Alvanos Paraskevas is a Researcher at the Department of Mathematics, Aristotle University of Thessaloniki. His research interests span Number Theory, Algebraic Geometry, and Adult Education, focusing on Diophantine equations, computational methods in algebraic geometry, and educational technology for teachers. He has contributed to topics like Pell equations over number fields, Riemann-Roch spaces, and integral solutions of polynomial equations. His work bridges theoretical mathematics with practical applications in education and computation. Publications highlight advanced studies in number theory, including bounds for integral solutions and algorithmic approaches to algebraic curves. His work on adult educators' digital readiness reflects interdisciplinary engagement with educational technology. Though no formal awards or grants are listed, his research demonstrates sustained contributions to mathematical theory and pedagogy.
Dr. Hubert Zarzycki is a researcher at the Department of Computer Science and Systems Engineering within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on computational intelligence, fuzzy systems, and algorithm design for optimization problems. Research Interests: Dr. Zarzycki specializes in Ordered Fuzzy Numbers and their arithmetic operations Swarm Intelligence algorithms (e.g., bacterial foraging, cuckoo search, firefly) Applications in financial modeling, sensor placement, and routing optimization Integration of blockchain technology for supply chain transparency Publication Trends: His research spans multiple domains including fuzzy logic, computational finance, and industrial IoT. Recent work emphasizes directional representation in fuzzy systems, robust risk management frameworks, and modular control systems for time-sensitive applications. Contact: Available at hubert.zarzycki@pwr.edu.pl
Prof Dirk Pattinson is a Professor in the School of Computing at Australian National University (ANU). His research focuses on modal logic, coalgebraic systems, automated reasoning, and formal methods. He holds a PhD in Computer Science and has supervised numerous research students. Research interests include coalgebraic logic, non-classical modal logics, automated theorem proving, and applications in computational social choice. His work bridges theoretical foundations with practical tools like the COOL reasoner for modal fixpoint logics. Notable contributions span over 70 peer-reviewed publications since 2008, with recent work on non-iterative modal resolution calculi (2024), Hennessy-Milner properties via topological methods (2022), and formal verification of voting systems (2021). His research often integrates algebraic, categorical, and coalgebraic perspectives.
Laurent Donzé is a Professor of Applied Statistics and Modelling at the Department of Informatics, Faculty of Economics and Social Sciences, University of Fribourg. He is also a Research Professor at KOF ETH Zurich and a Professor of Econometrics at the University of Neuchâtel. He leads the ASAM research group and has extensive experience in teaching mathematics, econometrics, and statistics. His educational background includes a Ph.D. in Econometrics from the University of Fribourg, followed by research roles at IRE and KOF ETH Zurich. His academic journey reflects deep engagement with statistical methodology and applied economic research. Donzé's primary research interests lie in applied statistics, particularly survey methodology, fuzzy statistics, imputation, causal inference, matching techniques, and wage discrimination analysis . He has made significant contributions to the development and application of fuzzy statistical tools, especially in defuzzification and fuzzy regression. His recent publications (2019–2025) demonstrate a consistent focus on fuzzy confidence intervals, fuzzy p-values, fuzzy ANOVA, and fuzzy regression models , often applied to real-world datasets like SHARE and Swiss SILC. These works emphasize robust statistical inference under uncertainty and contribute to both theoretical and applied advancements in fuzzy statistics. Among his scientific recognitions is the Best Student Paper Award at IJJCI 2020 . He has also edited special issues and contributed to leading journals and conferences in computational intelligence and fuzzy systems. Donzé has been involved in numerous research and teaching initiatives, including grants from the Swiss National Science Foundation, mentoring at the Swiss Study Foundation, and leadership in statistical societies. He has supervised research projects and collaborated with institutions such as Nestlé, the Swiss Federal Statistical Office, and pharmaSuisse. He is actively involved in academic and professional communities, serving as president of the Education and Research section of the Swiss Statistical Society and contributing to the development of statistical infrastructure in social sciences.
Gözde Yazgı Tütüncü is a Professor in the Department of Mathematics at İzmir University of Economics, where she has served as faculty since 2010. She earned her PhD in Operations Research and Statistics from Coventry University (UK) in 2006, following MSc degrees in Statistics (Ankara University, 2001) and Industrial Engineering (Başkent University, 2003). She previously held Assistant Professor positions at İzmir University of Economics (2006-2008) and IESEG School of Management, Lille Catholic University (France, 2008-2010). PhD: Operations Research & Statistics, Coventry University (2006) MSc: Statistics, Ankara University (2001) MSc: Industrial Engineering, Başkent University (2003) BSc: Statistics, Ankara University (1999) Her research spans Operations Research, Applied Probability, and System Optimization with emphasis on Reliability Engineering, Fuzzy Logic applications, Healthcare System Optimization, and Heuristic Algorithms for decision-making. She has developed innovative approaches in inventory control under fuzzy costs, vehicle routing optimization, and reliability analysis of complex systems. Her work bridges theoretical mathematics with practical applications in logistics, healthcare, and bioinformatics. Recent publications demonstrate evolving expertise from classical Operations Research (2008-2012) toward interdisciplinary applications in bioinformatics (RNA-Seq analysis since 2019) and advanced fuzzy mathematics (2024). Key publication venues include European Journal of Operational Research, OMEGA, and International Journal of Production Economics, with growing emphasis on computational methods for high-dimensional data. Her scholarly contributions appear in leading journals including: European Journal of Operational Research International Journal of Production Economics OMEGA: International Journal of Management Science Bioinformatics journals for genomic applications Professor Tütüncü has established collaborative research networks across Turkey, France, and international institutions, with significant contributions to reliability theory, fuzzy optimization, and vehicle routing algorithms. Her current work focuses on integrating machine learning with traditional Operations Research methods for complex system optimization.
Assoc. Prof. Dr. Ali GÜLBAĞ is an academic at the Faculty of Computer and Information Sciences , Sakarya University , specializing in Computer Engineering . His career spans over two decades, focusing on FPGA-based hardware design, machine learning applications, and educational methodologies in computer architecture. Doctorate (2003-2006): Quantitative determination of volatile organic compounds using artificial neural network and fuzzy logic-based algorithms MSc (1998-2000): Building automation using telephone lines BSc (1994-1998): Electrical-Electronics Engineering His research interests include Artificial Neural Networks , FPGA Design , and Water Resource Management , with applications in seismic event differentiation, environmental modeling, and educational technologies. Recent work emphasizes water consumption prediction using machine learning. Key projects: BZK.SAU.FPGA microcomputer architecture , Remote FPGA laboratories Publications demonstrate expertise in combining machine learning techniques (ANNs, gradient boosting, random forests) with hardware implementations for real-world problem-solving.
Prof. Dr.-Ing. habil. Kai Willner is a distinguished Professor at the Chair of Engineering Mechanics within the Department of Mechanical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His research spans multiple domains of computational mechanics with significant contributions to structural dynamics, uncertainty quantification, and biomechanics. His current work focuses on innovative applications of fuzzy arithmetic in engineering systems and the mechanics of brain tissue. Principal Investigator for SFB 1540 EBM (Erforschung der Mechanik des Gehirns) Lead researcher on multiple DFG-funded projects including polymorphic uncertainty modeling Active participant in international collaborations on structural dynamics Member of the research group FOR 2271 on process-oriented tolerance management Willner's research interests center on computational mechanics with emphasis on uncertainty quantification , fuzzy-stochastic finite element methods , contact mechanics , and brain biomechanics . His work addresses fundamental challenges in modeling systems with uncertain parameters, particularly in heterogeneous materials and biological systems. His research group develops advanced computational frameworks that integrate fuzzy arithmetic with traditional finite element methods to handle epistemic and aleatoric uncertainties simultaneously, with applications ranging from microstructural analysis to brain mechanics. Analysis of his recent publications reveals a strong trend toward interdisciplinary research, particularly at the intersection of computational mechanics and neuroscience. His work on brain mechanics within the SFB 1540 EBM project represents a significant shift toward biomedical applications of traditional mechanical engineering methods. His publications consistently demonstrate expertise in vibration analysis, structural dynamics, and uncertainty quantification, with increasing focus on applying these methods to biological systems and complex material behaviors. Prof. Willner has secured substantial third-party funding from the German Research Foundation (DFG), including multiple collaborative research center (SFB/TRR) projects, research units (FOR), and individual grants. His current major projects include the SFB 1540 EBM (2023-2026) investigating brain mechanics, and continuing work on polymorphic uncertainty modeling in heterogeneous materials. Within the SFB 1540 EBM consortium, Willner leads research on model-based matching of ex vivo and in vivo test data (project X01), focusing on resolving contradictions in mechanical properties of ultraweak brain tissue materials across different testing modalities. His team develops continuum-based simulation models to unify various experimental observations into a coherent mechanical framework for brain tissue.
Ana Belén Ramos Guajardo is a Professor in the Department of Statistics and Operations Research and Mathematics Education at the University of Oviedo. She is affiliated with the GRINAT Research Group (Grupo de Investigación en Riesgos Naturales) and holds a doctorate from the University of Oviedo with her thesis titled "Contrastes de hipótesis tratamiento de la variabilidad y la imprecisión" (2011), supervised by Dr. Gil González Rodríguez, Dr. María Angeles Gil Alvarez, and Dr. Ana María Colubi Cervero. Her research focuses on statistical methodologies for imprecise data, particularly fuzzy data analysis, random sets, interval data analysis, and hypothesis testing. She has developed innovative approaches for distance-based statistical analysis, fuzzy clustering, and regression models for random intervals. Her work bridges theoretical statistics with practical applications in environmental sciences and other domains requiring analysis of imprecise measurements. Her publications reveal a strong focus on developing statistical methods for non-standard data types, with particular emphasis on random fuzzy sets and interval-valued data. She has contributed significantly to hypothesis testing frameworks for fuzzy data, distance metrics for imprecise data analysis, and computational methods for implementing these techniques. Her most recent work continues to advance the theoretical foundations while exploring practical applications. Ramos Guajardo has collaborated extensively with researchers in the fuzzy data analysis community, particularly with Ana Colubi, Gil González-Rodríguez, and María Asunción Lubiano. Her work has been published in top statistical journals including International Journal of Approximate Reasoning, Fuzzy Sets and Systems, and Information Sciences. She has advised or collaborated with numerous researchers in the field, contributing to the development of statistical fuzzy data analysis as a specialized subfield. Her methodological contributions have applications in environmental risk assessment, particularly through her affiliation with the Natural Risks Research Group at the University of Oviedo.
