Dr. Samuel Iddi

Dr. Samuel Iddi is an accomplished Research Scientist II/Data Scientist/Bio-statistician at APHRC and boasts over 11 years of expertise in biomedical science, biostatistics, and data science. He leads innovative projects employing machine and deep learning to address non-communicable disease multimorbidity complexities. Proficient in R, SAS IML, MATLAB, and SQL, he also has expertise in data visualization, dynamic report generation, and macros and script development for efficient generation of results. He is a mentor, and a trainer, and contributed to knowledge generation and dissemination. Dr. Iddi's contributions include pioneering statistical methods tackling overdispersion, zero count inflation, and longitudinal outcome modeling, and employing these techniques to analyze biomedical data to inform clinical and medical practice. As an Associate Professor of Biostatistics and a recognized trainer in statistical methods and software, he has mentored researcher officers, data scientists, and statisticians and supervised 7 PhD and several graduate students. He has collaborated not only with statisticians but also with public health experts, epidemiologists, environmental scientists, neuroscientists, and medical practitioners to produce impactful research findings. He has published more than 50 research articles in recognized scientific journals and involved in the generation of several scientific products (reports, protocols, blogs, conference proceedings, etc.). Through the use of advanced statistical analysis to help uncover patterns, trends, and associations in longitudinal and other forms of data, Samuel has contributed to the generation and dissemination of valuable insights that contribute to individualized and policy-informed decision-making. His research work has been presented at several scientific conferences worldwide.

On this project, Samuel, with a multifaceted skill set and extensive experience in research design and methodology, will play a pivotal role in designing the research and sampling strategies, developing data collection and management plan, applying advanced statistical models, and contributing to robust and insightful analyses tailored to the unique characteristics of study design. Samuel will also provide tailored statistical support to researchers, early-career data managers, and statisticians who will work on the project. He will offer guidance on appropriate statistical methods and interpretation of results to these individuals to foster a culture of continuous learning and development within the research team.

Research Associates ›