Assoc. Prof. Dr. Saleem Ramadan | Al Hussein Technical University | Jordan
Dr. Saleem Z. Ramadan is an accomplished Data Analyst and Business Analyst with a strong interdisciplinary background in industrial engineering, systems optimization, and data science. With academic and consulting experience across the U.S. and Jordan, he has applied data-driven decision-making, predictive analytics, and optimization modeling to complex problems in healthcare, manufacturing, and finance. Dr. Ramadan holds a Ph.D. in Systems Engineering from Ohio University and has served as Acting Chair and Associate Professor at Al Hussein Technical University, leading research and teaching initiatives integrating machine learning and operations analytics. He has developed impactful analytics solutions—ranging from hydroponic resource optimization and radiology workflow improvement to financial risk dashboards—using tools such as Python, SQL, Power BI, Tableau, and Minitab. His consulting work with Healthcare Operations & Performance Excellence (HOPE) led to measurable improvements in hospital performance through Six Sigma and process control techniques. A Certified Analytics Professional (CAP) and Microsoft Power BI Data Analyst Associate, Dr. Ramadan has authored 20 peer-reviewed publications, accumulating 236 citations from 230 documents with an h-index of 6. His recent works focus on machine learning–driven optimization, surgical scheduling prediction, and additive manufacturing parameter tuning. Dr. Ramadan’s combination of technical proficiency, academic leadership, and applied research impact uniquely positions him at the intersection of analytics innovation and business performance excellence.
Featured Publications
Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2025). A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency. Journal of Industrial Engineering and Management.
Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2024). Optimizing tensile strength and energy consumption for FDM through mixed-integer nonlinear multi-objective optimization and design of experiments. Heliyon.