My research focuses on developing Bayesian and computational statistical methods for analyzing complex, high-dimensional, and lifetime data. I am particularly interested in building flexible inferential frameworks that combine statistical theory, computation, and simulation to address challenging problems in survival analysis, biomedical research, and modern data science.
Through this work, I aim to develop robust, interpretable, and computationally efficient statistical methods that advance statistical methodology while contributing to data-intensive scientific discovery.
Development of Bayesian methodology for hierarchical modeling, adaptive prior specification, posterior computation, and uncertainty quantification for complex and high-dimensional data.
Development and evaluation of computational algorithms, adaptive Markov chain Monte Carlo (MCMC) methods, and simulation-based approaches for modern statistical inference.
Bayesian and semi-parametric modeling of time-to-event data, hazard function estimation, frailty models, censoring mechanisms, and lifetime data analysis.
Statistical methodology for analyzing high-dimensional biological and biomedical data, including regularization, missing-data problems, and computational inference.
Development and application of statistical learning methods for complex datasets arising in genomics, proteomics, biomedical research, and other data-intensive scientific applications.
My current research program centers on Bayesian methodology for survival analysis and high-dimensional biomedical data. A major focus of my work is developing adaptive Bayesian methods that improve estimation, prediction, and uncertainty quantification for heterogeneous and censored survival data.
My recent work investigates adaptive prior learning, Bayesian Weibull survival models, Bayesian frailty models, and computational methods for posterior inference. In parallel, I study statistical methodology for missing-data imputation and computational techniques for analyzing high-dimensional biological datasets, particularly in genomics and proteomics.
Looking ahead, I plan to extend this work toward Bayesian machine learning, multi-omics data integration, scalable computational algorithms, and modern statistical methodology for analyzing increasingly complex data.
Adaptive Prior Learning in Bayesian Frailty and Joint Survival Models with Covariates
Development of adaptive prior specification methods for Bayesian frailty and joint survival models, with emphasis on improving estimation accuracy and uncertainty quantification.
Target Journal: Journal of Statistical Planning and Inference.
Hierarchical Bayesian Weibull Models under MNAR Censoring
Development of hierarchical Bayesian Weibull models with adaptive prior learning for reliable survival inference under informative censoring and heterogeneous populations.
Target Journal: Journal of Statistical Planning and Inference.
Adaptive Bayesian Survival Modeling for High-Dimensional Multi-Omics Data
Development of structured Bayesian survival models for integrating high-dimensional biological information using biologically informed prior distributions.
Target Journal: Journal of Computational Biology.
Performance Analysis of Computational Statistics Methods for Missing-Value Imputation in Mass Spectrometry-Based Label-Free Quantitative Proteomics
Comparative evaluation of computational methods for missing-value imputation in mass spectrometry-based proteomics.
Target Journal: BMC Bioinformatics.
I welcome opportunities for interdisciplinary collaboration in
Bayesian statistics
Computational statistics
Survival analysis
High-dimensional statistical inference
Biomedical research
Statistical computing
Data science applications
Researchers, faculty, and students interested in collaborative research are encouraged to get in touch.
My research and teaching are closely connected. My research informs my teaching by incorporating modern statistical computing, simulation methods, and real-world data applications into statistics education. Likewise, teaching continually inspires new questions about statistical communication, computation, and data-driven problem solving, strengthening both my research and instructional practice.
Additional information about my publications, citations, research activities, and professional experience is available through: