My research interests are varied but can mainly be categorized into two broad areas: large-scale data analysis and Bayesian analysis. I implement the Bayesian inference to solve real-world problems in a broad range of contexts including Biostatistics and Bioinformatics.

Analyzing large-scale data is one of the most appealing tasks in variety of applied and theoretic fields. For example in Genetics, thousands or millions of features (genes, SNPs, proteins,…) are analyzed together to discover if any of them is associated with a specific disease. A common measure of discovery is the posterior probability that a feature is associated given some observed data. Estimating this posterior probability in different research problems requires its own challenges, and I enjoy it!

Analyzing next generation sequencing data is another topic of my interest. With so many developments in the context, choosing an efficient tool to perform the task of mutation/fusion detection based on DNA/RNA samples still remains a major challenge. I am working on developing an advanced Bayesian model that allows an accurate mutation detection.

I have recently started working on cell-type decomposition, strain mixture identification and inference in phylogenetic trees.