Citizen science databases are increasing in importance as sources of ecological information, but variability in location and effort is inherent to such data. Spatially biased data—data not sampled uniformly across the study region—is expected. A further introduction of bias is the variable level of sampling activity, such as duration, across locations. This motivates our work: with a spatial dataset of visited locations and activity levels, we propose a formal, model-based approach for assessing effort at these locations. Adjusting for potential spatial bias both in terms of sites visited and in terms of effort is crucial for creating reliable species distribution models (SDMs). Using data from eBird, a global citizen science database dedicated to avifauna, and illustrative regions in Pennsylvania and Germany, we model spatial dependence in both the observation locations and observed activity. We apply point process models to explain the observed locations in space, fit a geostatistical model to explain observation effort at locations, and explore the potential existence of preferential sampling, i.e., dependence between the two processes. Altogether, we offer a more holistic notion of sampling effort, combining information about location and activity.

Assisted Professor Steve Wang with developing bias-corrected and accelerated boostrap confidence intervals for fossil data, with an additional randomization component. Abstract was presented at the Geological Society of America Conference in October 2017.

Assisted Professor Kelly McConville with developing an R package to implement various model-assisted surey estimators. Estimators include the Horvtiz-Thompson, ratio, and post-stratification.

CRAN website:

• https://cran.r-project.org/web/packages/mase/index.html
  

Teaching a ten-week course introducing students from various disciplines to data science and statistical thinking. The goal is to help students gain experience in data wrangling, exploratory data analysis, predictive modeling, data visualization, and effectively communicating results. The courses introduces and focuses on the R statistical computing language.

Course website:

• https://sta199-summer2021.github.io/website/
  

The primary goal of this project is to provide objective outcomes analysis for the CIT Collaborative, Durham County Detention Facility and Criminal Justice Resource Center. Team members will examine data provided by the Durham County Detention Facility and merged with health information through Duke’s Analytics Center for Excellence. As project manager, I maintain the effective functioning of the undergraduate team, and mentor the students as they conduct the data analyses.

In addition to serving as a Teaching Assistant, I along with another graduate student taught two lectures about text analysis as part of Duke University’s Data Expedition program.

• Sta 199: text analysis
• Sta 440: text analysis
  

Taught a six-week course introducing students to the discipline of statistics.

Course website:

• https://sites.duke.edu/sta101_01l_2ss2019/