I'm a PhD researcher in Statistics at the University of Edinburgh, working at the intersection of causal inference, machine learning, and large-scale health data. My research focuses on estimating causal effects from observational healthcare and genomic data, specifically applying Collaborative Targeted Maximum Likelihood Estimation (C-TMLE) to the UK Biobank.
Before my PhD, I spent 4+ years as a data scientist and analyst, delivering data-driven insights for multinational clients including Microsoft, Unilever, P&G, and Betway. I love bridging the gap between rigorous statistical methodology and real-world impact.
I'm currently looking for a summer 2027 placement or internship in Edinburgh, ideally in health/life sciences, financial services, or tech.
- Causal Inference — TMLE, C-TMLE, IPW, propensity score methods
- Statistical Modelling — high-dimensional regression, semiparametric efficiency theory
- Machine Learning — LASSO/elastic net, SuperLearner, ensemble methods
- Data Visualisation — ggplot2, Tableau, Power BI, Looker
- Health & Genomic Data — Dataloch, DecodeME, UK Biobank, observational study design, missing data
A comparative simulation study benchmarking TMLE and C-TMLE estimators across low- and high-dimensional settings. Built entirely in R using glmnet, SuperLearner, and influence function-based inference. Motivated by my PhD research on causal effect estimation in genomic data.
R TMLE C-TMLE glmnet Causal Inference Simulation
Actively extending TMLE.jl — a Julia package for Targeted Minimum Loss-Based Estimation published in the Journal of Open Source Software (2025), by integrating Collaborative TMLE (C-TMLE) estimators into the package. Successfully implemented Lasso C-TMLE with bootstrap simulation studies and test coverage. Developed in collaboration with the TARGENE research group at the University of Edinburgh.
Julia TMLE C-TMLE Causal Inference Open Source Research Software
- 🎓 PhD in Statistics — University of Edinburgh (causal inference, missing data, UK Biobank)
- 🔧 Integrating C-TMLE estimators into
TMLE.jl(Lasso C-TMLE implemented & tested) - 🔍 Actively seeking a summer 2026 placement in Edinburgh
- 📖 Reading: What If by Hernán & Robins (the causal inference bible)
I'm always happy to connect — whether it's about causal inference, data science, or potential collaborations.
"Data is not just numbers — it's the story of people's lives."