Shaokun Lyu
Profile
Shaokun Lyu earned his master’s degree in Spatial Data Science at the University of Southern California. His master’s study focused on geospatial science, spatial data science, machine learning, spatial databases, and computational data analysis. He previously earned a Bachelor of Science in Data Science through the Duke Kunshan University and Duke University dual-degree undergraduate program.
He has also served as a research assistant at the Spatial Science Institute at USC and the School of Resource and Environmental Science at Wuhan University. His research applies spatial analysis, GeoAI, remote sensing, machine learning, and statistical modeling to questions related to public health, urban environments, environmental exposure, spatial accessibility, and health disparities.
During his studies in the Population, Health and Place doctoral program, Shaokun plans to focus on GeoAI-related topics and epidemiological questions, with an emphasis on how place-based factors shape health outcomes. His work will primarily engage the health and place tracks, using spatial data science and quantitative methods to study environmental determinants of health and health disparities.
Education
M.S. Spatial Data Science, University of Southern California, Los Angeles, CA
B.S. Data Science, Duke Kunshan University / Duke University, Kunshan, China / Durham, NC
Publications
Wang, S., Yoo, J., Cai, W., Yang, F., Huang, X., Sun, Q. C., Lyu, S., et al. (2024). Reducing the social inequity of neighborhood visual environment in Los Angeles through computer vision and multi-model machine learning. Sustainable Cities and Society, 110, 105177.
Ibrahim, M., Lyu, S., Ahmad, N., Nasir, M. J., & Wang, S. (2025). Unequal green: A mixed methods spatial assessment of urban green space provision, quality, and accessibility in Mardan, Pakistan. Urban Informatics, 4, Article 19.
Wang, S., Lyu, S., Han, M., Chen, H., & Li, X. (2026). Evaluating spatial accessibility based on varying travel choices: A case study of accessing vaccine sites in Greater London. Cities, 174, 107043.