Speakers

Prof Inga Prokopenko is Vice-Chancellor’s Distinguished Chair and Professor of e-One Health at the University of Surrey, where she leads the Section of Statistical Multi-Omics. She previously served as Founding Co-Director and is currently a Principal Fellow of the Surrey Institute for People-Centred AI. She is Chair of the Education Committee of the European Society of Human Genetics, a member of the Scientific Committee of the Lister Institute, and Visiting Professor at the University of Lille, France. 

Prof Prokopenko obtained her PhD from the University of Pavia, Italy, in 2004. Following research in R&D at GlaxoSmithKline in Verona, she undertook postdoctoral research at the Wellcome Trust Centre for Human Genetics and the Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford. She subsequently spent six years at Imperial College London as Associate Professor before joining the University of Surrey. 

Her research spans statistical genetics, genomics, multi-omics, epidemiology and artificial intelligence, with a focus on extracting robust and biologically meaningful insights from complex, high-dimensional health data. Her major scientific contributions include the discovery of hundreds of genetic loci associated with glycaemic traits, type 2 diabetes, obesity and early growth, as well as studies demonstrating causal relationships between obesity and cardiometabolic disease, depression and type 2 diabetes, and hyperglycaemia and respiratory dysfunction. 

At Surrey, she established the Section of Statistical Multi-Omics and leads the high-performance computing infrastructure supporting large-scale genomics, AI multi-omics, and health data science research. She combines methodological development with applications to large-scale population and clinical datasets, with particular emphasis on reproducibility, robust statistical inference, validation and translation of research results into biological and health insights. 

At the Festival, Prof Prokopenko will contribute her expertise in trustworthy AI for biomedical research, including bias, data leakage and generalisability, and in making sense of complex multi-omic datasets, drawing on extensive experience of large-scale human genomic and multi-omics studies.