Adeniyi holds a Doctorate Degree in Medical Statistics in addition to Master’s Degree in Medical Statistics, Project Development and Implementation as well as MA in Monitoring and Evaluation and First Class Honors Degree in Statistics. He has keen interest in Application of Statistics and Demography in Human Health. His research activities as a Medical Statistician have been shaped over the years by evolving needs to develop and apply appropriate statistical methodologies, to understand and answer pertinent questions on health outcomes and their determinants as well as chances of such outcomes, its inequality and prognostic factors. Adeniyi’s main research focus is on the development, critiquing and application of survival analysis regression models to censored data with a bias for maternal and child health and sexual and reproductive health. The goal of his research activities is to inform policy framework through the prevention and management of morbidities and mortalities among these vulnerable sub-population-groups aimed at the relevant defunct millennium and the current sustainable development goals in developing countries. Evidence-based information through adequate statistical procedures is very essential in updating knowledge, solving health problems, providing facts to policymakers for decision making and interventions that can impact individual-level and population-level outcomes. This has motivated his research work within the medical circle to understand, provide and use appropriate statistical procedures in planning, implementation, analysis and dissemination of both clinical and public health studies. He have reviewed, developed and applied statistical methods to real-life and simulated data. My research activities in the development and application of survival analysis techniques started at my Master’s degree level with the development of a model for measuring the association between right-censored bivariate outcomes. The model employed survival analysis concept multivariate normal probability distribution, correlational concepts, transformation techniques, maximum likelihood estimation techniques and was applied to recurrence time of kidney infection among individuals using a catheter. The study found a significant correlation between the censored recurrence time of infection between the two kidneys. His long term research goal is to be an impacting Medical Statistician and Health Data Scientist within formidable research teams with plans to to further strengthen his research focal areas, seek opportunities for collaborative, multidisciplinary research activities at both local and international levels.