Quan (Kyle) Zhang
Economist · Data Scientist
University of Hawaiʻi at Mānoa
Economics · UHERO · Quantitative Health Sciences, John A. Burns School of Medicine
quanz@hawaii.edu · Google Scholar · ORCID · GitHub · LinkedIn · ResearchGate · SSRN
I am an applied economist and data scientist at the University of Hawaiʻi at Mānoa, trained in economics (Quantitative Economics & Econometrics) and in quantitative health sciences at the John A. Burns School of Medicine. I study how illness and aging change work, retirement, and family decisions, using causal inference, applied econometrics, machine learning, and large-scale data engineering on longitudinal microdata such as HRS, CHARLS, and SHARE in R, Python, SQL, and Stata.
I am a Senior Collaborator and peer reviewer for the Global Burden of Disease Study 2023 at the Institute for Health Metrics and Evaluation (IHME), University of Washington, where I have reviewed nine GBD 2023 estimation manuscripts and hold named collaborator-authorship on four currently under review at Lancet-family journals. As a graduate research assistant at UHERO, I support community health and program-evaluation projects in Hawaiʻi, including data collection and descriptive analysis for the NIH- and state-funded Maui Wildfire Exposure Study (MauiWES).
Methods & Tools
Machine learning & data science — gradient boosting (XGBoost), random forests, LASSO/elastic net, causal forests & heterogeneous treatment effects, double/debiased ML (DML), survival models, multiple imputation (MICE), etc.
Causal inference & econometrics — difference-in-differences (DiD, incl. staggered adoption), instrumental variables (IV/2SLS), event-study & panel methods, regression discontinuity (RDD), synthetic control, etc.
Biostatistics & bioinformatics — survival analysis (Cox PH, Kaplan–Meier, AFT), mixed-effects & longitudinal models, biomarker and genotype–phenotype analysis, frailty-index construction, IRT/CFA psychometrics, cost-effectiveness analysis (CEA/DCEA), meta-analysis, etc.
Programming & software — R, Python, SQL, Stata; Git, Bash, LaTeX; high-performance computing (HPC); reproducible data pipelines over CHARLS, HRS, SHARE, NHANES, IPUMS, GBD, etc.
Research Interests
- Health and labor economics: health shocks, work, and retirement
- Economics of aging and the family
- Causal inference and applied econometrics
- Health policy, health equity, and the global burden of disease
- Disaster recovery and community resilience
Education
- PhD in Economics (Quantitative Economics & Econometrics), University of Hawaiʻi at Mānoa, 2023–2027
- MA in Economics (en route to PhD), University of Hawaiʻi at Mānoa, 2026
- MS in Quantitative Health and Clinical Research, John A. Burns School of Medicine, University of Hawaiʻi at Mānoa, 2024–2026
Awards & Honors
- AWARD Network Summer Training Institute, University of California, San Francisco — a highly competitive national selection (22 participants; NIA/NIH-funded), 2026
- Research Stipend, Department of Economics, University of Hawaiʻi at Mānoa, 2026 — Global Burden of Disease, Health Inequalities, and Disaster Resilience
- Burnham Campbell Fellowship Award, Department of Economics, University of Hawaiʻi at Mānoa, 2025 and 2026
- Hung Family Fellowship, University of Hawaiʻi at Mānoa, 2024–25 and 2025–26
- Achievement Award, Department of Economics, University of Hawaiʻi at Mānoa, 2023
Professional Affiliations
- American Economic Association (AEA)
- American Society of Health Economists (ASHEcon)
- International Health Economics Association (iHEA)
- Society for Epidemiologic Research (SER)
- Gerontological Society of America (GSA)
- Hawaii Pacific Gerontological Society (HPGS)
- Hawaii Economic Association (HEA)
- Pi Gamma Mu (International Honor Society in the Social Sciences)
Selected Publications
Zhang, Q., & Fu, Y. (2026). Ensuring trustworthy AI assisted guideline development for clinical practice. npj Digital Medicine, 9(1), 661. DOI Scholar
Zhang, Q. (2026). Do Negative Social Ties Accelerate Aging in Adults, or Does Aging Erode Social Ties? Proceedings of the National Academy of Sciences (PNAS), 123(20), e2608036123. DOI Scholar