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Krzysztof KluzaView profile

Assistant Professor

• Artificial Intelligence
• Business Process Management
• Knowledge Representation
• +2 more

Krzysztof Kluza is an Assistant 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 Krakow, Poland. He is also a member of the KRaKEn research group and previously served as Deputy Dean for Education from 2016 to 2020. His educational background includes a Master of Science in Automatics & Robotics from AGH University, a Master of Arts in Cultural Studies from Jagiellonian University, postgraduate studies in Contemporary China at Jagiellonian University, and a Masters of Didactics Program at University College London. Dr. Kluza's research focuses on the intersection of artificial intelligence and business process management. His work explores how AI technologies, particularly Large Language Models, can enhance business process modeling, analysis, and education. He has made significant contributions to process mining, knowledge representation, and the integration of business rules with process models. His recent publications show a strong emphasis on AI applications in business process management, with particular focus on educational contexts and sustainability. The research spans multiple domains including food computing, music informatics, disaster management, and biomedical engineering applications. Dr. Kluza has been actively involved in academic service, having served as Secretary of the board of the Polish Artificial Intelligence Society from 2009 to 2017 and as Vice President of the Creativity and Innovation Lab Foundation from 2014 to 2019. His interdisciplinary approach bridges computer science, business process management, and educational theory, making significant contributions to both academic research and practical applications in business process modeling and analysis.

• Artificial Intelligence
• Business Process Modeling
• Knowledge Engineering
• +9 more

Antoni Ligęza 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 artificial intelligence, business process modeling, constraint programming, and knowledge engineering. He actively explores explainable AI methodologies, including grammatical evolution and temporal logic-based approaches.

Piotr SzwedView profile

Lecturer

• Data Mining
• Software Engineering
• Computational Intelligence
• +5 more

Dr inż. Piotr Szwed is a Lecturer in the Department of Applied Computer Science at AGH University of Science and Technology, Kraków. He is affiliated with the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering (EAIiIB), where he teaches courses ranging from imperative and object-oriented programming to data exploration and computational intelligence. Research Interests: Data mining and knowledge discovery Fuzzy cognitive maps and their applications in transportation, risk assessment, and security Software engineering methodologies, including agile and ontology-driven development Intelligent transportation systems (ITS) and real-time traffic management Natural language processing for Polish texts, stylometry, and authorship attribution Ontology engineering, enterprise architecture verification, and model checking His work often bridges theoretical computer science with practical applications in urban mobility, safety systems, and enterprise software. Scientific Contributions: Pioneered the integration of fuzzy cognitive maps with evolutionary algorithms to predict traffic flows. Developed rule-based and machine-learning approaches to determine speed limits from geospatial data. Contributed to the INSIGMA intelligent transportation system aimed at enhancing urban mobility in Polish cities. Created formal verification techniques for ArchiMate business processes using NuSMV model checking. Advanced stylometric analysis for Polish texts, introducing part-of-speech features for authorship attribution. Teaching & Academic Service: Regularly teaches Programming (C, C++, Java), Software Engineering, Data Exploration, and Computational Intelligence. Supervises engineering and master theses in applied computer science. Maintains a public wiki with course materials, lab exercises, and consultation schedules for students. Utilizes Git-based repositories to streamline code submission and continuous assessment in laboratory classes. Laboratory & Facilities: He is located in building C-2, room 403, at AGH’s main campus, al. Mickiewicza 30, 30-059 Kraków. The lab supports courses that emphasize practical software development, version control, and collaborative project work.

• Electrical Machines and Energy Conversion
• Fuzzy Logic
• Artificial Intelligence
• +1 more

Lecturer Gökhan BAHADIR is affiliated with Kastamonu University , where he works at the Tosya Vocational School in the Department of Electricity and Energy . He has held administrative roles including Deputy Director of Tosya Vocational School (2017–2019, 2025–) and Department Head (2023–). Education : PhD in Electrical and Electronics Engineering (2022, Düzce University), MSc in Energy Systems Engineering (2014, Karabük University), MSc in Occupational Health and Safety (2018, Kastamonu University), BSc in Electrical and Electronics Engineering (2009, Karadeniz Technical University; 2013, Cumhuriyet University). Research Interests focus on: Electrical Machines and Energy Conversion – Induction/synchronous motors, transformer systems, power electronics Control Theory and Applications – Direct torque control, field-oriented control, programmable controllers Computational Methods – MATLAB simulations, Runge-Kutta numerical analysis, SCADA systems Industrial Automation – Electromechanical systems, hydraulic-pneumatic controls, industrial drivers His publications analyze motor stability in power grids, control methodologies for electric vehicles, and educational approaches to electrical concepts. Collaborations include work with colleagues at Kastamonu and Karabük Universities.

Michal WeiszerView profile

Lecturer

• Optimisation
• Metaheuristics
• Operational Research
• +5 more

Dr. Michal Weiszer is a Teaching Fellow at the School of Engineering and Materials Science, Queen Mary University of London, specializing in optimization, metaheuristics, and operational research with applications in airport operations. His research has significantly contributed to the development of the Active Routing concept for airport ground movement, integrating aircraft engine performance and airframe dynamics with routing and scheduling to achieve greener operations. Dr. Weiszer's research interests focus on routing and scheduling, multi-objective optimisation, simulation, and intelligent transportation systems . His work bridges theoretical optimization techniques with practical aviation applications, particularly in airport ground movement. He has pioneered approaches that integrate aircraft performance models with routing algorithms to reduce emissions and improve efficiency in airport operations. His research spans both algorithmic development and practical implementation, with strong industry connections. Analysis of Dr. Weiszer's publication record shows a consistent focus on airport ground movement optimization using evolutionary algorithms and multi-objective approaches. His research has evolved from basic routing algorithms to more sophisticated integrated systems incorporating aircraft dynamics, emissions modeling, and uncertainty handling. Recent work has expanded into multimodal transportation and open-source simulation platforms, demonstrating the broadening impact of his research beyond traditional airport operations. Dr. Weiszer has received several notable awards and funding: QMUL Flexible Innovation Fund as Principal Investigator EPSRC sponsored TRANSIT project (EP/N029496/1) as Research Co-Investigator Impact Acceleration projects with Avisu as Co-Investigator Impact Acceleration projects with NATS (UK air traffic control) as Co-Investigator His grant portfolio demonstrates strong industry-academia collaboration, with projects translating theoretical research into practical applications. The funding from Royal Society, EPSRC, and industry partners highlights the recognition of his work's potential impact on sustainable aviation. Dr. Weiszer has successfully secured both early-stage innovation funding and larger collaborative projects, showing progression in his research career.

Waqas AhmedView profile

Associate Professor

• Entrepreneurship
• Environmental Sustainability
• Urban Sustainability
• +5 more

Waqas Ahmed is an Associate Professor in the Department of Venture Management at HSE University's Faculty of Management, where he has been employed since 2023. His academic qualifications include a PhD (2021), Master's in Business Administration (2010), and an MA in Management with Merit from London Metropolitan University (2012). He has completed significant professional development including certificates in AI Applications in Education (HSE University, 2024), Doctoral Supervision (University of Westminster, 2023), and Teaching & Learning (University of Westminster, 2022). His research spans entrepreneurial ecosystems, environmental sustainability, and urban development, with methodological expertise in multi-criteria decision analysis applied to complex socioeconomic challenges. Recent publications demonstrate strong foci on: Sustainable transportation and infrastructure development Public health policy analysis during crises Environmental policy implementation in emerging economies Tourism innovation and destination branding Notable recognitions include: 3rd prize in London's Business Plan Competition (2012) for startup mentorship UOW-WIUT Sustainability Challenge commendation Best Researcher award at Westminster International University (2021) Volunteer service recognition from London Metropolitan University He currently supervises doctoral research on energy innovation dynamics and secured a £10,000 grant for textile supply chain sustainability research. As Guest Editor for the Journal of Environmental Management (2024-2025), he contributes to scholarly discourse in sustainability science.

• Theoretical Computer Science
• Logic in Computer Science
• Artificial Intelligence
• +3 more

Sabine FRITTELLA is a Lecturer with HDR at INSA Centre Val de Loire, affiliated with the Laboratoire d'Informatique Fondamentale (LIFO). She is an active researcher in theoretical computer science, focusing on logic and privacy-preserving technologies. Her work bridges formal methods and practical applications in data privacy and reasoning under uncertainty. Her primary research areas include paraconsistent logic, Dempster-Shafer theory, and privacy-enhancing technologies. She investigates how to model contradiction and uncertainty in evidence, and develops formal frameworks for data minimization and informed consent in administrative forms. Her work often combines logical foundations with real-world privacy challenges. Analysis of her recent publications (2020-2025) reveals a strong focus on privacy-preserving technologies, particularly data minimization and consent management, alongside theoretical work in non-classical logics. She has made significant contributions to paraconsistent reasoning and the application of belief function theory to concept representation. Her research consistently addresses the challenge of handling inconsistent information in practical settings. She is a member of the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans), a joint research unit of the University of Orléans and INSA Centre Val de Loire, where she collaborates on projects in theoretical computer science and its applications.

• Fuzzy Logic
• Economics
• Business Management
• +4 more

Joan Carles Ferrer-Comalat is a Professor at the Department of Business, University of Girona. He holds a Doctorate in Economic and Business Sciences (1997) and specializes in the application of fuzzy logic to economics and business management. His research addresses classification techniques under uncertainty, expert systems, and artificial neural networks for economic forecasting. Graduate in Mathematical Sciences (Mathematical Statistics), University of Barcelona (1982) Diploma in Scientific Applications Programmer, University of Barcelona (1982) Master’s in Teaching Mathematics and Experimental Sciences, Autonomous University of Barcelona/University of Girona (1995) His research spans fuzzy logic in economic modeling, financial clustering, and neuromarketing. Recent publications focus on STEAM education, non-binary contexts, and compositional data analysis. He co-leads the Research Group in Accounting and Mathematics for Economics and Business. Scientific awards include the Premi Jaume Vicens Vives (2018). He developed software like PEV for fuzzy sales forecasting and contributed to museum exhibitions on fuzzy logic.

AYŞE YETER GÜNALView profile

Assistant Professor

• Civil Engineering
• Hydrology
• Fluid Mechanics
• +5 more

Ayşe Yeter GÜNAL serves as an Assistant Professor in the Department of Civil Engineering at Gaziantep University's Faculty of Engineering, Turkey. With over 30 years of academic experience since 1992, she has held positions including Lecturer in Geotechnical Engineering and Doctor Lecturer in Hydraulics, demonstrating sustained commitment to civil engineering education and research. Her academic foundation includes: Doctorate in Civil Engineering (2008-2015), Gaziantep University Master's in Civil Engineering (1997-2001), Kahramanmaraş Sütçü İmam University Bachelor's in Civil Engineering (1986-1991), Middle East Technical University, Gaziantep Dr. GÜNAL's research centers on hydrological systems and sustainable infrastructure , with pioneering work applying fuzzy logic (SMRGT) and artificial intelligence to water resource challenges. Her expertise spans climate change impact analysis, flow coefficient modeling, and wastewater management in humanitarian contexts, while her secondary focus explores sustainable construction through recycled materials and carbon footprint reduction. Recent publications (2023-2024) reveal a strategic shift toward AI-driven hydrological solutions and humanitarian engineering applications , with 60% of her work addressing climate change impacts on water systems and 30% focused on sustainable construction in conflict zones like Syria and Gaza. This interdisciplinary approach bridges computational hydrology, environmental engineering, and structural analysis. As a dedicated educator, she has supervised 15 graduate students across diverse water resource and structural engineering topics. Notable mentorship includes: PhD research on fuzzy SMRGT for flow coefficient modeling (2023) Master's theses on Murat River Basin drought analysis (2025) and Gaza Strip groundwater quality (2022) Multiple studies on water distribution systems for IDP camps in Northwest Syria

• fuzzy clustering
• time series clustering
• clustering methods for complex data structures
• +6 more

Pierpaolo D'Urso is a Full Professor of Statistics at Sapienza University of Rome, where he serves as Dean of the Faculty of Political Science, Sociology, Communication. He is Director of the Master's Program in Data Science for Public Administration and holds the Department of Social and Economic Sciences. His academic career includes serving as Director of the Department of Social and Economic Sciences, Vice Rector for Staff Training, and Member of the Academic Senate at Sapienza University. His primary research interests focus on advanced statistical methodologies, particularly fuzzy clustering , time series clustering , and clustering methods for complex data structures . He has extended these methods to diverse applications including statistics and democracy, electoral systems analysis, sports analytics, and public sector management. His work bridges theoretical statistical development with practical applications in social sciences and policy-making. His recent publications demonstrate a strong focus on spatial regularization techniques, entropy-based approaches, and novel applications of fuzzy clustering to interval-valued data and network structures. The research spans multiple disciplines including statistics, data science, economics, and political science, reflecting his interdisciplinary approach to statistical methodology. D'Urso has been recognized with inclusion in Stanford University's World's Top 2% Scientists ranking. He serves as Associate Editor and reviewer for numerous international journals in Statistics and Data Science, contributing significantly to the scholarly community through peer review and editorial work. At the institutional level, he has held significant administrative roles including Director of the Master in Data Science for Public Administration, demonstrating his commitment to developing statistical education for public sector applications. His work with government entities, particularly as a member of expert committees at the Presidency of the Council of Ministers, highlights the practical impact of his research on policy and governance.

Amr AbdelbariView profile

Assistant Professor

• Wireless Communication Systems
• Signal Processing
• 5G Networks
• +4 more

Assistant Professor Amr Abdelbari is affiliated with the Data Analytics Engineering Department at Near East University. His research focuses on wireless communication systems, signal processing, and machine learning applications in telecommunications. Academic Rank: Assistant Professor University: Near East University Department: Data Analytics Engineering Research interests include: Massive MIMO and 5G network optimization Direction-of-Arrival (DOA) estimation techniques Machine learning for wireless communication IoT applications in civil engineering Probabilistic and fuzzy logic-based signal processing Recent publications (2020-2025) demonstrate expertise in wideband signal processing, NOMA systems, and error probability modeling. Key trends in his work include integrating fuzzy logic for network optimization, improving DOA estimation for 5G systems, and applying neural networks to renewable energy modeling. Contact: amr.abdelbari@neu.edu.tr

• Statistical estimation and maximum likelihood methods
• Reliability engineering and life-cycle cost analysis
• Pavement management and transportation infrastructure
• +4 more

James V Carnahan is an Adjunct Professor (10 % appointment) in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, a role he has held since December 2004. Previously he served as Lecturer and Adjunct Professor (50 %), Coordinator of Project Design Activity (100 %), Visiting Assistant Dean in the College of Engineering, and Assistant Professor in what was then the Department of General Engineering. Education Ph.D., Engineering Sciences, Purdue University (1973) M.S., Engineering Sciences, Purdue University (1970) B.S., Engineering Sciences, Purdue University (1968) Research Interests Carnahan’s scholarship is notable for its breadth. Core themes include probabilistic modeling and maximum-likelihood estimation—especially for Beta distributions—reliability engineering, and life-cycle cost analysis of infrastructure systems. He has applied these tools to pavement management, underground heat distribution networks, bulk-material conveyor design, and vehicular-accident injury analysis. Additional curiosity-driven projects span human factors in speed/distance perception, pedestrian-flow congestion, optimization of flavor manufacturing, and even statistical analysis of golf-putting performance. Publications & Trends Across more than three decades Carnahan has published extensively in journals such as Management Science , Accident Analysis & Prevention , ASCE Journal of Transportation Engineering , Decision Sciences , and ASME Journal of Mechanical Design . Early work focused on transportation safety and pavement maintenance optimization; mid-career contributions advanced fuzzy multi-attribute decision making and environmentally conscious design; recent studies shift toward medical and sports analytics, illustrating an evolving interdisciplinary trajectory grounded in rigorous statistical methods. Scientific Awards & Recognition Engineering Council (Accenture) Award for Excellence in Advising, UIUC College of Engineering (2001, 2002) Anderson Consulting Award for Excellence in Advising, UIUC College of Engineering (1990–93, 1999–2000) Department of General Engineering Outstanding Professor (1986, 1991, 2003) Finalist, All-Campus Award for Excellence in Undergraduate Teaching (1990) Everitt Award for Undergraduate Engineering Teaching Excellence (1989) Advising, Grants & Senior Design Leadership Carnahan has coordinated large-scale multidisciplinary senior design projects since 1993, guiding student teams working with industry partners such as Harger International, NTN-Bower, Marmon Industries, Magnetrol, and FONA International. These efforts have garnered multiple James F. Lincoln Engineering Awards and continuous corporate sponsorship, providing students with authentic design-build experiences in reliability, process optimization, and product redesign. Laboratory & Consulting Activity While no dedicated lab is explicitly named, Carnahan maintains active consulting practices through Carnahan Engineering and Surveying (principal, 1979-1983) and ongoing advisory roles with The PERTAN Group, FONA International, Ruhl Forensic, Inc., and the U.S. Army Construction Engineering Research Laboratory, focusing on reliability, forensic accident investigation, and energy-system life-cycle analysis.

• Automatic text analysis
• Information search
• IT project management
• +2 more

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

Yoon Jin-heeView profile

Professor

• Mathematical Data Science
• Soft Computing Theory
• Fuzzy Logic
• +3 more

Professor Yoon Jin-hee is affiliated with the Department of Mathematics and Statistics at Sejong University, where she leads the Mathematical Data Science Lab. Her research focuses on mathematical data science and soft computing theory, bridging fuzzy logic, neural networks, and statistical modeling. Her work spans fuzzy logic and its applications in behavioral and conflict modeling neural network design with fuzzy systems regression analysis for complex data chaotic dynamics in socioeconomic models time series forecasting in AI-driven environments Recent publications emphasize hybrid architectures like LSTM and self-organizing fuzzy neural networks, along with advanced moderation/mediation frameworks. The Mathematical Data Science Lab serves as a hub for developing evolutionary algorithms, attention mechanisms, and theoretical advancements in fuzzy inner products and polynomial classifiers.

Jonathan GryakView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Applied Algebra
• +5 more

Jonathan Gryak is an Assistant Professor of Computer Science and Data Science at Queens College and the CUNY Graduate Center, where he also serves as Deputy Executive Officer. He leads the Interdisciplinary Data Science Lab (IDSL), focusing on developing novel AI/ML methods for biomedical applications that leverage underlying problem morphology and operational constraints. Education: B.S. in Mathematics and Computer Science (Dual), Rensselaer Polytechnic Institute M.S. in Mathematics, Fairfield University M.Phil. in Computer Science, CUNY Graduate Center Ph.D. in Computer Science, CUNY Graduate Center (2017) Gryak's research centers on Nonlinear Algebraic Data Analysis, combining tensor analysis, numerical algebraic geometry, deep neural networks, and invariant theory to create more accurate models of complex real-world data. His Sequential Algebrogeometric Analysis (SAGA) approach captures disease progression through heterogeneous, longitudinal medical data. His work bridges the gap between theoretical mathematics and practical clinical applications, particularly in cardiac care, traumatic brain injury, and food allergy diagnostics. His recent publications demonstrate a strong emphasis on interpretable machine learning methods, especially those employing tropical geometry, for critical healthcare applications. These include predicting heart failure therapy needs, detecting acute respiratory distress syndrome, and improving oral food challenge outcome prediction. His research consistently focuses on methods that incorporate domain knowledge, handle asynchronous data, and leverage intrinsic data structures while maintaining clinical interpretability. Scientific Awards: CUNY Faculty Fellowship Publication Program (2024) NSF grant for improving treatments for heart failure patients through machine learning Gryak actively mentors PhD and undergraduate students in the IDSL, guiding research that spans from theoretical mathematical developments to clinical implementations. His lab collaborates with medical professionals to translate AI techniques into commercial applications that impact patient care. Current projects include multimodal data integration for clinical decision support across various conditions including cancer, traumatic brain injury, and inflammatory bowel disease. The Interdisciplinary Data Science Lab under Gryak's leadership develops end-to-end solutions, from novel mathematical techniques to industry and clinical partnerships for commercialization. Recent work has produced automated systems for endoscopic disease assessment, hematoma segmentation, and cardiac event prediction, with multiple patents stemming from this research.