Mentor: Dr. Lior Fishman

Title: Using Mathematical Modeling to Create Non-Gerrymandered Congressional Voting Maps

Abstract: Gerrymandering is one of the biggest problems in American democracy. Political parties have, in the past few decades, used redistricting as a method of maintaining political control. Often, they create safe districts and expand their reach far beyond the will of the voters. A mathematical solution to political gerrymandering could help create a more vibrant and fair democracy. Without any settled solution to political gerrymandering, our democracy continues to be unrepresentative. In this project, we create three algorithms - Ideal Partition, Circular Engulfment, and LRPG - that use mathematical methods to create non-gerrymandered congressional maps.