Peter WG Tennant

Peter WG Tennant is an Associate Professor of Health Data Science at the University of Leeds and a Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence. He leads the Causal Inference Interest Group and Introduction to Causal Inference Course for Health and Social Scientists at the Alan Turing Institute.

His research focuses on adapting and translating contemporary causal inference methods into health and social sciences, with particular emphasis on epidemiology, biostatistics, maternal and child health, and nutritional research. He has developed significant methodological contributions in directed acyclic graphs (DAGs), compositional data analysis, and observational data interpretation.

His 15 most recent publications demonstrate expertise in causal inference methodology, nutritional epidemiology, and health data science. Key themes include analyses of compositional data, causal diagram interpretation, and methodological challenges in observational research.

Scientific Awards and Recognition

• Highest Scoring Abstract (Shortlisted), Society for Social Medicine 66th Annual Scientific Meeting (2022)
• Best Poster Presentation (Winner), Society for Epidemiologic Research 2022 Meeting (2022)
• Rising Star Award (Nominated), Society for Perinatal and Pediatric Epidemiology (2020)
• THE Innovative Teaching Award (Nominated), Times Higher Education Awards (2019)
• Best Blogger (Shortlisted), Mind Media Awards (2014)

As an academic leader, he has supervised numerous PhD and Master's students in health data science and epidemiology. His teaching includes module leadership positions for advanced epidemiology and causal inference courses at the University of Leeds.

He maintains active engagement with media and public through podcasts, YouTube presentations, and social media, with over 16k Twitter followers and multiple public speaking engagements, including stand-up comedy performances about academic life.