Lewis recently completed a Doctorate of Engineering at Leeds Beckett University. His thesis focused on the use of predictive artificial intelligence modelling for the diagnosis of dementia and mild cognitive impairment.

Lewis has been concentrating on the use of explainable artificial intelligence to extract information about the way machine learning algorithms interpret medical data. Prior to this, Lewis completed his Master’s in Information & Technology, where his interest in data science began.

Having previously worked with medical records pertaining to real patients, Lewis has developed an interest in applying statistical analyses to real-world data with the potential for positive impact.

Lewis recently completed a project exploring the relationships between school data and Not in Employment, Education or Training (NEET) status post-secondary school. In particular, he explored the relationship between school absence and NEET risk, as well as how school attainment (GCSE performance) mediated this relationship.

Lewis’s current project seeks to examine the characteristics and trajectories of young people in care who are recorded as having one or more episode of being missing. These will subsequently be compared to those of children who have not been missing in order to establish whether there are any systematic differences between the two groups. In the second phase of research, the research team will draw on linked education and health data to assess whether interactions with other services may provide additional utility in identifying those individuals at most risk of becoming missing.