Biography
I am a PhD candidate applying machine learning and artificial intelligence (AI) to public safety, and member of the Jerry Lee Centre of Experimental Criminology. Prior to my PhD, I completed my undergraduate degree at Harvard University, where I graduated magna cum laude with highest honours. I have also obtained an MPhil degree from the University of Cambridge in Criminological Research and completed a visiting studentship at the University of Oxford.
I have sat on a Steering Group for the Home Office, as well as lectured for the National Crime Agency, Home Office, and the University of Cambridge. I have also worked with police departments across the USA and UK, and taught courses on big data, data visualisations, and AI as it relates to policing.
Research
Most of my published work revolves around forecasting: using machine learning to predict who will and who will not commit a crime in a way that is both ethical and statistically sound. My models can typically predict crime more effectively than the status quo of professional judgement or unvalidated risk assessments. As such, I believe they can be used to foster better criminal justice decisions, decisions that result in fewer low-risk individuals incarcerated, a reduction in serious crime, and are based on instruments that have partially remediated historical biases commonly found within criminal justice data.
My other projects involve using language models to sift through large amounts of police data, as well as use computer vision to detect violence in both body-worn video and CCTV footage.
Publications
Verrey, J., Ariel, B., Harinam, V., & Dillon, L. (2023). Using machine learning to forecast domestic homicide via police data and super learning. Scientific Reports, 13(1), p.22932.
