Dr Yiyang Gao (she/her)

I am a computational social scientist and population geographer specialising in spatial inequalities and their cascading effects on educational, environmental, and health outcomes. Currently at the Sheffield Methods Institute, my work sits at the intersection of advanced quantitative methods, spatial analysis, and social justice.

My research journey began with educational segregation, where I developed novel analytical frameworks combining multilevel modelling with decomposition techniques to understand ethnic school segregation patterns across England. This foundational work, published in Population, Space and Place and British Educational Research Journal, established my expertise in handling large administrative datasets and complex hierarchical data structures.
However, my research has evolved beyond education to embrace a broader vision: accessibility as the common thread linking spatial inequalities. I've come to understand that access (to schools, jobs, clean air, and healthcare) shapes cascading disadvantages across multiple life domains. This realisation drives my current work on three converging research streams: graduate spatial mobility and regional brain drain/gain patterns, transport infrastructure's role in educational equity, and the intersection of environmental justice with health inequalities.
I am pioneering methodological innovations in MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy), developing approaches that integrate causal inference, machine learning, and spatial analysis to understand how intersecting identities and place-based factors create health disparities.
My ultimate goal is to establish Computational Health Geography as a field that uses AI and advanced spatial methods to understand and address the geographic dimensions of health inequalities. I believe that by combining rigorous quantitative methods with a deep understanding of how place shapes life chances, we can develop more effective interventions for spatial justice.
Looking ahead, I aim to lead research that doesn't just describe inequalities but actively works to reduce them through methodological innovation, policy engagement, and the democratisation of analytical tools for social good.