Fengjiao Li

E-mail:f.li.3@research.gla.ac.uk 

 

Personal Website 

ORCID iDhttps://orcid.org/0009-0006-9075-0818

Research title: Towards GeoAI-Enhanced Mobility-Based Health Risk Analysis: Embedding Spatial Intelligence into Graph Neural Networks for Dynamic Population Modelling

Research Summary

My research lies at the intersection of Geospatial Artificial Intelligence (GeoAI), spatial statistics, and machine learning, with a particular focus on developing advanced Graph Neural Network (GNN) models for analyzing spatial and dynamic population processes. Drawing on my statistical training, I aim to create interpretable, robust, and scalable methods for understanding how populations move and how diseases spread over space and time.

My current doctoral project focuses on embedding spatial structures, population mobility, and temporal dynamics into neural network architectures to support health risk analysis in urban environments. I work on integrating statistical reasoning with AI-driven models to improve epidemic modeling, risk prediction, and intervention planning.

By combining insights from statistics, epidemiology, and geographical data science, I strive to develop data-driven tools that are not only technically rigorous but also practically useful for public health decision-making.

Publications

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Jump to: 2025
Number of items: 1.

2025

Li, Fengjiao, Wu, Meiliu ORCID logoORCID: https://orcid.org/0000-0002-5246-4603 and Basiri, Ana ORCID logoORCID: https://orcid.org/0000-0002-2399-1797 (2025) Mobility vs. Contiguity: Spatially Explicit Graph Neural Networks for COVID-19 Forecasting. In: 6th Spatial Data Science Symposium (SDSS 2025), 04-05 Dec 2025, (doi: 10.5281/zenodo.17660772)

This list was generated on Fri Dec 26 14:07:44 2025 GMT.
Jump to: Conference Proceedings
Number of items: 1.

Conference Proceedings

Li, Fengjiao, Wu, Meiliu ORCID logoORCID: https://orcid.org/0000-0002-5246-4603 and Basiri, Ana ORCID logoORCID: https://orcid.org/0000-0002-2399-1797 (2025) Mobility vs. Contiguity: Spatially Explicit Graph Neural Networks for COVID-19 Forecasting. In: 6th Spatial Data Science Symposium (SDSS 2025), 04-05 Dec 2025, (doi: 10.5281/zenodo.17660772)

This list was generated on Fri Dec 26 14:07:44 2025 GMT.

Supervisors

Grants

University of Glasgow - The China Scholarship Council (CSC) Co-operative Scholarship (2024-2028)

Conferences

Li, F., Wu, M., & Basiri, A. (2025, November 20). Mobility vs. Contiguity: Spatially Explicit Graph Neural Networks for COVID-19 Forecasting. The 6th Spatial Data Science Symposium (SDSS 2025). https://doi.org/10.5281/zenodo.17660772

Teaching

Teaching Assistant：

  • Maths 2T，School of Mathematics and Statistics, 2025 Spring
  • Maths 2D，School of Mathematics and Statistics, 2025 Spring

Research Assistant：

RA, in Social Sciences Administration within the College of Social Sciences. 2025 Summer.

RA, in  School of Geographical & Earth Sciences, 2026 

Additional Information

  • M.Sc. in Applied Statistics (Biostatistics), University of Liverpool, 2024
  • B.Sc. in Applied Mathematics and Statistics, University of Wisconsin, 2022
  • B.Sc. in Economic Statistics,Suzhou University of Technology, 2022

     

    Professional Experience：

  • Biostatistician

    Zenith CRO, Shanghai, China
    12/2023 – 09/2024

    Transcenta, Suzhou, China
    06/2023 – 10/2023