Taban Baghfalaki

Taban Baghfalaki is a Lecturer in Statistics with a focus on advanced statistical methodologies applied to longitudinal data analysis, Bayesian inference, and joint modeling. His work integrates complex statistical techniques to address challenges in health sciences, genetics, and epidemiology. He has contributed to developing Bayesian approaches for handling zero-inflated data, competing risks, and pleiotropy in genomic studies.

Key research areas include dynamic prediction in longitudinal studies, variable selection in high-dimensional datasets, and the application of copula models for multivariate data analysis. He has pioneered tools like the GCPBayes pipeline for cross-phenotype genetic association studies, demonstrating expertise in both theoretical and applied statistics.

His publications span 2011–2025, emphasizing Bayesian methods, survival analysis, and longitudinal modeling. Notable contributions include frameworks for joint modeling of longitudinal and survival outcomes, methods for analyzing skewed mixed responses, and computational tools for genomic data.

Awards and grants are not explicitly mentioned in the provided texts, but his prolific publication record reflects sustained research activity. He collaborates on projects involving healthcare data (e.g., Tehran Lipid and Glucose Study) and genetic epidemiology.