Dr. Farouq Mohammad A. Alam | Computational Statistics | Best Researcher Award
Associate Professor at King Abdulaziz University, Saudi Arabia
A dynamic academic in the realm of Computational Statistics, this candidate has steadily progressed through a rigorous journey of research and educational leadership. With a Ph.D. earned under the mentorship of a world-renowned statistician, the nominee has cultivated expertise spanning distribution theory, statistical optimization, and computational modeling. Their professional path reflects an unwavering commitment to knowledge advancement, institutional development, and scientific integrity.
Profile
Education
The academic foundation of the nominee is rooted in a progressive trajectory from a B.Sc. in Statistics & Computer Sciences and an M.Sc. in Statistics—both attained at King Abdulaziz University—to a distinguished Ph.D. in Computational Sciences and Engineering from McMaster University, Canada. The culmination of this academic ascent came in 2017, where doctoral research under Professor N. Balakrishnan focused on advanced statistical inference and lifetime distributions. These experiences equipped the candidate with a rare blend of theoretical depth and practical computational expertise.
Experience
Professionally, the nominee has demonstrated breadth and depth in academia and consultancy. Starting as a Teacher Assistant in 2006, they advanced to Lecturer, then Assistant Professor, and currently serve as Associate Professor in the Department of Statistics at King Abdulaziz University. In addition to teaching, they have taken on multiple administrative and leadership roles, such as Head of the Department of Statistics and Head of the Accreditation Division. Notably, their consultancy with Wadi Jeddah Company and strategic contributions to the Decision Support Center showcase their applicability of statistical knowledge in real-world decision-making contexts.
Research Interest
The research pursuits of the nominee are grounded in computational statistics, particularly the development and application of statistical distributions for real-world data modeling. From reliability theory to biomedical data, their work addresses both theoretical properties and practical estimation techniques. Current interests include heavy-tailed distributions, entropy estimation, and comparative studies of stress-strength models. Their multidisciplinary approach allows for contributions to fields as varied as environmental science, engineering, and public health.
Awards and Training
While specific award titles are not listed, the nominee has undergone extensive professional development through diplomas and certifications, including an Academic Leadership Diploma tailored for department heads and a series of specialized workshops on effective teaching, ethics, and data science. These recognitions of growth reflect a sustained commitment to pedagogical excellence and academic leadership.
Publications
The nominee has authored numerous impactful peer-reviewed articles. Notable among them:
- On modeling x-ray diffraction intensity using heavy-tailed probability distributions: A comparative study (2025, Crystals).
- A new competing risks model with applications to blood cancer data (2024, Journal of Radiation Research and Applied Sciences).
- On entropy estimation of inverse Weibull distribution under improved adaptive progressively type-II censoring with applications (2023, Axioms).
- On modeling cancer and tuberculosis data using the Birnbaum-Saunders lifetime model (2022, Applied Sciences).
- The hazard rate function of the logistic Birnbaum-Saunders distribution (2021, Journal of King Saud University – Science).
- Maximum likelihood estimation of the parameters of a multiple step-stress model from the Birnbaum-Saunders distribution (2017, Communications in Statistics – Simulation and Computation). Each of these has contributed to the broader statistical discourse and has been cited by a range of scholars in applied sciences and statistical theory.
Conclusion
This candidate exemplifies the union of academic rigor and institutional service, making them a compelling nominee for recognition. Their portfolio demonstrates a rare blend of research innovation, administrative leadership, and educational development. Through collaborations, workshops, and publications, they have enriched the statistical community while driving academic quality at their institution. Their candidacy underscores an unwavering devotion to excellence in computational statistics and applied analytics, warranting distinguished acknowledgment in the academic sphere.