Assoc. Prof. Dr. Saleem Ramadan | Data Science | Best Researcher Award

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.

Profiles: Scopus | Google Scholar | LinkedIn

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.

Dr. Muhammad Arshad | AI Advancements | Best Researcher Award

Dr. Muhammad Arshad | Yeez Consultants, Pakistan

Dr. Muhammad Zeshan Arshad is a distinguished data scientist and academic with a Ph.D. in Statistics from the University of Agriculture, Faisalabad, specializing in mathematical statistics and advanced probability distributions. His expertise lies in machine learning, time complexity analysis, and predictive modeling, with applications spanning public health, engineering, and environmental sciences. With peer-reviewed publications, numerous ongoing collaborative projects, and experience in both academic and applied research settings, Dr. Arshad contributes significantly to interdisciplinary data-driven solutions. He is currently serving as a Data Scientist at Yeez Consultants and has held teaching positions at several renowned institutions in Pakistan.

Academic Profile

ORCID

Education

Dr. Muhammad Zeshan Arshad holds a Ph.D. in Statistics from the University of Agriculture, Faisalabad, where he graduated. His doctoral research focused on “A New Transformed G Class of Distributions with Theory and Applications,” which made substantial theoretical and applied contributions to statistical modeling and distribution theory. He also holds an M.Phil. in Statistics from the National College of Business Administration and Economics (NCBA&E), Lahore, where he worked under the mentorship of the late Prof. Dr. Munir Ahmad. His M.Phil. thesis dealt with a transmuted exponentiated moment Pareto distribution, contributing to probability theory. These qualifications are further strengthened by his participation in advanced workshops and short courses on computational modeling, data analysis using statistical software, and instructional methods. This educational journey reflects his strong grounding in both theory and application of statistics across multiple domains.

Experience

Dr. Arshad brings over 15 years of academic and professional experience in teaching, research, and data science. He is currently employed as a Data Scientist at Yeez Consultants in Faisalabad, where he has been leading machine learning and predictive modeling projects since 2020. In addition to his industry role, he has served as a visiting lecturer and tutor at several academic institutions including the University of Agriculture Faisalabad, Government College University Faisalabad, NCBA&E Lahore, and Allama Iqbal Open University. He has taught a wide range of subjects including probability and statistics, business mathematics, regression analysis, and statistical inference to students of economics, engineering, agricultural sciences, and business. His teaching responsibilities also included thesis supervision at M.Phil. and Ph.D. levels. His academic service has been complemented by administrative duties such as examination control and curriculum development, making him a versatile academic professional with a deep understanding of higher education systems.

Research Interests

Dr. Arshad’s research interests lie at the intersection of theoretical statistics and applied data science. He has a strong focus on the development of new probability distributions, entropy modeling, and statistical inference techniques. He is also deeply engaged in machine learning applications, particularly in time complexity analysis, noise-resilient predictive modeling, and shrinkage regression. His recent work includes forecasting COVID-19 cases, analyzing heart disease patterns, environmental modeling, and predictive analytics in public health and agriculture. With published journal articles and several under review in reputed international journals such as PLOS ONE, Heliyon, and AIMS Mathematics, his research has practical significance in health sciences, engineering, climate studies, and financial analytics. He actively collaborates with researchers across Saudi Arabia, Egypt, Canada, and Finland, and his publications reflect a blend of statistical innovation and real-world application.

Awards

Dr. Arshad has received several acknowledgments and awards for his academic excellence and research output. Notably, he was awarded an Appreciation Letter by the Higher Education Commission (HEC) of Pakistan for his successful completion of Ph.D. with distinction and impactful research. His scholarly contributions have earned him reviewer roles in internationally recognized journals including PLOS ONE, the Pakistan Journal of Statistics, and the International Journal of Data Science and Analysis. He has presented his work at national conferences such as the 21st International Conference on Statistical Sciences and previous events held in Faisalabad and Lahore. In addition to these academic recognitions, he has also received positive feedback and citations for his applied research in predictive modeling and statistical computing. These honors underscore his growing influence in the field of data science and statistics, both nationally and internationally.

Publications

Exploring Time Complexity and Machine Learning Scalability for COVID-19 Predictions: A Case Study from Saudi Arabia (2025) – Computational and Applied Mathematics

A NEW TRANSFORMED G CLASS OF DISTRIBUTIONS WITH THEORY AND APPLICATIONS (2023) – Statistics

Modeling potato data with the DUS modified Lehmann-type II power function distribution: Inference and applications (2023) – AIP Advances

An Alternative Statistical Model to Analysis Pearl Millet (Bajra) Yield in Province Punjab and Pakistan (2023) – Complexity

EFFECTS OF SOWING TIMES AND RICE VARIETAL RESISTANCE ON THE SEVERITY OF NARROW BROWN LEAF SPOT IN RELATION TO ENVIRONMENTAL CONDITIONS (2022) – Phytopathology

On Some Properties of a New Truncated Model With Applications to Lifetime Data (2022) – International Journal of Analysis and Applications

A new extended model with bathtub-shaped failure rate: Properties, inference, simulation and applications (2021) – Mathematics

A new generalization of Lehmann type-II distribution: Theory, simulation, and applications to survival and failure rate data (2021) – Scientific African

Conclusion

Dr. Muhammad Zeshan Arshad stands out as a dynamic and impactful researcher whose contributions span both theoretical innovation and practical application in statistics and data science. With a robust academic background, over a decade of interdisciplinary teaching and research experience, and a growing portfolio of international collaborations and high-quality publications, he exemplifies academic excellence and scientific integrity. His work continues to influence fields such as healthcare analytics, environmental modeling, and machine learning, reflecting a deep commitment to solving real-world problems through advanced statistical methods. These accomplishments make him a highly deserving candidate for recognition in any leading research or academic award platform.

Saleem Ramadan | Data Science | Best Researcher Award