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Sabina LichołaiView profile

Researcher

• Epigenetics
• Bioinformatics
• Genetics
• +3 more

Sabina Lichołai is a Medical Doctor and researcher affiliated with ACC Cyfronet AGH, part of the AGH University of Science and Technology. Her work focuses on epigenetic mechanisms in disease pathogenesis, integrating high-throughput biological measurements with advanced bioinformatics analysis. She serves as a genetics consultant for Sano and collaborates on international scientific projects. Her research interests emphasize molecular profiling of diseases using cutting-edge techniques, including next-generation sequencing and machine learning models for clinical prediction. Notable projects include analyzing circular RNAs in endothelial cells, optimizing liquid biopsy methods for brain tumors, and developing subgroup-based risk assessment for ANCA vasculitis using latent class analysis. She also explores miRNA roles in cardiovascular diseases and computational tools like GraphTar for predicting miRNA targets.

Alessandro CrimiView profile

Academic

• Neuroimaging
• Medical Imaging
• Machine Learning in Healthcare
• +4 more

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.

• Information Retrieval
• Knowledge Representation
• Machine Learning
• +3 more

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.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +4 more

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.

• Conceptual Data Modeling
• Semantic Knowledge Bases
• Ontology Engineering
• +3 more

Marcin Jodłowiec, PhD, is an academic staff member at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, affiliated with the Department of Applied Informatics. His research focuses on conceptual data modeling, semantic knowledge bases, and neural network optimization. Key interests include association-oriented database metamodels, ontology engineering, and UAV navigation systems. Recent publications emphasize metamodel semantics formalization, OWL translation techniques, and contextual neural network architectures. Research trends show integration of graph theory with semantic modeling and AI applications in real-time environments. He has not listed any scientific awards or student advisees in the provided data.

• Numerical Analysis
• High-Performance Computing
• Computational Mathematics
• +4 more

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.

• Computer Science
• Artificial Intelligence
• Smart Cities
• +5 more

Sebastian Ernst is a Lecturer at the Department of Applied Informatics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków. His research focuses on Computer Science , Artificial Intelligence , and Smart Cities , with a strong emphasis on Geographic Information Systems (GIS) and Energy Efficiency in urban environments. University: AGH University of Science and Technology Faculty: Electrical Engineering, Automatics, Computer Science and Biomedical Engineering Department: Applied Informatics Position: Lecturer Contact: ernst@agh.edu.pl Office: C-2, 4th floor, room 424 Phone: +48 12 617 51 95 His recent publications highlight trends in Graph-Based Modeling , Smart Lighting Systems , and Robust Algorithm Design for urban infrastructure. He explores Pairwise Comparisons , GIS Applications , and Energy Conservation through computational methods.

• Fuzzy Logic
• Temporal Logic
• Knowledge Engineering
• +4 more

Krystian Jobczyk is a Professor at the Department of Applied Informatics , Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering , AGH University of Science and Technology in Kraków. His research focuses on fuzzy logic, temporal logic, and their applications in knowledge engineering, artificial intelligence, and business process modeling. He is affiliated with the Disciplinary Council for Technical Information Technology and Telecommunications at AGH. Research Interests : Fuzzy logic systems and their categorial interpretations Temporal logic frameworks (e.g., Halpern-Shoham logic) for digital circuits and planning Process mining and business process modeling Philosophical and model-theoretic aspects of logic Publications highlight his work on fuzzy-temporal integrations, multi-agent systems, and logic-based verification methods. Key trends include applications of fuzzy integral logic, convolution-based approaches, and interdisciplinary bridges between logic and computer science. Contact : jobczyk@agh.edu.pl | krystian_jobczyk@op.pl | +48 12 617 50 64 | Room 319, Building C-2, 3rd floor.

• Graph transformations
• Smart city technologies
• IoT networks
• +6 more

Leszek Kotulski is a Professor at the Department of Applied Informatics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. His office is located in building C-2, 4th floor, room 425, with contact number +48 12 617 51 93 and email kotulski@agh.edu.pl. He serves on the Disciplinary Council for Technical Information Technology and Telecommunications. Professor Kotulski's research focuses on graph-based computational methods applied to smart city infrastructure, particularly in energy-efficient street lighting systems and IoT networks. His work integrates graph transformations, multi-agent systems, and spatial data processing to optimize urban infrastructure. He has developed innovative approaches for digital twin generation, lighting system modernization, and energy conservation in urban environments. His research bridges computer science with practical urban engineering challenges, creating solutions that improve both efficiency and sustainability of city infrastructure. Analysis of his recent publications reveals a clear research trajectory focused on applying graph theory to urban infrastructure challenges, with a particular emphasis on lighting systems. His work demonstrates increasing sophistication in integrating IoT networks with graph-based computational models, evolving from basic lighting control systems to comprehensive smart city solutions involving digital twins and distributed network configurations. The interdisciplinary nature of his research connects computer science, electrical engineering, and urban planning, with consistent contributions to both theoretical frameworks and practical implementations. Professor Kotulski has made significant contributions to the field through his development of graph-based methods for optimizing public lighting systems, energy conservation in smart cities, and spatial data processing techniques. His work on reactive power compensation algorithms for street lighting and graph-based approaches to lighting retrofit projects demonstrates practical applications of his theoretical research. His research activities include leadership in the GRADIS (Graph-based Distributed Adaptive Design) project and contributions to the Alvis modeling language for embedded systems. He has developed multi-agent systems supporting automated photometric computations and large-scale lighting design, creating frameworks that enable more efficient urban infrastructure planning and management.

• Automation
• Robotics
• Control Systems
• +7 more

Michał Bartyś, PhD Eng., is an Associate Professor at the Institute of Automatic Control and Robotics, Faculty of Mechatronics, Warsaw University of Technology. His research focuses on automation, robotics, and industrial process monitoring, with a specialization in control systems, fault detection, and real-time diagnostics. Science discipline: Automation, Electronics, Electrical Engineering, and Space Technologies (AEEEST) Expertise: Fault detection, actuator dynamics, fuzzy logic applications Projects: 3 funded initiatives Patents: 6 inventions His recent work examines electro-pneumatic actuator dynamics, fault diagnosis robustness, and energy-efficient robotic operations. He employs graph theory and hybrid inference methods to enhance diagnostic systems. Scientific output includes publications on Modbus vulnerabilities, image fusion for real-time monitoring, and decentralized industrial diagnostics. He has promoted 143 theses and supervises ongoing research in mechatronic systems and industrial safety. Contact: michal.bartys@pw.edu.pl

• Statistical Mechanics
• Complex Systems
• Sociophysics
• +4 more

Krzysztof Malarz is an Associate Professor and Head of the Department of Applied Computer Science and Computational Physics at AGH University of Science and Technology's Faculty of Physics and Applied Computer Science. He leads the Complex Systems Group, focusing on interdisciplinary research bridging physics and social sciences. His editorial responsibilities include roles at Open Physics and PLoS One . Research interests span: Statistical mechanics of phase transitions Percolation theory in complex lattices Sociophysical models of opinion dynamics Network science and computational approaches to social systems Econophysics and wealth distribution Recent publications (2021-2025) demonstrate consistent focus on universality in percolation thresholds, social balance dynamics, and opinion formation models. Work frequently employs Monte Carlo methods, agent-based simulations, and analytical physics techniques to explore critical phenomena in social and physical systems. The Complex Systems Group under his leadership investigates emergent behaviors in networked systems, with resources including high-performance computing facilities. No funded projects or student advisees are detailed in available sources.

• Bioinformatics
• Computational biology
• Graph theory
• +5 more

Professor Marta Kasprzak is a distinguished faculty member at Poznań University of Technology, holding a position in the Faculty of Computer Science and Telecommunications within the Institute of Computer Science. With the academic title of 'prof. dr hab. inż.', she represents the highest academic rank in the Polish system, indicating her significant contributions to her field. Her research interests focus on the intersection of computer science and biology, particularly in bioinformatics and computational biology. Professor Kasprzak specializes in applying graph theory and combinatorial algorithms to solve complex problems in genomics and DNA sequencing. Her work consistently bridges theoretical computer science with practical biological applications, making substantial contributions to both fields. Analysis of her recent publications reveals a strong trend toward increasingly sophisticated applications of graph theory to biological problems. Her 2024 publications span diverse topics from task allocation using Steiner systems to RNA World modeling with inhibitors, demonstrating both theoretical depth and biological relevance. The consistent publication output across decades indicates sustained research productivity and evolving expertise in computational approaches to biological challenges. Professor Kasprzak has supervised doctoral students including Maciej Antczak (2013) and Aleksandra Świercz (2007), and has served as a reviewer for numerous other dissertations in related fields. Her academic lineage connects to prominent researchers like Jacek Błażewicz, indicating integration within a strong research tradition in computational biology. Her research group at the Institute of Computer Science focuses on developing algorithmic solutions for biological data analysis, with particular emphasis on graph-based approaches to genomic sequence analysis and molecular modeling. This work contributes to the broader understanding of biological systems through computational methods and provides valuable tools for the bioinformatics community.

Krzysztof OlińskiView profile

Assistant Professor

• Discrete optimization
• Control optimization
• Optimal control
• +6 more

Krzysztof Oliński serves as an Assistant Professor at the Department of Decision Systems and Robotics within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. His academic profile demonstrates a strong focus on control engineering with particular expertise in optimization methodologies for complex dynamic systems. Dr. Oliński's research centers on discrete optimization techniques applied to control theory, with significant contributions to optimal control strategies for nonlinear dynamic processes. His work bridges theoretical foundations with practical industrial applications, particularly in manufacturing and pipeline systems. He has developed innovative approaches including graph-based representations of state-space dynamics and agent-based optimization methods. His keyword profile reveals expertise in computational intelligence, swarm algorithms, and intelligent manufacturing systems, reflecting his interdisciplinary approach to solving complex control problems. An analysis of his publication history from 2007-2012 shows a clear research trajectory evolving from foundational work on fault-tolerant control systems to more sophisticated methodologies like the 'toolgraph' approach. His early publications focused on discrete optimization techniques for control planning, while his later work expanded to include agent-based strategies and integration of natural and artificial intelligence in production systems. His most impactful contributions involve novel representations of state-space dynamics through flow graph structures that enable more effective control strategy design. Dr. Oliński maintains an active research presence with publications in both Polish and international journals including Mathematical Problems in Engineering and the International Journal of Applied Mathematics and Computer Science. His work demonstrates consistent theoretical rigor combined with practical relevance to industrial applications, particularly in intelligent manufacturing systems and pipeline dynamics modeling.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +3 more

Marcin Sawiński is a Researcher and Teaching Assistant at the Department of Information Systems within the Institute of Informatics and Quantitative Economics at Poznań University of Economics and Business. With expertise spanning computer and information sciences (75%) and management and quality studies (25%), his work focuses on developing AI tools for addressing misinformation and enhancing fact-checking processes. His primary research interests include: Artificial Intelligence applications for fake news detection Natural Language Processing techniques for misinformation analysis Machine learning approaches to credibility assessment Persuasion techniques detection in social media content Development of robust language models for fact-checking systems Analysis of political narratives during crisis events like pandemics Dr. Sawiński's recent publications (2023-2025) demonstrate a concentrated research trajectory focused on applying transformer-based models to misinformation challenges, particularly in Slavic languages. His work spans multiple dimensions of the fake news problem, from detection of persuasion techniques to cross-lingual transfer learning for check-worthiness assessment. He has been actively involved in international competitions like CheckThat! Lab at CLEF, where his team (OpenFact) has developed innovative approaches to fact-checking challenges, including adversarial text generation to test model robustness. His scientific contributions include 15 publications with over 50 citations, reflecting his growing impact in the field of AI for misinformation detection. His research often involves collaboration with colleagues including Krzysztof Węcel, Witold Abramowicz, and Ewelina Księżniak.