My research focuses on both Bayesian and frequentist approaches in mathematical statistics, emphasizing practical applications. I primarily explore Bayesian non-parametric and semi-parametric methods, statistical inference for finite populations, efficient random variate generation, and advanced Markov Chain Monte Carlo (MCMC) algorithms. Additionally, I have expertise in analyzing mixture models, regression analysis, and effective model selection techniques.

To learn more about my work, you can visit my Google Scholar profile, which lists my publications. Preprints of selected papers and technical reports can also be accessed through my ResearchGate profile.

You are welcome to download my PhD thesis, titled "Contributions to the Bayesian Analysis of Mixture Models", which explores advanced Bayesian methodologies and their applications.

The displayed graphic illustrates a density estimate (left) for the well-known galaxy dataset, alongside scaled density estimates and the single best clustering (right). These results were obtained by effectively addressing the label-switching problem. For more information, please see this reference, which offers a comprehensive overview of the label-switching problem and its solutions. Additionally, you can explore the label.switching library in R, which implements multiple solutions to this problem, including those discussed in the reference.