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Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Paper Title Number 4

Published in GitHub Journal of Bugs, 2024

This paper is about fixing template issue #693.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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talks

teaching

Modern Statistical Inference

Graduate course, Zhejiang University, Center for Data Science, 2026

This course develops a unified inference toolkit for real-world data where the data-collection mechanism is nontrivial: survey sampling, missing data, and causal inference. A recurring theme is that valid inference requires explicit reasoning about the mechanism—sampling design, nonresponse/missingness, or treatment assignment—and that modern methods (IPW, doubly robust estimators, semiparametric efficiency, ML-assisted inference) can be understood within a common framework. To motivate these ideas, we take a historical perspective, tracing how classical problems and early debates in statistics led to today’s emphasis on valid inference, efficiency, and robustness, and using these milestones to cultivate statistical thinking.