Yue Chu

Yue Chu

Global Health, Demography, Epidemiology, Population study, Data Science, Data Engineering

About Yue

I am a population health expert, demographer, and data scientist, with a multidisciplinary background spanning population health, demography, sociology, epidemiology, applied statistics, and clinical medicine.

My current research focuses on applying advanced statistical and demographic methods to address key questions in population health estimation, particularly in resource-limited settings. My work bridges cutting-edge technologies and methodologies — such as AI, large language models, machine learning, bayesian models — with most challenging public health issues in some of the world’s most pressing and underserved areas, ensuring practical impact and real-world relevance.

Background

I am currently a Ph.D. candidate in Sociology at the Ohio State University (OSU). I hold an M.B.B.S. in Preventive Medicine from Fudan University, China, and an M.S.P.H. from the Department of Population, Family and Reproductive Health at the Johns Hopkins Bloomberg School of Public Health. I worked at Global Disease Epidemiology and Control at Department of International Health, and Institute for International Programs, at Johns Hopkins Bloomberg School of Public Health, before starting the Ph.D journey at OSU. I’m also an alumnus of the Nationwide Center for Advanced Customer Insights with Fisher College of Business at OSU, worked as data science intern at Nationwide Mutual Insurance Company.

Research projects

I am deeply involved in a range of research projects, applying statistical and demographic methods to address key questions in population health and global health estimation. My current work focuses on:

  • Leveraging pretrained large language models and multimodal learning to improve cause-of-death classification from verbal autopsies
  • Developing Bayesian models for the indirect estimation of mortality and fertility across detailed age groups

Previous projects I have contributed to include:

  • Estimating cause of death and disease burden at local, regional, and global levels
  • Estimating COVID-19 prevalence using bayesian multilevel model in Ohio, USA
  • Analyzing changes in age at last birth during contemporary fertility declines
  • Quantifying misclassification between stillbirths and neonatal deaths in African countries
  • Investigating the relationship between cause of death and social determinants of health among children
  • Evaluating multiple public health intervention projects using mixed-methods approaches
  • Conducting a systematic review and meta-analysis on patient outcomes

Consultancies

I have provided analytical and technical support to a range of global health projects in collaboration with institutions such as the World Health Organization (WHO), the African Health Research Institute (AHRI), and the Swiss Tropical and Public Health Institute (Swiss TPH), among others.

  • Development of the Reference Death Archive and related Julia modules (WHO, AHRI)
  • Design, monitoring, and implementation of the Probbase Elicitation Project (CDC/OSU)
  • Revision of the WHO 2016 Verbal Autopsy Instrument (WHO)
  • Setup of the ODK system and central server for verbal autopsy data collection and storage in Guinea-Bissau (Ministry of Health, Guinea-Bissau)
  • Evaluation of data quality in global health estimates (WHO)
  • Estimation of cause of death for the 2018 Afghanistan Health Survey (Swiss TPH)

Skills

  • R, Python, Julia, Stata, SQL/SQLite, RJAGS, RNIMBLE.
  • Kubeflow, Snowflake, Teradata, DBeaver, Amazon AWS / S3.

Key words

  • demography, global health, population health, verbal autopsy, mortality, vital statistics, demographic surveillance, survey methodology, applied statistics, Bayesian modeling, data science, computational social science, large language models, machine learning, multimodal learning.

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