Associate Professor at Mathematics Department, Faculty of Science, Sohag University, Sohag, Egypt
Dr. Al-Wageh A. Farghal is an accomplished Associate Professor in the Mathematics Department at Sohag University, Egypt. With expertise in statistical inference, Bayesian methods, and reliability analysis, he has significantly contributed to mathematical statistics. Dr. Farghal earned his Ph.D. in 2016, focusing on statistical inference using progressive censored samples and accelerated life testing. He has numerous publications in high-impact journals, highlighting his innovative approaches to Bayesian and non-Bayesian methods. His dedication to academic excellence is evident in his continuous efforts in teaching, research, and mentorship.
PROFESSIONAL PROFILE
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STRENGTHS FOR THE AWARDS
- Extensive Academic Background: The researcher has completed a Ph.D. in Mathematical Statistics, focusing on Bayesian and non-Bayesian approaches, demonstrating expertise in advanced statistical methods.
- Prolific Research Output: The researcher has numerous publications in high-impact journals like the Alexandria Engineering Journal and Statistical Papers. Their work addresses critical areas such as progressive censoring, accelerated life tests, and Bayesian inference.
- Citations and Recognition: Many of the researcher’s articles have significant citations, highlighting their impact on the academic community.
- Diverse Research Areas: The researcher has contributed to several statistical methods, including reliability analysis, competing risks models, and lifetime performance indices, showcasing versatility.
- Professional Experience: Over a decade of teaching and research experience at Sohag University, progressing from Instructor to Associate Professor. This progression reflects their dedication and expertise.
AREAS FOR IMPROVEMENT
- Collaborations: Expanding collaborations with international researchers or institutions could further enhance the diversity and impact of their research.
- Applied Research: While the theoretical contributions are significant, integrating more applied case studies or interdisciplinary work may broaden the practical relevance of their findings.
- Grant Acquisition: Securing more research grants could further validate their research capabilities and provide resources to pursue innovative studies.
- Global Outreach: Presenting research at international conferences or participating in global projects could enhance visibility and recognition in the global academic community.
EDUCATION
Dr. Al-Wageh A. Farghal earned his Ph.D. in Mathematical Statistics from Sohag University in 2016, specializing in Bayesian and non-Bayesian approaches under progressive censoring. He completed his Master’s degree in 2011, focusing on reliability estimation and prediction using record values from the Rayleigh distribution. Dr. Farghal obtained his Pre-Master Diploma in 2008 and his Bachelor’s Degree in Mathematics in 2006 with honors. His academic journey showcases his deep commitment to advancing mathematical statistics and data analysis.
EXPERIENCE
Dr. Farghal has been an Associate Professor in the Mathematics Department at Sohag University since December 2023. He served as an Assistant Professor from 2016, contributing to research and teaching in advanced statistical methods. Between 2011 and 2016, he worked as an Assistant Lecturer, fostering his academic expertise, and began his career in 2007 as an Instructor. His professional trajectory highlights his continuous growth and dedication to mathematics education and research.
AWARDS AND HONORS
Dr. Al-Wageh A. Farghal has received recognition for his exceptional contributions to mathematical statistics. His innovative research and impactful publications have earned him accolades in academic and professional circles. He has been honored for his expertise in Bayesian and non-Bayesian inference, progressive censoring, and reliability analysis.
RESEARCH FOCUS
Dr. Farghal’s research centers on statistical inference, reliability estimation, Bayesian and non-Bayesian approaches, and progressive censoring schemes. His work explores accelerated life testing, generalized distributions, and competing risks models. Dr. Farghal’s innovative methodologies address real-world challenges in reliability engineering and life data analysis.
PUBLICATION TOP NOTES
- 📘 Partially constant-stress accelerated life tests model for parameters estimation of Kumaraswamy distribution under adaptive Type-II progressive censoring
- 📗 Bayesian estimation from exponentiated Frechet model using MCMC approach based on progressive type-II censoring data
- 📘 Estimation of Generalized Inverted Exponential Distribution based on Adaptive Type-II Progressive Censoring Data
- 📙 Assessing the lifetime performance index using exponentiated Frechet distribution with the progressive first-failure-censoring scheme
- 📘 Bayesian and Non-Bayesian Inference for Weibull Inverted Exponential Model under Progressive First-Failure Censoring Data
- 📗 Statistical Inference under Copula Approach of Accelerated Dependent Generalized Inverted Exponential Failure Time with Progressive Hybrid Censoring Scheme
- 📙 Analysis of generalized inverted exponential competing risks model in presence of partially observed failure modes
- 📘 Inference on Weibull inverted exponential distribution under progressive first-failure censoring with constant-stress partially accelerated life test
- 📙 Estimation of R= P (Y< X) using k-Upper Record Values from Kumaraswamy Distribution
- 📗 Statistical inference for competing risks model with Type-II generalized hybrid censored of inverse Lomax distribution with applications
- 📘 Statistical Inference of a New Pareto-type Model under Generalized Hybrid Type-I Censored Samples
- 📙 Statistical Inferences Based on Progressive First-Failure Censoring Scheme of Kumaraswamy Lifetime Distribution
- 📗 Progressive First-Failure Censored Samples in Estimation and Prediction of NH Distribution
CONCLUSION
The researcher demonstrates a strong academic foundation, impactful research contributions, and consistent professional development, making them a strong candidate for the Best Researcher Award. Strengthening global outreach, collaborations, and applied research aspects could further solidify their case for this prestigious recognition.