Zehang Richard Li

Assistant Professor
Department of Statistics
University of California Santa Cruz

Email: lizehang (at) ucsc.edu
Google Scholar: Zehang Richard Li
Github: @richardli
Twitter: @z_richard_li

Upcoming events

9/2024 GEOMED, Hasselt
8/2024 JSM, Portland
7/2024 ECOSTAT, Beijing
7/2024 ISBA, Venice
6/2024 ICSA, Nashville
6/2024 WNAR, Fort Collins

about     publications     people     teaching     software     vita     misc    

I am an Assistant Professor in the Department of Statistics at University of California Santa Cruz. I am broadly interested in statistical methods and tools to address scientific questions in demography, epidemiology, and global health. Currently I work on latent variable modeling in messy, high-dimensional data, space-time models, causal inference, and applications in health data science.

I was previously a postdoctoral researcher working with Forrest Crawford in the Department of Biostatistics at Yale School of Public Health. I received my Ph.D. from the Department of Statistics at the University of Washington, advised by Tyler McCormick.

My research is generously supported by the National Institutes of Health (NIH R03HD110962), Melinda and Bill Gates Foundation, Vital Strategies, the Hellman Fellows Program, and UCSC.

Some Current Projects
Domain adaption: Understanding data shift and developing robust predictive methods using data collected in different environments.
AOAS'24, Biost'24
Novel data collection: Active data collection, prior elicitation, and integrating data collection process into analysis pipeline.
Verbal Autopsy: Probabilistic frameworks to infer cause-of-death assignments and population distribution using noisy high-dimensional data.
BA'20, AOAS'20, JASA'16
openVA: Open-sourced tools for a standardized pipeline to process and code verbal autopsy data using multiple algorithms.
RJournal'23, arXiv'18
Small-Area Estimation: Prevalence estimation in small domains using data collected by complex surveys.
arXiv'21, DHS'21, Vignette'20, PLoS'19,
COVID-19: Model-based projections; prevalence estimation; and assessing the impact on population mortality.
AOE'22, SciReports'21, PNAS'21