Bio
Professor Mark Mon-Williams (MMW) holds a Chair in Cognitive Psychology at the University of Leeds, and is Professor of Psychology at the Bradford Institute of Health Research and Professor of Paediatric Vision at The Norwegian Centre for Vision. 25 years ago, MMW made fundamental contributions to our understanding of the sensorimotor impact of Virtual Reality (work that was headline news around the world).
He is Director of the Centre for Immersive Technologies at the University of Leeds. MMW is Director of the Centre of Applied Education Research (a partnership between the Universities of Leeds and Bradford together with the Department for Education, the Education Endowment Foundation, and the Bradford Local Authority) – a multidisciplinary Centre based at the Bradford Royal Infirmary. MMW leads a research group that use their fundamental scientific contributions to address applied issues within surgery, rehabilitation and childhood development and he has responsibility for ensuring societal impact arises from research conducted within the University of Leeds' Faculty of Medicine and Health.
MMW leads the NHS CLAHRC committee responsible for 'Identifying and Supporting Children with Difficulties', and is an executive member of the Born in Bradford project (a longitudinal cohort study following the lifelong development of 13,500+ children). His research is funded by a number of organisations including the EPSRC, EEF, MRC and ESRC. MMW is a Founder Member of the Priestley Academy Trust (a multiple academy trust that includes the first school known to provide free meals to children), and sits on the Bradford Opportunity Area partnership board.
Research interests
MMW’s Turing based research builds on a recently awarded Medical Research Council Mental Health Data Pathfinder grant (£1.5 Million) that is linking two large cohort datasets (the Avon Longitudinal Study of Parents and Children and Born in Bradford) in order to explore ways of decreasing the incidence of mental health problems. The goal is to create generative models that can better predict the childhood risk factors for mental health problems. The data analytic and artificial intelligence expertise within the Turing Institute will help ensure this project ‘revolutionises healthcare’.
A key mental health challenge is the effective early identification of children at risk of a mental health problem, so that early intervention may prevent future disability. Linked to this is the challenge of identifying factors that protect some children from developing mental illness, despite experiencing extreme adversity (including adverse events that lead to public care). Better understanding of these issues could empower service providers (including schools, health and social care services) to support children better, and mitigate the risk of children developing mental health problems. Risk prediction is improved by greater availability of relevant information which can also help reduce bias associated with single measures.
We are in the process of linking education, health and social care data, including data on receipt of public care, with study data to identify risk and protective factors. This work opens up the possibility of producing powerful predictive models that can improve our understanding of how underlying factors influence the probability of mental ill health.