Dr. Nataliya Chukhrova is a Researcher at the Institut für Mathematik und Statistik within the University of Hamburg Business School . Her research focuses on Data Science, Business Analytics, Fuzzy Statistics, and Quantitative Risk Management, with applications in Finance, Health, and Industrial Engineering. She has received notable awards including the Hamburger Lehrpreis 2015 for innovative teaching and the Jahrgangsbestenpreis 2011 for academic excellence. Her teaching portfolio includes courses such as Angewandte Statistik für Fortgeschrittene , Statistik mit SPSS , and Statistische Qualitätskontrolle . Her publications emphasize statistical methods in quality control, fuzzy hypothesis testing, and stochastic modeling, with a focus on practical applications in healthcare and finance. In recent years, her work has explored the intersection of fuzzy logic with traditional statistical techniques, particularly in addressing challenges posed by the COVID-19 pandemic. She collaborates extensively, often with co-author Arne Johannssen, on projects that bridge theoretical statistics and real-world operational problems.
Dr. Filip Strobin is an Associate Professor in the Division of Applications of Contemporary Mathematical Analysis at Lodz University of Technology. His research centers on theoretical aspects of fractal geometry and functional analysis. Key research themes include: Topological and geometric properties of fractal attractors Generalizations of iterated function systems (IFS) Embedding theorems for metric spaces Fixed point theory applications to fractals Lineability and spaceability in function spaces Recent work characterizes conditions for connected attractors in IFS, establishes embedding capabilities of fractals in various spaces, and develops algorithms for fractal visualization. His publications frequently appear in analysis and topology journals.
Mahinda Mailagaha Kumbure is an Assistant Professor (Tenure Track) at LUT Business School, Lappeenranta-Lahti University of Technology (LUT University), Finland, where he is part of the Business Analytics research team. He holds a Doctor of Science in Economics and Business Administration (2022), a Master’s in Computational Engineering (2018), and a Bachelor’s in Mathematics and Statistics (2014) from the University of Ruhuna. He has been employed at LUT University since 2020, progressing from Junior Researcher to Postdoctoral Researcher, and now Assistant Professor. Doctor of Science in Economics and Business Administration, LUT University (2018–2022) M.Sc. in Computational Engineering, LUT University (2016–2018) B.Sc. in Mathematics and Statistics, University of Ruhuna (2009–2014) His research centers on data mining, applied machine learning, fuzzy systems, and fuzzy qualitative comparative analysis (fsQCA) , with applications in business, management, and financial forecasting. He develops and enhances fuzzy classification and regression models, particularly focusing on fuzzy k-nearest neighbor (k-NN) variants using aggregation operators like OWA, Bonferroni mean, and power means. His work also explores causal cognitive maps for modeling managerial cognition and organizational learning, and hybrid feature selection techniques for financial prediction. His recent publications (2019–2025) show a strong focus on improving fuzzy classification algorithms , applying fsQCA in decision support , and using machine learning for stock market forecasting . His work spans journals in artificial intelligence, soft computing, expert systems, and business research, indicating interdisciplinary impact. He frequently employs granular computing, Minkowski distance, and ensemble methods to enhance model robustness and interpretability. He has served as a peer reviewer for top journals including Expert Systems with Applications , Information Sciences , Engineering Applications of AI , and Renewable Energy , demonstrating active engagement in the research community. Mahinda advises no students listed in the provided data and has not been mentioned in relation to specific grants, awards, or leadership of a lab or research team. However, his collaborative work with researchers such as Pasi Luukka, Anssi Tarkiainen, Jan Stoklasa, and Ari Jantunen suggests strong team integration within the Business Analytics group at LUT.
Prof. Dr. Nizami Gasilov is a faculty member at Başkent University's Faculty of Engineering, specializing in computer engineering. His research focuses on fuzzy differential equations, interval analysis, numerical methods, and plasma physics. Teaches courses including Discrete Structures, Numerical Analysis Techniques, and Differential Equations Holds a PhD in Computational Mathematics and Cybernetics from Lomonosov Moscow State University (1989) Recent work analyzes fuzzy and interval differential equations for deterministic uncertainty modeling, with applications in tokamak plasma dynamics and recommender systems. His publications span journals like Kybernetika and Soft Computing. Active in editorial roles for multiple journals in 2024.
Mohd Farhan Md Fudzee is an academic researcher with a focus on interdisciplinary computational research spanning multimedia systems, bioinformatics, and network engineering. His work emphasizes service-oriented architectures, data fusion techniques, and optimization of complex systems. Key contributions include advancements in content adaptation policies for distributed multimedia, machine learning methods for disease gene prediction, and disaster management protocols in mobile ad-hoc networks (MANET). He has collaborated extensively with institutions on projects involving fuzzy logic applications, healthcare informatics, and safety-critical system development. Research interests are driven by practical applications in: Multimedia Adaptation: Developing QoS-aware service selection frameworks and dynamic path determination policies for content delivery networks. Health Informatics: Leveraging machine learning for disease module identification and medical systems reliability assessment. Data Science: Innovating classification methods using fuzzy soft set theory and multi-agent systems for data fusion challenges. Recent work (2022-2024) highlights trends in bioinformatics pathway analysis, social network prediction algorithms, and hybrid routing approaches for disaster response systems. His publications consistently address real-world system optimization across domains like transportation, healthcare, and energy sectors.
Harold W. Lewis is the Bartle Professor at the School of Systems Science and Industrial Engineering, Binghamton University. He holds a BS, MS, and PhD from Binghamton University. His research focuses on intelligent systems, fuzzy control, and soft computing, emphasizing applications in control systems and cognitive modeling. Key research interests include fuzzy clustering algorithms, neuro-fuzzy systems, and adaptive control methodologies. His work bridges theoretical foundations with practical implementations in automation and decision-making under uncertainty. Dr. Lewis received the Chancellor's Award for Excellence in Teaching (2013). His contributions span fuzzy logic, evidence theory, and systems engineering, with notable studies on aircraft landing scheduling and cognitive approaches to soft computing. His advising and grants involve advancing fuzzy set theory applications in interdisciplinary fields. Ongoing work explores hybrid systems and AI-driven control frameworks.
Dr. JingTao Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He joined the University of Regina in January 2002 and has been actively contributing to the academic community since then. His extensive academic career includes previous teaching positions at Massey University (New Zealand), National University of Singapore, The Open University (Singapore), and Xi'an Jiaotong University (China). Dr. Yao's research interests span multiple areas within computer science, with a strong focus on granular computing, rough sets, soft computing, data mining, three-way decisions, neural networks, computational finance, electronic commerce, and web intelligence . His work represents significant contributions to the theoretical foundations and practical applications of these areas. His recent publications demonstrate a consistent research trajectory focusing on three-way decision theory, game-theoretic rough sets, and their applications across diverse domains including text classification, intrusion detection, fraud detection, and biomedical applications. The publications show increasing integration of traditional rough set theory with modern deep learning techniques. Ranked as world top 2% scientist (top 0.92%) by Stanford University Three Highly Cited Papers according to Web of Science One Hot Paper according to Web of Science Dr. Yao coordinates the Rough Set Technology Lab and the Web Intelligence Consortium Canada Research Centre. He has served on numerous administrative committees at the departmental, faculty, and university levels, including as Chair of the Graduate Committee (Data Science) and member of various search and review committees. His professional activities include extensive editorial work as Area Editor of the International Journal of Approximate Reasoning and editorial board membership for several other journals. He has also been actively involved in organizing numerous international conferences in his field, serving as chair, program committee member, and steering committee member for major conferences in rough sets, granular computing, and web intelligence.
Liudmyla Dorokhova is a Visiting Professor (part-time) at the University of Tartu's School of Economics and Business Administration within the Faculty of Social Sciences. She concurrently held an Associate Professor position at the National University of Pharmacy (Kharkiv, Ukraine) from 1997 to August 2024. Her research focuses on consumer behavior, marketing strategies in healthcare, and decision-making models. PhD in Pharmaceutical Market Research (1994) Master's in Pharmacy (1990) Economist training (1998) Her work bridges consumer psychology with practical applications in pharmacy operations, digital marketing, and organizational HR practices. Notable research includes studies on fitness gadget preferences, pharmacy service optimization, and cross-cultural HR strategies. She collaborates internationally, publishing in journals like Studies in Business and Economics and Journal of Risk and Financial Management . Her contributions span consumer modeling techniques, healthcare market analysis, and innovative platform development for charity tourism.
Prof. Fatma İnci Albayrak is a Professor at the Department of Mathematics Engineering within the Faculty of Chemical and Metallurgical Engineering at Yildiz Technical University . She holds a doctorate, postgraduate, and undergraduate degree in Mathematics Engineering from the same institution. Her research spans Mathematics , Optimization , Natural Sciences , and Fuzzy Modeling , with a focus on applying fuzzy logic to complex engineering and operational problems. Education: Doctorate (1993–1997), Yildiz Technical University Postgraduate (1990–1993), Yildiz Technical University Undergraduate (1986–1990), Yildiz Technical University Her publications reflect expertise in fuzzy systems , multi-objective optimization , differential equations , and nanostructured materials . She has contributed to journals like Physica Scripta , Canadian Journal of Chemical Engineering , and Cellulose , often addressing challenges in biomedical applications, transportation networks, and nanotechnology. She has served as a peer reviewer for Soft Computing and Promet journals. She is affiliated with the Multidisciplinary Nanoscience Technology working group at Yildiz Technical University since 2021, integrating nanomaterials with mathematical modeling. Her work bridges theoretical mathematics and practical engineering, demonstrated through collaborations in polymer nanocomposites, heat exchanger optimization, and traffic assignment problems.
Kian Pokorny serves as Professor of Computing at McKendree University, maintaining an office in Clark Hall 200 with contact phone (618) 537-6440. His academic foundation includes a Ph.D. from Louisiana Tech and dual degrees (M.S., B.S.) from Central Missouri State University. Education background: Ph.D., Louisiana Tech M.S., Central Missouri State University B.S., Central Missouri State University His research spans Artificial Intelligence , Soft Computing , Data Science , and Computer Science Education , driven by the philosophy: "My highest priority as an educator is to produce lifelong learners with strong critical thinking skills. This I hope to accomplish by creating a non-threatening, student-centered environment within my classroom that allows students to achieve the highest possible levels of learning." This commitment manifests in curriculum innovations and educational tool development. Analysis of his 15 most recent publications reveals consistent focus on computer science pedagogy evolution. Key trends include: integration of data science into traditional curricula (2022), machine learning applications for institutional analysis (2021), and sustained development of the Frances tool suite for computer architecture education (2009-2012). His work bridges theoretical computer science with practical educational implementation, demonstrating particular strength in constraint satisfaction problems and fuzzy logic applications. Scientific recognition: ACI Grant for Intelligent Tutoring Systems Design: An Empirical Approach (2005-2006) While specific student advisees aren't documented, his extensive publication record in educational methodology suggests significant mentorship impact. The ACI grant specifically supported research into adaptive learning systems, indicating early recognition of his contributions to educational technology innovation. No laboratory facilities or research teams are referenced in available materials.
Logan Mayfield serves as a Professor in the Department of Mathematics, Statistics, and Computer Science at Monmouth College, contributing to both academic instruction and research initiatives within the institution. His research spans quantum computing, fuzzy logic, and quantum information theory with emphasis on parameterized frameworks, multi-dimensional uncertainty modeling, and quantum search abstractions. This interdisciplinary work bridges theoretical computer science with practical applications in quantum information processing systems. Publications from 2004-2012 reveal a cohesive research trajectory advancing quantum computation models—from foundational quantum search generalizations to sophisticated frameworks for multi-dimensional information processing—demonstrating consistent innovation in quantum algorithm design and uncertainty quantification. No scientific awards were documented in the provided sources. Details regarding student advising activities and research grant acquisitions remain unspecified in available materials. Information about dedicated research laboratories or collaborative teams was not included in the source documentation.
