Teaching
| Year | Class (click for details) | 2013+2014 fall | Biostat 561 |
|---|---|
| Introduction to Computing with R (1): Covered the basics of statistical computing/simulation | |
| 2013+2014 winter | Biostat 562 |
| Introduction to Computing with R (2): Covered the basics of statistical computing/simulation | |
| 2015 winter | Biostat 527 |
| Nonparametric Regression and Classification: This course covers nonparametric methods including kernel estimators, projection estimators, penalized regression, as well as more modern machine-learning-based estimators. The course is taught with a focus on the bias/variance tradeoff. Each method is examined through the lens of that tradeoff (both practically and theoretically) | |
| 2016+2017 fall | Biostat 544 |
| Introduction to Biomedical Datascience: This course covers the entire programmatic analysis pipeline, including data wrangling, visualization and exploration, formal statistical analysis, and automated report generation. Programming is done primarily in R. The focus is on generating and answer useful biomedical questions with data. | |