👋🏿 About me
I am a Data Analyst at the University of Oxford, working with large-scale clinical and electronic health record (EHR) data to support reproducible research and evidence generation in healthcare. I have over seven years of experience working with complex health datasets, developing transparent analytical workflows, and producing statistical outputs for research and reporting.
My work focuses on building reproducible pipelines in R for data ingestion, cleaning, validation, and analysis. I am particularly interested in ensuring that analytical processes are well-documented, scalable, and interpretable. I have experience working with multi-table healthcare data structures and relational datasets, and I am comfortable translating these into structured workflows and database queries.
Alongside my professional role, I am currently pursuing an MSc in Statistics and Data Science (Biostatistics). My academic and applied work increasingly intersects with artificial intelligence, including developing Shiny applications and experimenting with LLM-powered tools to enhance data interaction and usability.
Beyond my technical work, I am an active contributor to the R community. I co-organise community initiatives and enjoy creating resources that make data science more accessible, including teaching materials, small R projects, and visual explanations of statistical concepts.
🔍 What you’ll find on this site
This website brings together different aspects of my work:
- 📄 Publications: research outputs including journal articles and conference papers
- 🎤 Talks: conference presentations, workshops, and community talks
- 📚 Courses: training and teaching materials in R and statistics
- 💻 Fun R Things: small projects, experiments, and creative coding work
- 🎨 Stats Comics: visual explanations of statistical ideas
- ✍🏿 Microblogs: short posts with practical R tips and insights