Recent Posts

This is my third attempt at building a website, including an (overly?) ambitious idea to document all of the #Rcats and #Rdogs (and #Rchickens Lucy!) on twitter. After two false starts caused by a combination of teaching responsibilities, making time to snuggle my doggos, and some general anxiety, I think this time is my proverbial charm. First a shoutout to the excellent tutorials by Yihui Xie, Amber Thomas, and Alison Presmanes Hill.



Science Fairs

Outreach at various science fairs

R-Ladies Los Angeles Founder and Organizer

A global network of meetups to encourage and promote gender inclusivity in the R community.

Selected Publications

Complex population processes may require equally complex models, which can lead to analytically intractable estimation problems. Approximate Bayesian computation (ABC) is a computational tool for parameter estimation in situations where likelihoods cannot be computed. Instead of using likelihoods, ABC methods quantify the similarities between an observed data set and repeated simulations from a model. A practical obstacle to implementing an ABC algorithm is selecting summary statistics and distance metrics that accurately capture the main features of the data. We demonstrate the application of a sequential Monte Carlo ABC sampler (ABC SMC) to parameter estimation of a general stochastic stage‐structured population model with ongoing reproduction and heterogeneity in development and mortality. Individual variation in demographic traits has considerable consequences for population dynamics in many systems, but including it in a population model by explicitly allowing stage durations to follow a realistic distribution creates a complex model. We applied the ABC SMC to fit the model to a simulated representative data set with known underlying parameters to evaluate the performance of the algorithm. We also introduced a systematic method for selecting summary statistics and distance metrics, using simulated data and receiver operating characteristic (ROC) curves from classification theory. Evaluations suggest that the approach is promising for model inference in our example of realistic stage‐structured population models.
In Ecology, 2014

Recent & Upcoming Talks


At UCLA I taught Mathematics for Life Scientists in the Winter quarter of 2018.