Abstract: In this talk, I will describe a a body of mathematical work trying to quantify the extent to which a redistricting plan is a partisan gerrymander.
This work served as the basis for my court testimony in Common Cause v. Rucho which recently declared the NC 2016 Congressional maps a partisan gerrymander. The Duke Quantifying Gerrymanderig group also produced a report on partisan gerrymandering in Wisconsin which was one of the biases for Eric Lander’s amicus brief in Gill v Whitford. The method turns on generating an ensemble of redistrictings without regard to any (or little) partisan data and then using this ensemble to bench mark what properties a typical redistricting should have. This in turn can be used to determine if a specific redistricting is a statistical outlier. More information and source papers can be found at https://sites.duke.edu/quantifyinggerrymandering/ .
Speaker bio: Jonathan Mattingly grew up in Charlotte, NC where he graduated from the NC School of Science and Mathematics and received a BS is Applied Mathematics with a concentration in physics from Yale University. After two years abroad with a year spent at ENS Lyon studying nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to attend Princeton University where he obtained a PhD in Applied and Computational Mathematics in 1998. After 4 years as a Szego Assistant Professor of Mathematics at Stanford University and a year as a member of the Institute for Advanced Studies in Princeton, he moved to Duke University in 2003. He is currently a Professor of Mathematics and of Statistical Science. He is the recipient of a Sloan Fellowship, a PECASE CAREER award, and is a fellow of the IMS and the AMS.