Edinburgh Medical School | The University of Edinburgh
Data Science, Causal Inference, and Medical Impact
I am Chris Oldnall, Lecturer in Data Science at the Edinburgh Medical School. My work combines rigorous statistics, modern machine learning, and causal methodology to answer clinically meaningful questions.
Role
Lecturer in Data Science
Base
Edinburgh Medical School
Theme
Causal methods for health and genomics
Research Projects
Current and past research across genomics, biostatistics, and optimisation.
Presentations
Talks, invited sessions, and downloadable slides.
Teaching
Academic teaching and outreach-focused resources.
Supervision
Projects, mentoring themes, and student collaboration.
Volunteering
Community programmes and youth-focused initiatives.
Industry
Consulting and applied work through Armstrong and Partners.
Profile
I am a Lecturer in Data Science at the Edinburgh Medical School with a strong mathematical background and a focus on robust, interpretable methods.
My research sits at the interface of statistics, machine learning, and biomedicine, with particular interest in causal identification, uncertainty quantification, and trustworthy AI for clinical and population-scale settings.
Current Focus Areas
Applied Data Science
- Causal inference in population genomics and medical research.
- Integrative statistical modelling for healthcare systems and policy questions.
Methodological Development
- Uncertainty quantification and explainability for high-impact AI systems.
- Bias detection and robustness analysis for large-scale model architectures.
Beyond Research
I am strongly committed to widening participation and community impact through volunteering, mentoring, and teaching innovation. This includes work with programmes such as PreWired, CodeBar, and The Brilliant Club.
If you are looking for collaboration opportunities, supervision support, or speaking contributions, please get in touch by email.