Stochastic superparameterization through local data generation - Yoonsang Lee, Department of Mathematics, Dartmouth College
UMass Lowell Applied Mathematics Seminar Series – Fall 2019
September 11, 2019

Abstract: Stochastic superparameterization is a class of multiscale methods that approximate large-scale dynamics of complex dynamical systems such as turbulent flows. Unresolved sub-grid scales are modeled by a cheap but robust stochastic system that mimics the true dynamics of the sub-grid scales, which is crucial to model non-trivial and non-equilibrium dynamics. In this talk, we propose a numerical procedure to estimate the modeling parameters, which avoids the use of climatological data.

Hardness results for sampling connected graph partitions with applications to redistricting - Daryl Deford, MIT
UMass Lowell Applied Mathematics Seminar Series – Fall 2019
September 18, 2019

Abstract: The problem of constructing “fair” political districts and the related problem of detecting intentional gerrymandering has received a significant amount of attention in recent years. A key problem in this area is determining the expected properties of a representative districting plan as a function of the input geographic and demographic data. A natural approach is to generate a comparison ensemble of plans using MCMC and I will present successful applications of this approach in both court cases and legislative reform efforts. However, our recent work has demonstrated that the commonly used boundarynode flip proposal can mix poorly on real-world examples. In this talk, I will present some new proposal distributions for this setting and discuss some related open problems concerning mixing times and spanning trees. I will also discuss some generic hardness results for sampling problems on partitions of planar graphs.