I develop statistical and causal discovery methods for assessing quantitative fairness for human decision making, such as racial bias in peer review and ballot order bias in election administration. In addition, I have worked in computational algebraic geometry (see publications sidebar), MCMC sampling algorithms (undergraduate thesis), machine learning in genomics (internship at Microsoft Research), and medicine (UW statistical consulting program).


In Progress

I have a first-author paper on information in peer review scoring under review at the British Journal of Mathematical and Statistical Psychology, and aim to submit my final dissertation chapter (on Bayesian causal discovery) to a journal in the 2nd half of 2021.

Software and Data

If you're looking for code samples:

  • For a reproducible data analysis script, see reproduce_Erosheva-et-al.Rmd from the NIH project's OSF repository (below).
  • For production-type code, see targetedordertest, a basic R package implementing the targeted tests for uniform randomness of orderings from Grant et al. (2020).

Open Science Foundation repository for NIH peer review data and Erosheva et al. (2020) reproduction code