Journal of AI, Data Science and Cyber Systems

Anton Iliev is a Bulgarian mathematician and computer scientist born on 3 December 1973 in Plovdiv, Bulgaria, currently serving as Professor and Chair of the Computer Technology Department at the University of Plovdiv, Faculty of Mathematics and Informatics. He graduated in 1996 with a degree in Mathematics (specialization in Informatics) from the same university and obtained his PhD there in 2001. Over his academic career, he has combined research, teaching, and leadership, including serving as Vice-Rector of the University of Plovdiv from 2019 to 2023. His scientific interests encompass informatics, mathematics, modelling, and numerical methods, with significant contributions to the development and refinement of computational and number-theoretic algorithms, extended Euclidean-type methods, hybrid algorithms, growth models, differential equations, oscillator systems, and applications of orthogonal polynomials in dynamical systems. He is a member of the Union of Bulgarian Mathematicians and the Association for Development of the Information Society and received a Diploma of Excellence for Young Scientists in Natural Sciences and Mathematics in 2006 from the University of Plovdiv’s Scientific Research Fund. Professor Iliev has authored and co-authored numerous publications in international peer-reviewed journals and conference proceedings, including Scopus- and Web of Science-indexed journals, as well as several academic books and monographs, contributing significantly to mathematical informatics and computational modelling in Bulgaria and beyond.

Dr. Sang Gyu Kwak is a Professor of Medical Statistics at the School of Medicine, Daegu Catholic University, Republic of Korea. His research focuses on clinical data science, biostatistics, and applied statistical methodology in medical research. He has extensive experience in study design, statistical modeling, causal inference, and machine learning applications in healthcare data.

Dr. Kwak has collaborated on numerous interdisciplinary clinical studies and has published widely in peer-reviewed international journals. His work emphasizes robust statistical methods, reproducible research, and the integration of artificial intelligence with clinical decision-making. He actively contributes to methodological research in medical statistics and serves as a reviewer for several international journals.

Davut Pehlivan is an academic and researcher specializing in maritime transportation, port operations, logistics systems, and risk and resilience analysis in transportation networks. He holds extensive experience in quantitative modeling approaches, including discrete-event simulation, multi-criteria decision-making methods (AHP/FAHP, Fuzzy Axiomatic Design), and data-driven performance analysis applied to maritime and logistics systems.

His research interests focus on port efficiency, intermodal logistics, human factors and human reliability in maritime operations, environmental impacts of transportation systems, and the resilience of critical infrastructure under uncertainty. He has actively worked on simulation-based decision support models for port–dry port systems and logistics corridors, integrating operational, environmental, and sustainability dimensions.

Dr. Pehlivan has served as a reviewer for several international peer-reviewed journals and has been involved in editorial and quality assurance processes within higher education institutions. His reviewing experience reflects a balanced approach that combines methodological rigor with constructive, developmental feedback for authors. In addition to his academic research, he has professional experience in logistics consultancy, contributing practical insights to his scholarly work. He is particularly interested in interdisciplinary research that bridges operations research, transportation studies, and sustainability science.

Through his editorial contributions, he aims to support high-quality, ethically sound, and impactful research, while fostering inclusive and forward-looking academic dialogue within the field.

Mehdi Gheisari is a PhD holder at the crossroads of AI, data science, and cyber systems with 15+ years specializing in privacy-preserving technologies for intelligent connected environments. Ranked among the top 2% of global scientists (Stanford University, 2024–2025), he blends cutting-edge AI, data-driven security, and resilient cyber infrastructure. A author of highly cited top-tier publications and tech patents, he leads global collaborations and contributes to international technical communities. As an editor, he fosters cross-disciplinary innovation, uniting global talent to advance trusted solutions in AI, data science, and cyber systems.