Jiani is a computational social scientist whose research lies at the intersection of population health, computational demography, and artificial intelligence. Her work focuses on understanding health inequalities and the social determinants of health, particularly in relation to mortality, morbidity, and multimorbidity in ageing populations. She employs advanced quantitative methods, including machine learning, deep learning, and explainable AI, to uncover complex patterns in population health and investigate how social and demographic factors shape health outcomes across different populations and contexts.

Beyond substantive research on health inequalities, Jiani has a strong interest in the methodological foundations of computational research, particularly model evaluation, predictive uncertainty, robustness, and interpretability. Her broader research also explores the application of computational methods and novel data sources to demographic and social challenges, including digital inequalities and population dynamics. Through her interdisciplinary work, she aims to advance rigorous, transparent, and interpretable data-driven research that can inform public health policies and contribute to reducing health inequalities.

Jiani is 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 these positions, she completed her DPhil in Sociology at the University of Oxford, where she was funded by the Economic and Social Research Council (ESRC) through the Advanced Quantitative Methods pathway. She was also affiliated with the Laboratory of Digital and Computational Demography at the Max Planck Institute for Demographic Research (MPIDR) and the ESRC Centre for Care.