Yue Li

My research sits at the intersection of the built environment, environmental health, and urban systems, with a focus on how spatial and infrastructural conditions shape population health outcomes. I am particularly interested in the ways that urban form, transportation systems, service accessibility, and environmental exposures interact to influence mental health, dementia risk, and mortality across diverse communities. Using large-scale linked datasets, I aim to identify structural determinants of health disparities and translate empirical evidence into actionable planning and public health strategies.

My current work examines how urbanicity and neighbourhood context contribute to uneven health risks in England, leveraging national census, mortality, and environmental datasets. I apply statistical and machine learning models—including Cox proportional hazards models, causal inference frameworks, and graph-based deep learning architectures—to uncover nonlinear dose-response relationships and quantify the potential health benefits of targeted environmental and infrastructural interventions.

Beyond my primary research, I have worked across multiple domains, including environmental engineering, transportation planning, primary health care efficiency evaluation, microplastic risk assessment, and occupational health. These projects reflect a broader commitment to addressing environmental and social determinants of health across scales—from individual behavioural patterns to system-level infrastructure and policy.

I have published in international peer-reviewed journals spanning environmental health, public health, and environmental management, contributing methodological tools and empirical insights relevant to urban planning, environmental epidemiology, and health policy. My work continues to expand toward integrating machine learning with causal inference to inform equitable and sustainable urban development.