My research centers on statistical modeling and advanced data analysis in health and biomedical sciences, with a strong focus on pediatric epidemiology, nutritional assessment, and anthropometric growth studies. I am particularly interested in applying statistical methodologies—such as logistic regression, Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Quantile Regression (QR), and machine learning techniques—to the analysis of large-scale and complex health datasets. My work investigates intricate relationships among metabolic disorders, malnutrition, and growth patterns, utilizing predictive analytics and health informatics to support personalized and evidence-based health decision-making. Additionally, I integrate spatial analysis and geostatistical approaches to examine health disparities and inform targeted interventions aimed at improving child health outcomes. I routinely employ statistical and computational software, including SPSS, and R (programming language), for data management, modeling, visualization, and reproducible research.