Research

My research interests span two main areas: large-scale data analysis and Bayesian methodology. I apply Bayesian inference to address real-world challenges across various domains, particularly in Biostatistics and Bioinformatics.

One of the most engaging aspects of my work is analyzing large-scale datasets, a critical task in both applied and theoretical research. For example, in Genetics, we often examine thousands—or even millions—of features such as genes, SNPs, and proteins to identify associations with specific diseases. A key metric in these investigations is the posterior probability that a given feature is associated with the outcome, given the observed data. Estimating these probabilities presents unique challenges in different research contexts, which I find both intellectually stimulating and rewarding.

Another area of focus for me is the analysis of next-generation sequencing (NGS) data. Despite rapid advancements in the field, selecting efficient tools for detecting mutations or gene fusions from DNA/RNA samples remains a significant challenge.

Recently, I have also begun exploring topics such as cell-type decomposition, strain mixture identification, and phylogenetic inference—each of which presents exciting new opportunities for methodological development and practical application.