I’m a quantitative researcher with a background in public health, social science, and applied statistics. I’ve published as a lead author in multiple well-regarded peer-reviewed journals, and have contributed to many more collaborative projects. Much of my work centers on data wrangling, modeling, reproducible workflows, and helping people move all the way from "what is R" to a publication-ready analysis.

I’ve spent years training others in statistical programming, including undergraduate students, graduate research assistants, and postdoctoral fellows. A lot of people I’ve worked with start with minimal coding experience and end up able to run analyses they could defend in class or in publication. My statistical programming approach follows simple principles: understand the question, understand the data, design a flexible workflow, and then build it as cleanly and transparently as possible.

I can help you with:

• R programming (tidyverse, data cleaning, modeling, visualization)
• Stata workflows and common empirical methods
• General statistical reasoning and model interpretation
• Reproducible analysis (projects, scripts, versioning, literate code)
• Intro and intermediate machine learning with R (tidymodels, xgboost, random forests, keras, iml)
• Refining vague ideas into true research questions into usable analysis plans
• Navigating messy or real-world datasets (surveys, admin data, evaluation projects)

If you want support on coursework, a thesis, a project, or just improving your skills, I’m happy to work with you. Drop me a line, tell me what you’re trying to do, and we'll go from there.