About me

I am a scientist with a multidisciplinary education spanning genomics, molecular diagnostics, and data science. Over more than 30 years I have worked across academia, healthcare, and industry — always at the intersection of genetics, statistics, and real-world health impact.

My work is driven by a simple conviction: that data, rigorously collected and carefully analysed, can improve how we understand and manage disease — whether in a genomics lab in Europe or a neonatal ward in sub-Saharan Africa.

Current position

I am currently a Senior Data Scientist at University College London (UCL), where I work on the Neotree project. Neotree is a digital health initiative that uses structured clinical data and machine learning to improve neonatal care in low-resource settings, primarily in sub-Saharan Africa. My focus is on developing and validating clinical decision support algorithms — making advanced statistical modelling useful in contexts where specialist expertise is scarce.

Background

I completed my doctoral training in genetics and genomics, followed by research positions in population genetics and molecular diagnostics. Over the years I have worked on diverse topics — from the genetics of complex traits and infectious disease to the application of machine learning in clinical practice.

My methodological background spans classical statistics, Bayesian inference, survival analysis, and modern machine learning. I am equally comfortable working at the bench of a bioinformatics pipeline or presenting findings to a clinical or policy audience.

Identifiers & profiles

Interests beyond the lab

I believe strongly in open science, reproducible research, and the democratisation of data skills. Outside the research context, I teach data science and statistics to researchers and students — helping people build the quantitative confidence to ask better questions of their data.

I am also interested in the broader picture of health — the One Health framework that links human, animal, and environmental health — and in how data science can be a practical tool for equity as well as efficiency.