Research

I use math, statistics, and data science, specifically MCMC sampling and network theory, to study how people interact in social and political systems. My main focus is political redistricting: developing new sampling algorithms for graph partitions and using them to answer applied questions about representation and voting rights.

• The Marked Edge Walk: A Novel MCMC Algorithm for Sampling of Graph Partitions
  Atticus McWhorter and Daryl DeFord — accepted, Journal of Data Science — arXiv:2510.17714


• Free Elections in the Free State: Ensemble Analysis of Redistricting in New Hampshire
  Atticus McWhorter and Daryl DeFord — Journal of Computational Social Science — link.springer.com/article/10.1007/s42001-026-00466-3


• Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy
  Atticus McWhorter, Caroline Hammond, Nianqiao Phyllis Ju, and Daryl DeFord — under review, special issue of La Matematica, Mathematical and Computational Democracy — arXiv:2606.14453


• Reinforcement Learning Dynamics of Network Vaccination and Hysteresis: A Double-Edged Sword for Addressing Vaccine Hesitancy
  Atticus McWhorter and Feng Fu — arXiv:2504.07254


• Heterogeneous Preferences and Personality in Adaptive Network Models
  Olivia J. Chu, Atticus W. McWhorter, and Louis Fan — manuscript available soon

Talks

• Optimization of Graph Partitions for Redistricting via the Marked Edge Walk (invited)
  2026 INFORMS Annual Meeting: Combinatorial Optimization and Political Redistricting


• The Marked Edge Walk (invited)
  2026 Joint Mathematics Meetings, AMS Special Session on The Mathematics of Elections and Redistricting


• Free Elections in the Free State (invited)
  2024 SIAM Annual Meeting, Mathematical and Computational Redistricting: Algorithms and Analysis


• Q-Learning Dynamics in Vaccination (contributed)
  Seventh Northeast Regional Conference on Complex Systems


• Research Computing Methods in Python and Julia (contributed)
  Fu Lab talk series