Personal profile


Dr Sperrin is Senior Lecturer in Health Data Science in the Health eResearch Centre (, University of Manchester, with a background in statistics.

Research interests

My primary research interests are in the development of new statistical methodology for prediction, in healthcare applications. Specifically:

1. Role of causality in prediction. Wile causality is typically seen as a separate type of analysis to prediction, it's increasingly recognised that incoporating causality in prediction models brings a number of advantages. For example, the ability to ask 'what if' prediction questions (counterfactual prediction), improving model generalisability and face validity, and the study of fairness. 

2. Adaptive sampling and informative observation/missingness in prediction. Informative observation refers to the fact that presence or absence of data is almost always informative (e.g., in electronic health records). This is both a problem and opportunity, and I am interested in the implications. Adaptive sampling is a proactive question about when to measure things, trading off improved prediction with costs.

3. Model updating and validation. Prediction model performance needs to be checked, and maintained over time and space. 

If you are interested in the above areas, we are always looking for new people to join our group, at all levels. Please get in touch to find out more, making your approach as specific as possible.


Dr Sperrin contributes to the MSc Health Data Science. He also runs training courses for NHS staff and supports undergraduate and postgraduate teaching across the Faculty of Health and Medicine.


Memberships of committees and professional bodies

Royal Statistical Society, CStat.


Methodological knowledge

Statistics, Epidemiology, Data Science


PhD, Statistics, Lancaster University (2010)

MMORSE (Masters in Maths, Operational Research, Statistics and Economics), University of Warwick (2006)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 10 - Reduced Inequalities

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI
  • Digital Futures
  • Christabel Pankhurst Institute
  • Healthier Futures


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