithms
• Machine Learning
• +۳ مورد دیگر

Robert Schaefer, a former Professor at the Faculty of Computer Science and Telecommunications of the Pedagogical University of Kraków, specialized in areas such as multi-agent systems, genetic algorithms, global optimization, and swarm intelligence. His research also encompassed graph decomposition, data mining, and finite element method (FEM) techniques. He contributed to over 6 publications and held an h-index of 1 according to Scopus and Web of Science. Though an ex-employee, his work focused on advancing heuristic models and evolutionary computation methods. Research Interests: Development of hierarchical genetic strategies Optimal decomposition techniques in graph theory Swarm intelligence applications Data mining through evolutionary algorithms Achievements: 1 notable achievement (details unspecified). Grants and advising: No specific grants or student advisees listed. Laboratories/Teams: No formal lab or team affiliations explicitly mentioned in the profile.

Piotr Bródkaمشاهده پروفایل

دانشیار

• Complex Networks
• Social Network Analysis
• Multilayer Networks
• +۲ مورد دیگر

Prof. Piotr Bródka is an Associate Professor at the Department of Artificial Intelligence within the Faculty of Computer Science and Telecommunications at Wroclaw University of Science and Technology. His research focuses on modeling and analyzing complex networks, including social networks and water supply systems, with emphasis on dynamic processes like information spread and social influence. His research examines propagation mechanisms in multilayer networks, community evolution, and social network dynamics. Recent publications explore influence maximization strategies, network diffusion frameworks, and applications in epidemiology and social behavior modeling. Scientific Awards: Top500 Innovators Scholarship at Stanford University Scholarship for Young Scientists from Ministry of Science Member of Academia Professorum Iuniorum He leads research projects funded by National Science Centre, EU, and Visegrad Fund, focusing on multilayer network analysis. As former deputy head of the Department of Artificial Intelligence, he supports research team development.

• 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.

Maciej Malawskiمشاهده پروفایل

دانشیار

• Parallel and Distributed Computing
• Cloud Computing
• Data Science
• +۵ مورد دیگر

Maciej Malawski is an Associate Professor at the Department of Computer Science, AGH University of Science and Technology, and Research Team Leader at the Sano Centre for Computational Medicine in Kraków, Poland. He holds a PhD in Computer Science (2009) and MSc degrees in Computer Science (2001) and Physics (2004). His research focuses on parallel/distributed computing, cloud technologies, and biomedical applications, with contributions to federated learning, serverless computing, and scientific workflow optimization. Education: PhD in Computer Science, AGH University (2009) MSc in Physics, Jagiellonian University (2004) MSc in Computer Science, AGH University (2001) Research Interests: Parallel computing, cloud infrastructures, serverless systems, federated learning, biomedical data analysis, and workflow scheduling. His work bridges computational methods with healthcare, such as federated learning for medical imaging and sensitivity analysis in cardiovascular models. Publications & Awards: Over 50 international publications, including top-tier venues like SC, IPDPS, and IEEE Cluster. Recognitions include the 2018 AGH Rector’s Award, Publons Peer Review Award (2018), and 1st place in the Executable Paper Grand Challenge (2011). Labs & Affiliations: Director of Sano, senior researcher at ACC Cyfronet AGH, and collaborator with CERN. Leads projects on HPC/Cloud integration and scientific computing for medicine.

Alessandro Crimiمشاهده پروفایل

عضو هیئت علمی

• Neuroimaging
• Medical Imaging
• Machine Learning in Healthcare
• +۴ مورد دیگر

Alessandro Crimi is a Visiting Lecturer at the African Institute for Mathematical Sciences (Ghana & South Africa) and founder of Yawlab (Zurich). He holds a Ph.D. in Medical Imaging from the University of Copenhagen and an MBA in International Health Management from the Swiss Tropical and Public Health Institute (University of Basel). His research focuses on neuroimaging, machine learning applications in healthcare, and global health initiatives. Crimi has extensive experience in postdoctoral research across Italy, France, and Switzerland, specializing in clinical neuroimaging for multiple sclerosis, Alzheimer’s, Parkinson’s, and glioma. He is editor of the MICCAI BrainLesion Workshop and has led healthcare projects in low-resource settings in Africa, including diabetes screening and prenatal care initiatives. Research interests include diffusion MRI, functional MRI, EEG/fNIRS connectivity analysis, quantum computing for brain network analysis, and neurotech device development. Crimi’s work emphasizes translating research into clinical solutions and addressing global health challenges through education and technology. Notable contributions include biomarker discovery for neurodegenerative diseases, green AI for fetal brain segmentation, and explainable AI for epilepsy diagnosis. He has organized workshops on brain lesion analysis and authored over 50 peer-reviewed publications.

Zdzisław Burdaمشاهده پروفایل

استاد

• Statistical Mechanics
• Random Matrices
• Complex Systems
• +۳ مورد دیگر

Prof. Zdzisław Burda is a Professor at the Department of Applied Informatics and Computational Physics, Faculty of Physics and Applied Computer Science, AGH University of Science and Technology in Kraków. His research focuses on statistical mechanics, random matrices, complex systems, quantum gravity, and quantitative finance. He holds positions in academic governance, including membership in the Rada Dyscypliny Nauki Fizyczne and Kolegium Wydziału. His research explores interdisciplinary topics such as phase transitions in condensed matter systems, spectral universality in random matrix products, and dynamical models of wealth distribution. Key contributions include studies on Yang-Lee zeros, Rényi entropy in zeta urns, and eigenvector statistics of Ginibre matrices. His work spans theoretical frameworks like free probability, non-equilibrium dynamics, and agent-based modeling. Burda collaborates extensively with experts in mathematical physics and econophysics, addressing challenges in network science, stochastic processes, and quantum gravity. His research often bridges abstract mathematical theories with real-world phenomena, such as epidemiological modeling and wealth inequality dynamics. His academic profile includes over 150 publications, reflecting a deep engagement with topics such as quantum gravity, random networks, and financial systems. He actively contributes to academic leadership and innovation within his department and faculty.

• Assistive Technologies
• Accessibility for Visually Impaired
• Computer Science
• +۵ مورد دیگر

Dr. Dariusz Mikułowski is an Assistant Professor at the Institute of Computer Science, Faculty of Natural Sciences, University of Siedlce. His primary research focuses on developing assistive technologies to enhance communication, education, and daily functioning for people with disabilities, particularly the visually impaired. He leads research teams and has contributed to over 56 publications, achieving an h-index of 3 in Scopus and Web of Science. His research areas include Assistive technologies for navigation and spatial orientation Accessibility in education and e-learning Multimodal interfaces for visually impaired users Ontology-based systems for semantic mapping Audio-based mathematics instruction tools Key projects include the Blind-enT system for object visibility assistance, the WPad BVI multilingual knowledge exchange tool, and Virtual Cubarithms for arithmetic education. His work emphasizes inclusive design principles and cross-disciplinary collaboration. Dr. Mikułowski actively participates in academic communities through Google Scholar, Scopus, and Web of Science profiles, and contributes to initiatives like Science Poland. His research has been recognized for addressing critical gaps in accessibility technologies for disabled populations.

• Information Retrieval
• Knowledge Representation
• Machine Learning
• +۳ مورد دیگر

Prof. Czesław Jędrzejek is a former faculty member at Poznań University of Technology, affiliated with the Faculty of Electronics and Information Technology. His research focuses on information retrieval, knowledge representation, machine learning, and cybersecurity. He has supervised two doctoral dissertations and contributed to interdisciplinary projects involving medical data analysis and economic crime modeling. Key research interests include ontology reasoning (e.g., OWL 2 RL), collaborative filtering algorithms, and semantic web technologies. His work bridges theoretical foundations with practical applications in healthcare informatics and network security. Prof. Jędrzejek's publications span journals like Database and STUDIA INFORMATICA , addressing topics such as RDF querying methods and adaptive audio stream transmission. He has also reviewed doctoral theses in computer vision and network systems. His advising record includes students focused on knowledge bases for economic crimes and IP audio streaming optimization. Despite being a former employee, his contributions to computational methods and interdisciplinary research remain notable.

• Automata Theory
• Infinite-State Systems
• Theoretical Computer Science
• +۳ مورد دیگر

Wojciech Czerwiński is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw. His research focuses on automata theory and infinite-state systems, particularly addressing reachability problems in Vector Addition Systems (VASS) and related models. ERC Starting Grant Principal Investigator (2021-present) Organizer of Infinite Automata Workshop (2024) Co-organizer of Friday Afternoon Seminar Former editor of Delta popular science magazine (2013-2022) His research has produced significant results in computational complexity for VASS reachability, earning Best Paper Awards at CONCUR 2019 and STACS 2017. Current work explores separability problems, unambiguous systems, and structural properties of infinite-state models through the INFSYS ERC project. Key scientific contributions include determining the Ackermann-completeness of VASS reachability and developing new techniques for universality in unambiguous register automata. He actively participates in community-building efforts through workshops and seminars.

• affective computing
• emotion recognition from physiological data
• machine learning
• +۳ مورد دیگر

Dr. Stanisław Saganowski serves as a Professor at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, specializing in the Department of Artificial Intelligence. With expertise in affective computing and machine learning, he focuses on emotion recognition from physiological data, group evolution prediction in complex networks, and mobile technology applications. His research interests include: Affective Computing Emotion Recognition using Wearables Machine Learning for Imbalanced Data Network Analysis of Social Dynamics Predictive Modeling in Complex Systems Mobile Healthcare Applications Recent publications demonstrate interdisciplinary approaches combining data science with behavioral analysis and network theory. While no formal awards are listed, his work contributes to both theoretical advancements and practical implementations in human-centric AI systems.

Filip Turobośمشاهده پروفایل

استادیار

• Mathematical Analysis
• Automotive Safety Engineering
• Transportation Systems
• +۳ مورد دیگر

Filip Turoboś is an Assistant Professor at the Division of Insurance and Capital Markets , Lodz University of Technology . His academic profile indicates active research engagement without mention of part-time status, retirement, or former institutional affiliations. Research Focus: Dr. Turoboś demonstrates interdisciplinary expertise spanning: Mathematical Analysis : Including semimetric spaces, fixed-point theorems, and topological extensions Automotive Safety Engineering : Specializing in precrash velocity modeling using machine learning and optimization techniques Urban Mobility & Transport : Analyzing behavioral patterns during crises (e.g., pandemics) and regulatory impacts Emerging Technologies : Blockchain applications in security and ontological ecosystems Publication Trends (2020-2024): Recent works cluster into three dominant themes: Mathematical foundations of generalized metric spaces and functional analysis (30% of publications) Data-driven transportation studies focusing on pandemic-related behavioral shifts and urban policy effects (40%) Automotive crash reconstruction using AI/statistical methods and blockchain cryptography (30%)

• Artificial Intelligence
• Machine Learning
• Image Processing
• +۶ مورد دیگر

Szymon Antoni Głowania is a Researcher at the Department of Machine Learning within the Faculty of Informatics and Communication at the University of Economics in Katowice. Holding an MSc degree, he serves as a teaching and research assistant with his scholarly work focused entirely on information and communication technology (ICT) as documented by institutional records. His research expertise centers on Artificial Intelligence and Machine Learning , with significant contributions in Image Processing , Financial Risk Analysis , and Data Modeling . Głowania's work demonstrates strong interdisciplinary applications, connecting computer science with finance, cultural studies, industrial contexts, and chemistry as evidenced by his publication keyword profile which includes terms like 'sztuczna inteligencja' (artificial intelligence), 'ryzyko' (risk), 'finanse' (finance), and 'przemysł' (industry). Analysis of his 19 publications reveals a consistent research trajectory with increasing sophistication in applying machine learning techniques to practical problems. His work shows progression from foundational classification algorithms to specialized applications in financial risk assessment, industrial quality control, and cultural heritage preservation. The interdisciplinary nature of his research is particularly notable, bridging economic applications with technical AI implementations. Głowania has achieved an h-index of 3 according to both Scopus and Web of Science citations, with a total impact factor of 8.938 and a SNIP score of 5.077. His ministerial score of 1,035 reflects the significance of his contributions within the Polish academic system. As a teaching and research assistant, he contributes to academic programs within the Faculty of Informatics and Communication. While specific advising relationships aren't documented, his position suggests involvement in mentoring students within the Department of Machine Learning. His ORCID identifier (0000-0003-1137-4350) and presence across multiple academic platforms indicate active engagement with the international research community. His research appears conducted within the Department of Machine Learning's facilities, with practical applications particularly evident in finance and industrial contexts, aligning with the University of Economics in Katowice's mission to connect technical expertise with economic applications.

• Numerical Analysis
• High-Performance Computing
• Computational Mathematics
• +۴ مورد دیگر

Maciej Woźniak serves as a university professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, actively contributing to research and teaching staff while participating in the Computer Science Discipline Council and College of the Faculty. His office is located at D-17, ul. Kawiory 21, III, 4.58, with primary contact via macwozni@agh.edu.pl. His research spans computational mathematics and high-performance computing, specializing in parallel algorithms for numerical simulations including isogeometric analysis, finite element methods, and multi-frontal solvers. Key applications address environmental challenges like hail suppression and urban smog reduction, alongside biomedical modeling of tumor growth and airborne pathogen dispersion. Methodological innovations focus on GPU acceleration, shared-memory architectures, and efficient integration techniques for partial differential equations. Analysis of his 2019-2024 publications reveals consistent advancement in parallel computational frameworks for environmental and biomedical applications, demonstrating interdisciplinary impact through experimental validation of hail cannons, smog reduction technologies, and pathogen dispersion modeling during the COVID-19 pandemic. His work bridges theoretical algorithm development with real-world problem solving across physics, engineering, and life sciences. Scientific Awards: No specific awards were documented in the provided sources. Student advising, research grants, laboratory affiliations, and educational background details beyond his PhD, DSc, and Eng. titles remain unspecified in the available materials, though his extensive publication record suggests active mentorship and collaborative research leadership.

Szymon Głąbمشاهده پروفایل

استاد

• Mathematical Analysis
• Set Theory
• Functional Analysis
• +۶ مورد دیگر

Dr. DSc, Eng Szymon Głąb is a research and teaching university professor at the Department of Applications of Modern Mathematical Analysis, Lodz University of Technology. His work bridges abstract mathematical analysis with applied problems in computer science and topology. Fields of Interest Mathematical Analysis Set Theory Functional Analysis Algebrability Measure Theory Density Operators His research focuses on the interplay between set theory, measure theory, and functional analysis, particularly in characterizing small sets (e.g., Haar-null sets, meager ideals) and studying algebrability of non-measurable functions. Recent publications highlight his contributions to inverse Fraïssé limits, duality for posets, and topological properties of ideals. Notable collaborations include advising Jarosław Swaczyna (2020) on Haar-I sets, Jacek Marchwicki (2018) on conditionally convergent series, and contributing to blockchain applications in computer science.