Rate My Professor Rob Free

Dr Robert Free is a Lecturer in Health Data Science in the School of Computing and Mathematical Sciences at the University of Leicester. He holds key leadership positions including Director of Informatics for the NIHR Leicester Biomedical Research Centre, Board member of the Leicester Institute for Precision Health, and Associate Director of Health Data Research UK Midlands. Free's background spans molecular biology, genetics, bioinformatics, clinical informatics, software engineering, artificial intelligence, and machine learning. He earned his PhD from the University of Leicester's Department of Microbiology and Immunology between 2016 and 2021. His work emphasizes collaboration at the clinical interface to advance healthcare technologies through data science.

Free's research centers on developing and translating advanced data science approaches into practical tools for healthcare professionals, with a focus on integrating complex multidisciplinary data sources such as omics data, clinical test results, coded observations, imaging, and mobile health data. Key projects include AI/ML-based models to predict outcomes for community-acquired pneumonia from multifaceted hospital admission data, self-learning digital twins for acute respiratory patients in A&E, and secure AI clinical decision support systems with data validation and monitoring. He has contributed as technical lead and co-investigator to high-impact studies including the East Midlands Breathomics Pathology Node, Extended Cohort for E-health, Environment and DNA (EXCEED), and national pandemic efforts like the UK Research study into Ethnicity and COVID-19 outcomes in Healthcare workers (UK-REACH). Notable publications include 'A data-driven framework for clinical decision support applied to pneumonia management' (Frontiers in Digital Health, 2023), 'Specialist pneumonia intervention nurse service improves pneumonia care and outcome' (BMJ Open Respiratory Research, 2021), 'LabPipe: an extensible bioinformatics toolkit to manage experimental data and metadata' (BMC Bioinformatics, 2020), and 'GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies' (European Journal of Human Genetics, 2014). Free supervises several interdisciplinary PhD students across data-driven AI health analytics, clinical decision support infrastructure, and complex data integration challenges.