pathway: Advanced Quantitative Methods Alumni

Former Bolivian president, Evo Morales, had been criticized for accommodating neoliberal policies and state-corporate alliances, widely considered to harm the people who supported his election into office. By using difference in difference with matching, as well as the synthetic control method, which is innovative to social sciences, my aim with this project is to provide new insights on the impacts of Public Private Partnerships (PPPs) for development and examine how such partnerships may, contrary to popular opinion, actually be improving the livelihoods of indigenous peoples.

My research seeks to identify the barriers at different organisational levels that affect access to higher education (HE) in Nigeria and the additional role that parental perception of education plays. I intend to answer my research questions by conducting a programme of original item response theory, multilevel, and structural equation modelling analyses of complex clustered secondary data on two cohorts of individuals, households and communities data as it relates to access to Higher Education in Nigeria.

My research project is looking at applying advanced statistical models, including machine learning and multilevel modelling, to large forensic datasets. This includes datasets from prisons and from alternative provision school settings. The aim of this is to better understand and predict risk of outcomes such as violence, poor mental health, and recidivism in prisoners, and contact with the criminal justice system in adolescents. I am interested in the cumulative risk effects of the presence of neurodisabilities (including autism and traumatic brain injury), the lifetime impact of traumatic experiences, and risk factors associated with belonging to minority ethnic groups.

My research looks at different forms of urban flows, such as Intra-national Migration or patients flows within City of Bristol, and explores the ways we try to predict them. This includes review of methodologies that has been developed in past, but mainly explores the suitability of Machine Learning algorithms for flow prediction. Most importantly, the focus of the research is on the spatial effects we observe in urban flow data, which are the main feature of the urban flows. Predicting human flows can be beneficial in wide range of fields, transport, urban planning and even health care.