Data Scientist specialising in bioinformatics, machine learning, and clinical data analytics. MSc Biomedical Engineering · Imperial College London.
About me
I'm a Bioinformatics-focused Data Scientist with a background in Biomedical Engineering. I build production-scale genomic analytics pipelines, deploy machine learning models for patient stratification, and translate complex molecular datasets into structured, decision-ready evidence.
I hold an MSc in Biomedical Engineering from Imperial College London and a First Class BEng from Queen Mary University of London. This site is where I write about the ideas and topics that shape how I think, from genomics to machine learning to medicine.
Multivariable calculus, linear algebra, and signal processing, the rigour behind every model I build.
Living systems are noisy, dynamic, and deeply interconnected, precisely what makes data science on them so compelling.
The Blog
Biology and engineering seem worlds apart — yet their intersection is where the most exciting science happens. Bioinformatics and data science give me the tools to make sense of complex biological systems: finding patterns in genomic data, modelling disease progression, and translating raw molecular information into something meaningful.
The human genome contains billions of base pairs. A single RNA-seq experiment generates millions of reads. A clinical dataset can span thousands of patients across years of follow-up. Making sense of that data, rigorously and reproducibly, is both a technical challenge and a creative one.
This blog is where I explore those ideas, from the logic of machine learning pipelines to the biology they're trying to decode. I hope you find something here that sparks your curiosity.
