At CoDE Lab, we study how computational methods can uncover and address inequities in redistricting, voting, and beyond.
Through modeling, mapping, and interdisciplinary collaboration, we bring quantitative insight to questions of justice and public good.
Ongoing Research with Students:
Studying the connectivity of the ReCom Metagraph, to better understand the technique of outlier analysis in detecting gerrymandering
Investigating the impact of disconnected precincts in constructing ensembles of redistricting maps
Other Ongoing Research:
Constructing Multimember districts and generating ranked-choice ballots (with VoteKit). Studying the impact of the ballot-generation process, as well as the number of seats per district
Completed Products with Students:
USF graduate students Ananya Agarwal, Alusi, Arbie Hsu, Arif Syraj, and I have cleaned and coalesced data for post-2020-Census maps; the maps can be found at https://github.com/eveomett-states
States of Disarray: Cleaning Data for Gerrymandering Analysis with A. Agarwal, Alusi, A. Hsu, and A. Syraj (to be published in The Mathematics Enthusiast)
The Intersectionality Problem for Algorithmic Fairness with J. Himmelreich, A. Hsu, and K. Lum. Proceedings of Machine Learning Research 279 (2024). Accepted Paper at the 2024 NeurIPS Workshop: Algorithmic Fairness through the Lens of Metrics and Evaluation
If you're a USF student interested in this work, please reach out to Ellen.