UKRI Future Leaders Fellowship

I hold a UKRI Future Leaders Fellowship, which funds my research into developing new statistical methods to help tackle modern slavery. Modern slavery is a hidden crime, and we often don’t know how many people are affected or where they are. My fellowship focuses on building computational tools that can help answer these questions using data. The aim is to give governments, charities, and police forces better evidence so they can protect more people.

Scale of Harm: Estimating child exploitation trafficking in the Philippines

I worked with the International Justice Mission (IJM) on their Scale of Harm project, which aimed to estimate how many children in the Philippines are being trafficked to produce child sexual exploitation material. Using survey data and statistical models, we estimated how common this crime is across different parts of the country. The results helped IJM and the Philippine government understand the true size of the problem and plan where to focus their efforts to protect children. The law in the Philippines and the US was changed as a result of this work.

Comparative Judgement

Some things are really hard to measure with numbers alone, like how deprived a neighbourhood is or how widespread a social problem might be. My comparative judgement work asks experts to look at two areas side by side and say which one is worse. By collecting lots of these simple comparisons, I can use statistical models to build a full ranking and create maps that show where problems are most serious. This approach has been used to map forced marriage across England and urban deprivation in Dar es Salaam, Tanzania. Local councils have used these maps to train their staff, and MPs have asked questions in Parliament based on this research.

Multiple Systems Estimation

When people are victims of crimes like modern slavery, most are never identified by any official organisation. Multiple systems estimation is a way of using the small number of victims who do appear in different databases to estimate how many are being missed. I develop new statistical and machine learning methods using neural networks to make these estimates more accurate. This work helps governments and charities understand the true scale of hidden crimes so they can respond more effectively.