Technical Training
I have an extensive record of developing and delivering technical education, from one-on-one mentorship to large workshop curricula, across topics including reproducible research, statistical modeling, machine learning, biodiversity data, and R & Python programming. I believe rigorous methods should be accessible to anyone willing to learn.
Courses & Workshops
I have designed and taught in-person workshops across universities, conferences, and research institutes. Materials are openly available for reuse and remixing.
Using Biodiversity and Natural History Museum Databases — Workshop introducing researchers to natural history and biodiversity databases, with hands-on exploratory analysis in R using the Neotoma database. Developed for R-Ladies and adapted for multiple audiences.
Evo Devo Module — A three-hour lecture and wet lab on evolutionary developmental biology using fresh-cut flowers. Designed to be taught at the undergraduate level; delivered at three colleges and universities.
Reproducible Research Version Control (Data Carpentry) — Co-authored and taught this lesson on version control with git as part of the Reproducible Science Curriculum, used across multiple Data Carpentry workshops internationally.
Introduction to Git and GitHub — Co-developed and co-taught with Matthias Bussonnier for the Hacker Within community at UC Berkeley, covering git fundamentals and collaborative workflows.
The Data Science of Shape Using Momocs — Workshop on 2D morphometric analysis using the Momocs R package, taught at R-Ladies Copenhagen.
SOM Clustering Visualization — Companion workshop exploring Self Organizing Maps for clustering using ggplot and the kohonen R package.
Reproducible Science Workshop — Full-day workshop on tools, resources, and practices for reproducible research. Co-written and co-taught on multiple occasions.
MACS2 for ChIP-Seq Data — Genomics lab module built and taught as part of BIS180L at UC Davis, covering peak calling and ChIP-Seq analysis workflows.
Self-Paced Tutorials
Standalone resources designed for independent learners, used in both self-study and embedded in university courses.
Become a Superhero, Handle Your Data with R — A beginner R course written for learners with no programming background. Originally developed for high school students and later adopted in undergraduate and graduate courses across multiple institutions.
Gene Expression Analysis with Self Organizing Maps — A practical tutorial on SOM clustering for gene expression data, emphasizing how to constrain and interpret clustering by experimental variables such as genotype.
Mixed Effect Linear Modeling in R — An accessible introduction to mixed-effect linear modeling using the lme4 R package, co-developed with Dan Chitwood for researchers working with nested and repeated-measures data.
Eisen Lab Coding Club — A documented collection of computational techniques for genomic and 3D image analysis, produced through a recurring knowledge-sharing series I organized and led within Michael Eisen’s lab at UC Berkeley.