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Hi , I am
J Yan .

Computational Social Scientist

Understanding health inequalities with AI

about me

Computational social scientist dedicated to understanding health inequalities with advanced AI algorithms.

I specialise in the study of health disparities leveraging machine learning, deep learning, and explainable AI. My research focuses on social determinants of health and their influence on mortality and morbidity outcomes. Currently a DPhil student at Oxford, supervised by Prof. Charles Rahal and Prof. Ridhi Kashyap.

Research Interests: Explainable AI · Health Inequalities · Social Determinants · Computational Demography · Model Evaluation

location

Oxford, UK

email

jiani.yan@wolfson.ox.ac.uk

profile_image

education

2014 - 2018

BSc
Computer Science

Southwestern University of Finance Economics

Chengdu, China

2018 - 2019

MSc
Financial Economics

University of Birmingham

Birmingham, United Kingdom

2020 - 2022

MPhil
Sociology & Demography

University of Oxford

Oxford, United Kingdom

2022 - Now

DPhil
Computational Social Science

university of oxford

oxford, united kingdom

research projects

Featured work in computational social science and health inequalities

Social Determinants of Health

American Journal of Epidemiology · 2026

@article{yan2026sdoh,
  title   = {Revisiting the social determinants of health with explainable AI: a cross-country perspective},
  author  = {Yan, Jiani},
  journal = {American Journal of Epidemiology},
  volume  = {195},
  number  = {3},
  pages   = {681--688},
  year    = {2026},
  doi     = {10.1093/aje/kwaf205}
}

Examining how social factors influence health outcomes using explainable AI and machine learning approaches.

  • Among people aged 50 and over, age and gender are the consistent predictors of mortality across every context studied.
  • Demographic and socioeconomic factors are the most important social domains for predicting death in all contexts.
  • The importance of other social factors is age-dependent and context-dependent.
Read Paper

Prediction Limits

Nature Computational Science · 2025

@article{yan2025limits,
  title   = {On the unknowable limits to prediction},
  author  = {Yan, Jiani and Rahal, Charles},
  journal = {Nature Computational Science},
  volume  = {5},
  number  = {3},
  pages   = {188--190},
  year    = {2025},
  doi     = {10.1038/s43588-025-00776-y}
}

Exploring the unknowable limits to prediction in computational social science.

Read Paper

RobustiPy

Patterns · 2026

@article{valdenegro2026robustipy,
  title   = {RobustiPy: An efficient next-generation multiversal library with model
             selection, averaging, resampling, and explainable AI},
  author  = {Valdenegro, Daniel and Yan, Jiani and Dai, Duiyi and Rahal, Charles},
  journal = {Patterns},
  volume  = {7},
  number  = {8},
  pages   = {101609},
  year    = {2026},
  doi     = {10.1016/j.patter.2026.101609}
}

Python package for multiversal analysis with model selection, averaging, resampling, and explainable AI.

GitHub Paper

Digital Gender Gaps

PNAS · 2025

@article{breen2025dgg,
  title   = {Mapping subnational gender gaps in internet and mobile adoption using
             social media data},
  author  = {Breen, Casey F. and Fatehkia, Masoomali and Yan, Jiani and Zhao, Xinyi
             and Leasure, Douglas R. and Weber, Ingmar and Kashyap, Ridhi},
  journal = {Proceedings of the National Academy of Sciences},
  volume  = {122},
  number  = {42},
  pages   = {e2416624122},
  year    = {2025},
  doi     = {10.1073/pnas.2416624122}
}

Mapping subnational gender gaps in internet and mobile adoption using social media advertising data.

Website GitHub Paper
View all projects →

publications

Peer-reviewed publications and scientific software

Journal Articles & Software

  1. Valdenegro, D., Yan, J., Dai, D., & Rahal, C. (2026). RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI. Patterns, 7(8), 101609. DOI
  2. Yan, J. (2026). Revisiting the social determinants of health with explainable AI: a cross-country perspective. American Journal of Epidemiology, 195(3), 681-688. DOI
  3. Yan, J., & Rahal, C. (2025). On the unknowable limits to prediction. Nature Computational Science, 5, 188–190. DOI
  4. Breen, C. F., Fatehkia, M., Yan, J., Zhao, X., Leasure, D. R., Weber, I., & Kashyap, R. (2025). Mapping subnational gender gaps in internet and mobile adoption using social media data. Proceedings of the National Academy of Sciences, 122(42), e2416624122. DOI
  5. Leasure, D.R., Kashyap, R., Rampazzo, F., Dooley, C.A., Elbers, B., Bondarenko, M., Verhagen, M., Frey, A., Yan, J., Akimova, E.T., Fatehkia, M., Trigwell, R., Tatem, A.J., Weber, I., & Mills, M.C. (2023). Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data. Population and Development Review. DOI
  6. Leasure, D.R., Yan, J., Bondarenko, M., Kerr, D., Fatehkia, M., Weber, I., & Kashyap, R. (2023). Digital Gender Gaps Web Application, v1.0.0. Zenodo, GitHub. Zenodo | GitHub
  7. Mills, M., Rahal, C., Brazel, D., Yan, J., & Gieysztor, S. (2020). COVID-19 Vaccine Deployment: Behaviour, ethics, misinformation and policy strategies. London: The Royal Society & The British Academy.

Working Papers

  1. Rahal, C., Yan, J., & Verhagen, M. (2026). On the Dangers of a Single Seed in Applied Scientific Research. In progress.
  2. Yan, J. (2026). Discovering Prevalent Chronic Disease Profiles with Advanced Clustering Methods in UK Biobank. In progress.
  3. Yan, J. (2026). An Item Response Theory Approach to Model Multimorbidity Complexity Trajectory and the Role of Social Determinants in UK Biobank. In progress.
  4. Yan, J., Li, J., Breen, C.F., & Kashyap, R. (2026). Nowcasting global digital gender gaps using social media data. In progress.