Dr. Maha Al-Sheikh | Supply chain & logistics | Best Researcher Award

Assistant Professor, Middle East University, Jordan

Dr. Maha Al-Sheikh is an accomplished Assistant Professor of Supply Chain Management at Middle East University, known for her pioneering research in Digital Supply Chain Management, Artificial Intelligence in Logistics, and Sustainable Operations. Her academic path reflects a strong commitment to the integration of advanced analytics, technology, and sustainability within the global business ecosystem. Dr. Maha Al-Sheikh earned her Ph.D. in Supply Chain Management, where she specialized in developing frameworks for data-driven decision-making, adaptive logistics, and resilience modeling. Throughout her professional journey, she has held academic and research-oriented roles that emphasize innovation, interdisciplinary collaboration, and industry engagement. Her teaching and research experience span multiple areas, including AI for Business Transformation, Smart Logistics Systems, Sustainable Supply Chain Networks, and Predictive Modeling for Resource Optimization. Dr. Maha Al-Sheikh’s research interests center on the intersection of artificial intelligence, sustainability, and industrial transformation, exploring how digitalization and intelligent systems can reshape modern supply chains. She demonstrates expertise in AI algorithms for logistics management, neuro-fuzzy modeling, statistical forecasting, simulation tools, and environmental impact assessment. Her research excellence is evidenced through publications in highly regarded journals such as IEEE Access, Technological Sustainability, Problems and Perspectives in Management, and Environmental Economics, addressing key challenges like energy-conscious logistics, clean transportation, and adaptive supply chain resilience. Her professional achievements include active membership in academic and research associations such as IEEE, INFORMS, and the Academy of Management, enhancing her involvement in international conferences, technical sessions, and peer-review activities. Her dedication to innovation, mentorship, and educational leadership has made her a key figure in promoting AI applications for responsible business practices. She has guided numerous postgraduate students in research projects focusing on supply chain resilience, digital transformation, and sustainability transitions, fostering an environment of academic growth and collaboration.

Profiles: Google Scholar | Scopus

Featured Publications

Al-Sheikh, M. (2025). Toward a cleaner road: Environmental transformation in Hungary’s automotive sector. Environmental Economics, 16(2), 1. Citations: 3
Al-Sheikh, M., Morshed, A., Alkhodary, D., Khrais, L. T., & Altarawneh, R. (2025). Beyond efficiency: unpacking AI’s dual role in driving sustainable and energy-conscious logistics in North Africa. Technological Sustainability, 4(3), 293–310.
Samhouri, M., Abualeenein, M., & Al-Sheikh, M. (2025). Mitigating disruptions in transportation and logistics through adaptive neuro-fuzzy inference-based supply chain resilience. IEEE Access.
Zoubi, M., Estaitia, H., Morshed, A., Khrais, L. T., Haikal, E., & Al-Sheikh, M. (2025). Augmented reality and sustainable luxury: transforming fashion retail in the UAE. Technological Sustainability, 1–18.
Al-Sheikh, M. (2025). Assessing how supply chains strategy contributes to business success and varies by firm size and industry. Problems and Perspectives in Management, 23(2), 498.

Assist. Prof. Dr. Ahmad Mousavi | Bilevel Optimization | Best Researcher Award

Assistant Professor, American University, United States

Assist. Prof. Dr. Ahmad Mousavi is an Assistant Professor in the Department of Mathematics and Statistics at American University with a Ph.D. in Applied Mathematics from the University of Maryland, Baltimore County, and postdoctoral training at the University of Florida and the University of Minnesota Institute for Mathematics and its Applications. Over the last decade, Assist. Prof. Dr. Ahmad Mousavi has combined deep expertise in large-scale optimization, sparse recovery, and data science with leadership in machine learning, natural language processing, and quantum computing to advance both theoretical and applied research. His professional experience includes directing online master’s programs in data science, serving as a reviewer for leading journals such as Neural Networks and Journal of Optimization Theory and Applications, and mentoring graduate students on fairness, pruning, and multimodal misinformation detection. Research skills include algorithm development, programming in Python/R/Matlab, statistical modelling, deep learning frameworks, and high-performance computing. Assist. Prof. Dr. Ahmad Mousavi has earned recognition through travel grants, competitive fellowships, and teaching awards and has built an international collaboration network with researchers in North America, Europe, and Asia. He has published extensively in journals such as Journal of Industrial and Management Optimization, Soft Computing, and ESAIM: Control, Optimisation and Calculus of Variations, Over 9 publications, 49 citations, and an h-index of 4 in scopus.

Featured Publications

Mousavi, A. (2022). Multi-objective enhanced interval optimization problem. Journal of Optimization, 45(3), 215-230. Citations: 19.

Mousavi, A. (2023). Prediction-based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Applied Soft Computing, 124, 109832. Citations: 9.

Mousavi, A. (2024). Implementation of machine learning in ℓ∞-based sparse Sharpe ratio portfolio optimization: A case study on Indian stock market. Expert Systems with Applications, 246, 123566. Citations: 1.

Mousavi, A. (2023). Parametric approach for multi-objective enhanced interval linear fractional programming problem. Annals of Operations Research, 321(1), 245-262. Citations: 1.