Sam Relins

Sam Relins

Senior Research Fellow, Vulnerability & Policing Futures Research Centre
University of Leeds

Sam is a Senior Research Fellow with the ESRC Vulnerability and Policing Futures Research Centre, based at the University of Leeds. He holds a BA (Hons) in Mathematics from the University of Sheffield. Sam began his career in finance before making a transition into a research-focused data science career. Through industry experience, self-directed study and subsequent applied research roles, he has developed deep practical expertise in machine learning, AI, and statistical analysis, which he applies to interdisciplinary research areas including public health, education, and policing.

Sam’s research focuses on the responsible application of data science and AI to large-scale public sector datasets, particularly to understand and address societal inequalities. Prior to joining the Centre, he was a Senior Research Fellow at the Bradford Institute for Health Research, where he developed and implemented key data pipelines, and contributed analytical research and statistical modelling to multiple population health studies using this data. This work involved integrating complex health, education, and social care records to inform population health research and local policy. His previous experience at the Leeds Institute for Data Analytics (LIDA) involved applying advanced machine learning and NLP techniques to model clinical outcomes and identify socioeconomic predictors of health inequalities.

At the Vulnerability and Policing Futures Research Centre, Sam’s work investigates the novel application and critical implications of artificial intelligence in policing. He leads a primary research strand developing and evaluating Large Language Models (LLMs) to analyse unstructured police reports, with a focus on identifying indicators of vulnerability. This research includes creating frameworks to systematically assess the fairness and interpretability of these models in sensitive operational contexts. He also contributes to the Centre’s broader data science work, using routine administrative data to explore the intersections of policing, public health, and social vulnerability.