My research focuses on developing statistical and computational methods for genomics data analysis, with applications in macroevolution, epidemiology, and cancer research.

Understanding the evolutionary relationships among tumour cells provides critical insights into how cancers develop, adapt, and spread. The advent and growing accessibility of single-cell sequencing technologies present an opportunity to reconstruct detailed cell lineage trees from tumour samples. My work focuses on testing and developing robust, accurate, and scalable methods to enhance our ability to interpret the complex evolutionary history of cancer at single-cell resolution.

Evolutionary histories reconstructed from genetic data carry information about population dynamics parameters across various application areas—for example, diversification rates in macroevolutionary studies, transmission rates in infectious disease epidemiology, and tumour growth rates in cancer research, among others. I am working on extensions and applications of birth–death models to improve such phylodynamic inference.

Classical statistical methods for inferring phylogenetic trees are well-established and reliable, but can be computationally intensive. My research focuses on developing likelihood-free inference techniques for phylogenetic analysis. My other research interests include machine learning techniques for sequencing data pre-processing, genotype–phenotype association studies, and other related problems.