Gregory J. Herschlag, Associate Research Professor

I am interested in studying techniques to understand fairness in redistricting.  I am also interested in computational fluid dynamics and high-performance computing.

Contact Info:

Office Location:	207 Physics
Office Phone:	919-660-2861
Email Address:
Web Page:	https://sites.duke.edu/quantifyinggerrymandering

Education:

Ph.D.

University of North Carolina - Chapel Hill

2013

Keywords:

Computational fluid dynamics • Computational methods in Markov chains • Fairness • Gerrymandering • High performance computing--Research • Lattice Boltzmann methods • Redistricting • Sampling (Statistics)

Recent Publications   (More Publications)

1. Herschlag, G; Lee, S; Vetter, JS; Randles, A, Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm, Ieee Transactions on Parallel and Distributed Systems, vol. 32 no. 10 (October, 2021), pp. 2400-2414 [doi]  [abs]
2. Autry, EA; Carter, D; Herschlag, GJ; Hunter, Z; Mattingly, JC, METROPOLIZED MULTISCALE FOREST RECOMBINATION for REDISTRICTING, Multiscale Modeling & Simulation, vol. 19 no. 4 (January, 2021), pp. 1885-1914, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
3. Herschlag, G; Mattingly, JC; Sachs, M; Wyse, E, Non-reversible Markov chain Monte Carlo for sampling of districting maps (August, 2020)  [abs]
4. Autry, EA; Carter, D; Herschlag, G; Hunter, Z; Mattingly, JC, Multi-Scale Merge-Split Markov Chain Monte Carlo for Redistricting (August, 2020)  [abs]
5. Herschlag, G; Kang, HS; Luo, J; Graves, CV; Bangia, S; Ravier, R; Mattingly, JC, Quantifying Gerrymandering in North Carolina, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 30-38, Informa UK Limited [doi]  [abs]