My research develops computational methods for reconstructing cancer cell lineage trees from single-cell RNA sequencing data. Tumor cells accumulate somatic mutations as they divide, and these shared variants can be used to infer evolutionary relationships among cells—revealing how tumors diversify, spread, and develop treatment resistance.

I focus on long-read single-cell RNA sequencing, which captures full-length transcripts and enables signals unavailable from short reads, such as variant phasing, copy-number alterations, and isoform composition. My PhD project involves building an open-source simulator to benchmark phylogenetic inference from long-read scRNA-seq data, and extending the GT16 substitution model within the BEAST2/Phylonco framework to incorporate these long-read-specific signals. 

Previously, my Honours research systematically benchmarked single-cell phylogenetic methods across thousands of simulation scenarios, quantifying the effects of mutation count, sequencing error, and ascertainment bias correction on tree accuracy.