Edinburgh Medical School | The University of Edinburgh
Data Science, Causal Inference, and Medical Impact
Hi there! My name is Chris Oldnall. I am Lecturer in Data Science at the Edinburgh Medical School and my work focuses on causal inference, statistical modelling, and machine learning methods for complex biomedical and population health data. I am particularly interested in how we move from association to explanation; developing methods that are mathematically rigorous, interpretable, and useful in real clinical and public health settings.
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
Hi there! My name is Chris Oldnall. I am Lecturer in Data Science at the Edinburgh Medical School and my work focuses on causal inference, statistical modelling, and machine learning methods for complex biomedical and population health data. I am particularly interested in how we move from association to explanation; developing methods that are mathematically rigorous, interpretable, and useful in real clinical and public health settings.
Alongside research, I am heavily involved in teaching and curriculum development across statistics, epidemiology, and machine learning. I am especially passionate about making quantitative methods more accessible to students from diverse academic backgrounds.
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 care strongly about widening participation and improving access to quantitative education. I have contributed to mentoring and outreach initiatives including CodeBar, the Royal Statistical Society, The Brilliant Club, and PreWired. I particularly enjoy supporting students and early-career researchers who are developing confidence in statistics, programming, and data science for the first time.
Outside academia, I am interested in science communication, educational design, long-distance running, and building practical tools that make complex methods easier to understand and apply. If you are interested in collaboration, student supervision, teaching partnerships, or invited talks, please feel free to get in touch.