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TL;DR:
Data scientist ← c(“ecologist”, “evolutionary biologist”)
Broadly, I apply statistical and machine learning methods, linear modeling, and modern workflow practices to genomic and ecological data. Curious about what I’m working on? Explore recent projects and methods on my Research page.
About me
I am a data scientist and bioinformatician that focuses on translating research insights into open, reproducible tools that others can build upon and use to do publishable science.
My scholarship sits at the intersection of statistical modeling, research software engineering, and microbial community ecology. To that end, I develop reproducible analytical pipelines in R and Python to analyze amplicon and whole‑genome sequencing data, uncovering how microbial symbionts interact with their plant hosts, from tropical trees to alpine yellow monkeyflowers. I integrate sequencing outputs with ecological context, focusing on community assembly, host associations, and environmental drivers in high‑dimensional datasets. Most recently, I help build cross‑institutional workflows with containerized environments to standardize microbiome analyses across bioenergy feedstocks. My focus is translating complex datasets into clear, shareable results that others can reproduce, reuse, and build upon.
Open, transparent, and reproducible science guides my work. I prioritize community standards and reproducibility, whether developing R packages for core‑microbiome analysis or contributing to ESIP’s data stewardship initiatives to advance best practices in data management. Additionally, I share practical skills in data visualization, wrangling, and analysis through workshops and tutorials.