I’m a research software engineer and data science educator at the University of Arizona with a background in chemical ecology, plant-insect interactions, and plant population ecology. My Ph.D. focus was on the effects of climate change and insect herbivory on the quality (flavor and health benefits) of tea (Camellia sinensis).


  • Plant ecology
  • Statistics and programming in R
  • Reproducible research practices
  • Tea


  • PhD in Biology, 2020

    Tufts University

  • MS in Ecology, Evolution, and Conservation Biology, 2010

    University of Illinois at Urbana-Champaign

  • BA in Biology, 2006

    Whitman College


Effects of drought and habitat fragmentation on a tropical understory plant.

My postdoctoral project in the Bruna lab at University of Florida involves analyzing long-term demographic data from an experimentally fragmented tropical forest experiment in Brazil.


An R package for accessing chemical information from the web.

Use of partial least squares regression (PLS) in ecology

PLS is a powerful multivariate regression method that has many applications for ecological data. When is it best used, what are its advantages, and how should you report your results?


An R package for modeling bumblebee colony growth

Climate Effects on Bug-Bitten Tea

Eastern Beauty wulong tea is only produced from tea leaves damaged by leafhoppers. The induced volatiles produced by damaged tea plants gives the finished tea a unique flavor. How will leafhopper damage change in a warming climate, and how will that impact tea quality?


I’ve been teaching biology ever since I was 12 when I became an interpretive guide at the Lindsay Wildlife Experience in my home town of Walnut Creek, CA. Since then I’ve taught science of all sorts to all ages in a variety of formats.

Selected Teaching Experience:

  • Ecological Statistics and Data. Spring 2020. I took over this course during my last semester as a PhD student as the instructor of record. I reorganized the syllabus and created new content to teach genearlized linear models and mixed effects models with ecological applications using R.
  • Biostatstics in R. Fall 2016–2018. Recitation section for BIO132 at Tufts University. In collaboration with Natalie Kerr in 2016 and Avalon Owens in 2018, we designed and taught this new course. Previously, BIO132 used SPSS rather than R and did not have a required recitation. Course materials available here.
  • Organisms and Populations. Spring 2015. Lab section for BIO0014 at Tufts University. I served as a graduate TA for a lab section of this course in a year when the course was being entirely redesigned. I actively participated in designing course materials and lab activities in addition to teaching my own lab section.
  • Introductory Biology. 2011 – 2014. I taught a guaranteed transfer credit lecture and lab course at Front Range Community College in Fort Collins, CO where I was an instructor. My students came from diverse age, socioeconomic, and learning backgrounds. I designed lectures and assessment materials and engaged in revising lab exercises and course materials. I attended professional development workshops including an especially helpful one on teaching veterans.

Recent Posts

New job: Scientific Programmer & Educator

My postdoc with the Bruna lab coming to an end in June and I’m excited to announce my new position as a Scientific Programmer & Educator with University of Arizona! I thought I’d use this post to explain a little about what I’ll be doing and the path I took to get here in case it’s helpful for grad students or postdocs exploring career options.

Assessing the reliability of an R package

In the most recent rOpenSci community call, Juliane Manitz presented on standards for statistical software in the pharmaceutical industry. One of her slides in particular was about how the pharmaceutical industry assesses the reliability of a package.

Fitting a DLNM is simple without the {dlnm} package

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. Since I’ve been working so much with GAMs for this project, I decided to read sections of Simon Wood’s book, Generalized additive models: an introduction with R more thoroughly.

DLNMs: hypothesis tests and p-values

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. A major goal of my postdoc project is to determine whether drought has an effect on plant vital rates (growth, survival, reproduction, recruitment).

Speeding up DLNMs with bam()

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. DLNMs themselves may not be that computationally expensive, but when combined with random effects and other smoothers, and a large-ish dataset, I’ve noticed gam() being painfully slow.