Mohamed Megheib | Spatial | Assistant Professor at Southampton University/ Cairo University, United Kingdom
Mohamed Megheib, PhD, is a Senior Research Fellow at WorldPop and the School of Geography and Environmental Science (SoGES) at the University of Southampton, UK. His extensive academic background spans multiple disciplines in statistics, including spatial statistics, biostatistics, and nonparametric modeling. Dr. Megheib’s work intersects with a range of global health challenges, particularly in the areas of population estimation, vaccination coverage, and the use of advanced statistical methods to model complex datasets. His research has practical applications in public health, genomics, and drug approval processes, particularly in low- and middle-income countries. He holds a PhD in Statistics from George Washington University, where his dissertation focused on nonparametric modeling for correlated data.
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Education
Dr. Megheib’s academic journey began at Cairo University, where he earned a B.Sc. in Statistics with an Economics Minor in 2009, followed by a Master’s degree in Mathematical Statistics in 2014. His passion for statistical modeling led him to George Washington University, where he completed his M.Sc. in Statistics in 2017, and later his PhD in 2020. His doctoral research, supervised by Professor Sudip Bose, centered on nonparametric regression techniques for analyzing correlated data. This rigorous academic foundation laid the groundwork for his ongoing research in statistical methodologies applied to public health and environmental science.
Experience
Dr. Megheib’s professional experience spans across academic, governmental, and research institutions. He currently serves as a Senior Research Fellow at WorldPop, University of Southampton, focusing on spatial statistical modeling and subnational population data to support UN agencies and address public health crises. His previous role as a Senior Statistical Scientist at the US Food and Drug Administration (FDA) involved providing statistical guidance for clinical trials in oncology. His academic appointments include being an Assistant Professor at Cairo University, where he taught courses in statistics, and as a Postdoctoral Fellow at the Institute for Modeling Collaboration and Innovation (IMCI), contributing to several interdisciplinary projects. His varied roles demonstrate his ability to apply advanced statistical techniques to solve real-world problems in public health and drug development.
Research Interests
Dr. Megheib’s research interests are diverse but focused primarily on statistical modeling for complex data. His work encompasses parametric and nonparametric modeling, spatial statistics, microsimulation, and Bayesian inference. He is particularly interested in developing methodologies to estimate small-area health indicators and map genetic variations across multiple phenotype levels. Recent projects include genomic analysis in collaboration with NIH and large-scale population mapping efforts for health and disaster relief. His work has a direct impact on policy-making, especially in low- and middle-income countries, where data infrastructure is often lacking.
Awards
Dr. Megheib’s dedication to statistical research has earned him several accolades throughout his career. At the FDA, he was recognized with the OTS Super Star Appreciation Card in 2022 and 2023 for his leadership and contributions to oncology drug approvals. His academic excellence was also honored by George Washington University with multiple GW WID Fellowships from 2017 to 2020. Additionally, he was named a Teaching Fellow at Le Havre University, Sciences Po, in 2020. These awards underscore his impact in both research and teaching.
Publications
Ashour, K.S. & Megheib, M. (2014). “Kummer Beta-Weibull Geometric Distribution: A New Generalization of Beta-Weibull Geometric Distribution,” International Journal of Sciences: Basic and Applied Research (IJSBAR), Vol. 16, No 2, pp. 258-273.
Bose, S. & Megheib, M. (2024). “Posterior Consistency in Nonparametric Regression under the Presence of Exponential Correlated Errors,” Statistics & Probability Letters.
Erich, S., Megheib, M., et al. (2022). “Estimating County-Level Health Indicators Using Spatial Microsimulation,” Population, Space and Place.
Kvamme, J., Badsha, M.B., et al. (2022). “Types of Cis- and Trans-Gene Regulation of Expression Quantitative Trait Loci Across Human Tissues,” PLOS Genetics.
Megheib, M. (2021). “Bayesian Approach in Nonparametric Regression with Spatially Correlated Data,” Communications in Statistics – Simulation and Computation.
Megheib, M. & Bose, S. (2020). “Nonparametric Modelling for Correlated Data,” George Washington University.
Conclusion
Dr. Mohamed Megheib’s multifaceted expertise in statistics and its applications to global health and policy makes him an outstanding candidate for recognition. His contributions to statistical research, particularly in nonparametric modeling and spatial statistics, have had significant real-world impacts, especially in public health interventions and genomic research. With a proven track record of excellence in both academia and professional practice, Dr. Megheib is a valuable asset to the statistical community and global health initiatives.