Statistics student · Statistical inference · Reproducible research
GitHub · Featured projects · How I work
I am an undergraduate student in Statistics at Soochow University, expected to graduate in 2027. I am interested in statistical inference, computational statistics, spatial data, and the way careful analysis can connect mathematical reasoning with real-world questions.
Over the next three years, I am building both research depth and practical project-delivery skills. My goal is not simply to make code run, but to make assumptions, diagnostics, limitations, and evidence clear enough for others to examine.
| Project | Focus | My role / public scope |
|---|---|---|
| Suzhou EV Charging User Satisfaction Survey | Market research on cost, service quality, accessibility, and app experience for EV charging | Team project; public methodology and ethics archive |
| Low-Altitude Tourism Demand in the Yangtze River Delta | Consumer demand, safety perception, value, and market segmentation | Project lead; public research-design archive |
| Digital Economy and New Quality Productive Forces | Provincial panel-data research design on digital development and productivity | Competition project record with a reproducibility roadmap |
- Statistical inference and probabilistic modeling
- Computational statistics and reproducible workflows
- Spatial and panel-data analysis
- Survey research, consumer insight, and data-informed decisions
Python · R · MATLAB · Julia · SAS · Statistical Modeling · Data Analysis
- Start with a precise question and an explicit data-generating story.
- Treat model assumptions, diagnostics, and uncertainty as part of the result.
- Separate public project evidence from restricted data and private materials.
- Prefer transparent documentation over impressive-looking but unverifiable claims.
Open to thoughtful discussion about statistics, research practice, and data projects.