Public Health and Health Professions
Lin Lab
Led by Tuo Lin · Biostatistics
What we study
The Lin Lab develops statistical methods for analyzing complex data, including cancer studies and clinical trials. They focus on semiparametric theory, missing data, and network models.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Papers
- 5
Recent papers, explained
- Changing internal medicine residents’ attitudes towards managing people with obesity using an interactive educational session with lecture and embedded real-life patients’ experiences
2026 · Obesity Research & Clinical Practice
Taught medical students about obesity care using a mix of lectures and real patient stories.
- Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta‐Diversity
2026 · Statistics in Medicine
Developed a new method to study how gut bacteria change over time in people with missing data.
Show 3 more papersShow fewer
- A doubly robust estimator for the Mann Whitney Wilcoxon rank sum test when applied for causal inference in observational studies
2024 · Journal of Applied Statistics
Tests a method to fix the Mann-Whitney test for causal inference in observational studies.
- On Frequency and Probability Weights: An In‐Depth Look at Duelling Weights
2024 · International Statistical Review
Explained the differences between two types of weights used in statistics to avoid mistakes.
- Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models
2023 · Scientific Reports
Uses machine learning to find best wastewater test results for COVID-19 notifications.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Tuo Lin hasn't set up this page yet. You can also look them up in the UF directory (search “Tuo Lin”).