Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Download Paper | Download Slides
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Download Paper | Download Slides
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
Download Paper | Download Slides
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).
Download Paper
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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.