Research Background

Biostatistics is my main area of interest. With a background in applied statistics, I have worked full-time as a statistician at the Centre for Injury Prevention and Research, Bangladesh (CIPRB) which is considered the only research organization in the developing world dedicated to exclusive studies on intervention measures to prevent injuries in Bangladesh and in countries with similar health and safety issues. I am experienced in statistical research, data analytics, data mining, and machine learning. Since joining Bond University in 2018, I have been acquiring further research excellence and functional knowledge of statistical methodologies for big data analysis and public health statistics particularly concentrated on the development of a statistical model for the early detection of Alzheimer’s Disease.

I have served in major international collaborative ventures such as the Barisal Drowning Reduction Project. The objective was to test the theory that coordinated and comprehensive national and sub-national level responses can affect a significant reduction in drowning at the national and ultimately global levels. This was a Royal National Lifeboat Institute-funded project with CIPRB as collaborators and The George Institute for Global Health, Australia as academic partners. I have been involved in data management for BHIS 2016, the largest research on injuries and related fatalities in children over the age of one year in Bangladesh. BHIS was a product of collaboration between the Institute of Child and Mother Health (ICMH), the Directorate General of Health Services (DGHS), The Alliance for Safe Children (TASC), and the United Nations Children’s Fund (UNICEF). Currently, I teach and tutor students on quantitative methods at Bond Business School.

Main Areas of Research

Theoretical and applied statistics, biostatistics, statistics in biomedical and clinical studies, statistical inference, multilevel models, experimental design, methodological research, methodology, Bayesian statistics, survey sampling, and small-area estimation with applications to health, economic, statistical learning for big data.