Nicola Fox

I am a mixed-methods criminologist and PhD candidate at the University of Manchester, with a professional background in computer science and over 20 years of experience in the IT industry, including leadership roles in data analysis and programme management, working with stakeholders across disciplines and around the world.

My research focuses on missing children, child exploitation, and the use of natural language processing and AI to analyse unstructured, sensitive text data from safeguarding systems in an explainable and sustainable way.

I also teach undergraduate students on core quantitative criminology modules, equipping them with practical skills in R and Excel for data analysis in real-world criminological contexts.

I co-created the Missing Persons Information Hub (MPIH), and I am a member of:

• NPCC Missing Persons Expert Reference Group
• Coalition for Missing Children
• Artificial Intelligence and Text Mining for Social Good (ARTEMIS)
• SPRITE+ - a network bringing together people in research, practice, and policy with a focus on digital contexts
• The University of Manchester's Reading Sessions in Quantitative Criminology (RESQUANT)

My first study - "Too risky yet not risky enough: the intersecting characteristics, vulnerabilities, harm indicators and guardianship issues associated with seriously harmed missing children" - has been published in the journal Policing and Society.

My second study - "SATISFY EASY LADDERS: Accessible machine learning to facilitate analysis of larger sensitive text data samples in criminology" - published in Evidence Base, is framed as a tutorial and demonstration of using natural language processing (NLP) and machine learning with a human-in-the-loop, in an explainable and precise way, for the partial automation of labelling lengthy text documents containing sensitive crime-related data, for which only small samples are available for training models.

For more information, please visit my personal website: foxnic.github.io