I am a population health expert, demographer, and data scientist working at the intersection of global health, demographic estimation, and applied AI.
My research examines how advanced statistical, demographic, and AI-driven methods can help close persistent data and evidence gaps in low- and middle-income countries and other resource-limited settings.
Across work on mortality, fertility, disease burden, data quality, evaluation, and research infrastructure, I focus on three priorities:
- Reliable population health estimates More accurate, interpretable, and decision-ready estimates of mortality, fertility, disease burden, and health disparities.
- Stronger data systems Improving the quality and usability of survey and surveillance data, and examining how social determinants, data gaps, and measurement choices shape the evidence.
- Research translated into practice Validation studies, evaluation frameworks, and practical data tools that turn research and measurement into usable evidence and public health action in underserved populations.
Background
I am a Postdoctoral Fellow in the Health and Environment Modeling Co-Laboratory at The Ohio State University. My training spans clinical medicine, population health, demography, sociology, epidemiology, and applied statistics. I hold a PhD in Sociology with a minor in Statistics from The Ohio State University and an MSPH in Population, Family and Reproductive Health from the Johns Hopkins Bloomberg School of Public Health, with earlier clinical training that grounds my work in the practical realities of health systems and patient care. Across graduate training and collaborative research, I have worked with WHO, AHRI, Swiss TPH, and other public health partners on projects that connect methodological research with real-world population health measurement, evidence generation, and decision-making in resource-limited settings.
Selected Research Areas
AI in Global Health
I study how language models and multimodal machine learning can strengthen health measurement in low-resource settings. This work focuses on verbal autopsy analysis, where narrative text and structured symptom data can be combined to improve cause-of-death assignment and make health estimates more timely and scalable.
Disease Surveillance and Global Epidemiology
I use surveillance, survey, and modeled mortality data to study how health risks and causes of death change across place and time. The goal is to produce population-level evidence that is locally grounded enough to guide policy, while still allowing meaningful cross-country comparison.
Demographic Methodology
I build demographic estimation methods for settings where data are incomplete, sparse, or uneven across age groups. Much of this work uses Bayesian modeling to recover age-specific mortality and fertility schedules with transparent uncertainty, so estimates remain useful for planning and evaluation.
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Selected Papers
2025
Leveraging language models and machine learning in verbal autopsy analysis
A methodological paper on using language models and machine learning for verbal autopsy analysis and cause-of-death classification.
2024
Temporal changes in cause of death among adolescents and adults in six countries in eastern and southern Africa in 1995-2019
A multi-country surveillance study using verbal autopsy data to examine long-run changes in cause-specific mortality across eastern and southern Africa.
2021
Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests
A Bayesian framework for statewide COVID-19 seroprevalence estimation under low positivity, imperfect testing, and survey nonresponse.
Contact
- University email: chu.282@osu.edu
- Personal email: ychu612@gmail.com