Research
Understanding human genetic diversity to make precision medicine genuinely precise for everyone.
The way we study human genetics today is fundamentally skewed. The vast majority of genomic research draws from populations of European descent — leaving the most genetically diverse continent, Africa, as the least represented in the data that drives drug development, diagnostic tools, and treatment decisions.
My research is about changing that equation. I combine population genomics, multi-omics approaches, and data science to produce insights that are more representative, more inclusive, and ultimately more useful for human health.
Population Genomics & African Genetic Diversity
African populations harbour more genetic diversity than the rest of the world combined — yet they remain vastly underrepresented in global genomic studies. My work focuses on characterising genetic variation in understudied African communities, developing computational frameworks for population-scale sequencing data, and contributing to the growing body of knowledge that will make precision medicine genuinely precise for everyone.
This includes variant calling and annotation pipelines for whole-genome and whole-exome data, population structure analysis, and pharmacogenomics studies that connect genetic variants to drug response.
Multi-Omics Integration
A genome sequence tells you what's possible. But understanding what's actually happening in a biological system requires looking at multiple layers simultaneously — DNA, RNA, proteins, metabolites. I integrate data across genomics, proteomics, and metagenomics to build richer, more complete biological models.
My training spans both wet-lab techniques (NGS library prep, LC-MS/MS sample processing) and the computational side (bioinformatics pipelines, statistical modelling, and multi-omics data integration frameworks). This dual fluency lets me design studies end-to-end, from sample collection to insight.
Health Data Science & Translational Research
Biological datasets are growing faster than our ability to make sense of them. I develop computational pipelines and machine learning approaches to extract meaningful patterns from large-scale genomic and health data — with a focus on translational applications that connect bench science to clinical relevance.
Whether it's building variant annotation tools, applying unsupervised learning to population-scale datasets, or creating reproducible analysis workflows, the goal is always the same: turn data into decisions that improve outcomes.
Interested in collaboration or speaking?Let's connect.