Andromachi Tseloni is Professor of Quantitative Criminology at Nottingham Trent University, and Co-Editor of Criminology. Her research revolves around five broad themes: risk and protective factors of criminal victimisation across population groups and communities, explanations and distributive justice of the crime drop, crime perceptions, social capital and cross-national comparisons.

Machi is Academic Lead of the Ministry of Justice Data First project supporting the development of ethical, independent and transparent multifaceted evidence for improving justice outcomes. The project has won the Analysis in Government Collaboration Award 2024.

Her work informs neighbourhood crime prevention police operations, the Home Office Safer Streets Fund Crime Prevention Toolkit, Neighbourhood Watch strategy and the Department for Levelling Up, Housing and Communities Decent Homes Review. She has been awarded the ONS Research Excellence Award 2019 and the ONS Linked Administrative Data Award 2022.

"The wealth of information within administrative data systems in the UK offers immense potential for building the evidence base to address theoretical knowledge and policy gaps. However unlocking this potential requires a rounded vision balanced with awareness of its limitations and cross-sectional inter-disciplinary long-term collaboration.

"The value of ADR UK lies in this multifaceted approach. It supports transparency, dialogue and collaboration across stakeholders starting from data sharing, creating research-ready datasets, to open and competitive excellent research commissioning that impacts upon practice and policy, not least through research-informed recommendations for future administrative data recording to expand the wealth and quality of evidence for the public good.

"Thrilled to be part of ADR UK’s network, my focus lies in: raising awareness of administrative data potential; expanding quantitative skills and changing evidence seeking mindset; and promoting wide use of administrative data in an ethical, responsible and transparent manner with input from organisations representing those administrative data refers to."