Cindy Cheng is a data scientist and short-term consultant based in Munich with a PhD in political science and over a decade of experience turning messy, large-scale data into policy-relevant insights. She founded and led the CoronaNet initiative, building one of the most detailed global COVID-19 policy datasets (180,000+ hand-coded observations) and managing a distributed team of 1700+ contributors while producing multiple high-impact publications. Her work spans causal inference and machine learning—she has trained models to classify millions of texts with high F1 scores and designed robust data pipelines and taxonomies to maintain quality under rapidly changing conditions. At the World Bank she applies inferential statistics to fragile contexts and translates analysis into pragmatic policy guidance, and she’s seeking a transition outside academia in Germany. A practical problem-solver, she combines rigorous research design with hands-on data engineering and experience securing diverse international funding.
12 years of coding experience
2 years of employment as a software developer
Bachelor's degree Political Economy, Bachelor's degree Political Economy at University of California, Berkeley
Doctor of Philosophy - PhD Political Science, Doctor of Philosophy - PhD Political Science at Duke University
This is the data repository of the CoronaNet project on government responses to the COVID-19 pandemic and the data/code repository for the paper "A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts".
Contributions:2 releases, 144 commits, 198 pushes in 2 years 10 months
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Cindy Cheng - Short Term Consultant at The World Bank