I am a researcher at the BLG Data Research Centre, based at the University of Essex and Research Fellow at the Department of Government. At the BLG Data Research Centre we deploy applied analytics to inform key policy questions, turning data into knowledge and knowledge into impact. My expertise is in prediction, using statistical methods and supervised machine learning techniques.
My role is two-fold: to inform policy questions from local government and charities and to aid the prediction of political conflict within the academic field of political science. My policy role involves engagement with stakeholders such as Essex County Council, Essex Police and the West Midlands Violence Reduction Unit, exploring a range of policy questions and data analysis related to crime, health and economic growth. Most recent projects include forecasting the referral of abuse cases in the context of the COVID-19 pandemic and exploring the impact of sports interventions on the reduction of violent crime. Through the centre, I also provide training in introductory modules to statistics and data science to our stakeholders.
My current academic research agenda is focused on developing two important bodies of conflict research. The first explores the emergence of nonviolent resistance campaigns and the impact that nonviolent action has on ongoing civil war and peace processes. The second, focuses on analysing armed violence; including the dynamics of armed militia violence and the effectiveness of UN peacekeeping at the subnational-level. This currently consists of the analysis of violence and UN peacekeeping across Africa and within three case-studies: Darfur, the Central African Republic and the Democratic Republic of Congo. My research is published in the Journal of Peace Research, Journal of Conflict Resolution, the Journal of Global Security Studies and Mobilization.
My more general research interests include: nonviolent resistance and nonviolent action, peace building and peace-processes, non-state armed actors, UN peacekeeping and mixed methods (i.e. combining qualitative, statistical and/or machine learning approaches).