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

Teaching (Fall 2024):

• MATH 231.01, ALGORITHMIC INTRO PROBABILITY Synopsis

  Gross Hall 318, WF 01:25 PM-02:40 PM

• STA 698.01, CAPSTONE PROJECT Synopsis

  Gross Hall 103, F 10:05 AM-11:20 AM

• IDS 798.01, CAPSTONE PROJECT Synopsis

  Gross Hall 103, F 10:05 AM-11:20 AM

• MATH 230.02, PROBABILITY Synopsis

  Social Sciences 119, WF 10:05 AM-11:20 AM

  (also cross-listed as STA 230.02)

• MATH 730.02, PROBABILITY Synopsis

  Social Sciences 119, WF 10:05 AM-11:20 AM

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. Autry, E; Carter, D; Herschlag, GJ; Hunter, Z; Mattingly, JC, METROPOLIZED FOREST RECOMBINATION FOR MONTE CARLO SAMPLING OF GRAPH PARTITIONS, SIAM Journal on Applied Mathematics, vol. 83 no. 4 (August, 2023), pp. 1366-1391 [doi]  [abs]
2. Zhao, Z; Hettle, C; Gupta, S; Mattingly, JC; Randall, D; Herschlag, GJ, Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan, ACM International Conference Proceeding Series (October, 2022), ISBN 9781450394772 [doi]  [abs]
3. 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]
4. Autry, EA; Carter, D; Herschlag, GJ; Hunter, Z; Mattingly, JC, METROPOLIZED MULTISCALE FOREST RECOMBINATION for REDISTRICTING, Multiscale Modeling and Simulation, vol. 19 no. 4 (January, 2021), pp. 1885-1914, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
5. Herschlag, G; Mattingly, JC; Sachs, M; Wyse, E, Non-reversible Markov chain Monte Carlo for sampling of districting maps (August, 2020)  [abs]