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