J Yan

about me

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

I am a computational social scientist working at the intersection of population health, computational demography, and scientometrics. My research focuses on understanding health inequalities and the social determinants of health, particularly mortality, morbidity, and multimorbidity in ageing populations. I employ advanced quantitative methods, including machine learning, deep learning, and explainable AI, to investigate complex patterns in population health.

My research also extends to scientometrics, where I examine trends in scientific research, the use of large-scale biomedical datasets, and diversity and representation in genetic research. Alongside these substantive interests, I am interested in methodological robustness, model uncertainty, and the evaluation and interpretability of predictive models.

I am currently a Researcher in Data Science and Informatics at the Nuffield Department of Population Health, University of Oxford, and a Non-Stipendiary Research Fellow at Nuffield College, Oxford. Prior to my current positions, I pursued my DPhil in Computational Social Science at the University of Oxford, supervised by Professor Charles Rahal and Professor Ridhi Kashyap. My doctoral research was supported by the Economic and Social Research Council (ESRC) through the Advanced Quantitative Methods pathway.

Research Interests: Computational Social Science · Health Inequalities · Scientometrics · Explainable AI · Model Uncertainty & Evaluation

location

Oxford, UK

email

jiani.yan@demography.ox.ac.uk

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

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publications

Peer-reviewed publications and scientific software

Journal Articles & Software

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
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
Yan, J., & Rahal, C. (2025). On the unknowable limits to prediction. Nature Computational Science, 5, 188–190. DOI
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
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
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
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

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

Hi , I am J Yan .