I am an Assistant Professor of Data Analytics in the Department of Mathematics and Statistics at Washington State University. Previously, I was a postdoctoral associate at MIT (CSAIL) in the Geometric Data Processing Group, working under Justin Solomon and associated with the Metric Geometry and Gerrymandering Group at MIT and Tufts, working with Moon Duchin and Justin Solomon on the mathematics of redistricting. I earned my Ph.D. in mathematics at Dartmouth College under the supervision of Dan Rockmore in 2018. My research interests tend towards applications of algebraic and combinatorial methods in data analysis.

• Current CV
• Current Projects
• Recent Presentations
• Software and Data

(Google Scholar) (arXiv) (MathSciNet)

Accepted Papers

1. Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming with D. Wu and D. Palmer, SIAM Journal on Optimization (SIOPT), (2022).
2. Random Walks and the Universe of Districting Plans, with M. Duchin, Book Chapter in Political Geography, Birkhauser, (2022).
3. Empirical Sampling of Connected Graph Partitions for Redistricting with L. Najt and J. Solomon, Physical Review E, 104, 064130, (2021).
4. ReCombination: A family of Markov chains for redistricting, with M. Duchin and J. Solomon, Harvard Data Science Review, 3(1), (2021).
5. Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado, with J. Clelland, H. Colgate, B. Malmskog, and F. Sancier-Barbosa, Journal of Computational Social Science, Online First, (2021).
6. Implementing partisan symmetry: Problems and paradoxes, with N. Dhamankar, M. Duchin, V. Gupta, M. McPike, G. Schoenbach, K. W. Sim, Political Analysis, (to appear 2021).
7. Partisan Dislocation: A Precinct-Level Measure of Representation and Gerrymandering, with N. Eubank and J. Rodden, Political Analysis, (to appear 2021).
8. Medial Axis Isoperimetric Profiles, with P. Zhang and J. Solomon, SGP'20 Computer Graphics Forum, 39(5), 1-13, (2020).
9. A Computational Approach to Measuring Vote Elasticity and Competitiveness, with M. Duchin and J. Solomon, Statistics and Public Policy, 7(1), 69-86, (2020).
10. Mathematics of Nested Districts: The Case of Alaska, with S. Caldera, M. Duchin, S. Gutenkust, and C. Nix, Statistics and Public Policy, 7(1), 39-51, (2020).
11. On the Spectrum of Finite Rooted Homogeneous Trees, with D. Rockmore, Linear Algebra and Applications, 598, 165-185, (2020).
12. Aftermath: The Ensemble Approach to Political Redistricting, with J. Clelland and M. Duchin, MAA Math Horizons, 27(3), 34-35, (2020).
13. Total Variation Isoperimetric Profiles, with H. Lavenant, Z. Schutzman, and J. Solomon, SIAM J. Appl. Algebra Geometry, 3(4), 585-613, (2019).
14. Spectral Clustering Methods for Multiplex Networks, with S. Pauls, Physica A, 121949, (2019).
15. Redistricting Reform in Virginia: Districting Criteria in Context, with M. Duchin, Virginia Policy Review, 12(2), 120-146, (2019).
16. A new framework for dynamical models on multiplex networks, with S. Pauls, Journal of Complex Networks, 6(3), 353-381, (2018).
17. Multiplex Dynamics on the World Trade Web, Proc. 6th International Conference on Complex Networks and Applications, Studies in Computational Intelligence, Springer, 1111-1123, (2018).
18. Cyclic Groups with the same Hodge Series, with P. Doyle, Revista de la UMA, 59(2), 241-254, (2018).
19. Random Walk Null Models for Time Series Data, with K. Moore, Entropy, 19(11):615, (2017).
20. Enumerating Tilings of Rectangles by Squares, Journal of Combinatorics, 6(3), 339-351, (2015).
21. Pulsated Fibonacci Sequences, with K. Atanassov and A. Shannon, Fibonacci Quarterly (Conference Proceedings), 52(5), 22-27 (2014).
22. Enumerating Distinct Chessboard Tilings , Fibonacci Quarterly (Conference Proceedings), 52(5), 102-116, (2014).
23. Seating Rearrangements on Arbitrary Graphs, Involve, 7(6), 787-805, (2014).
24. Empirical Analysis of Space-Filling Curves for Scientific Computing Applications, with A. Kalyanaraman, Proceedings of the 42nd International Conference of Parallel Processing, 170-179, (2013).
25. Counting Rearrangements on Generalized Wheel Graphs, Fibonacci Quarterly, 51(3), 259-273, (2013).

Preprints

26. Does the first-serving team have a structural advantage in pickleball?, with S. Ethier, arXiv: 2303.02942, (2023).
27. Stirling Numbers of Uniform Trees and Related Computational Experiments, with A. Barghi, (2023).
28. Multi-Balanced Redistricting, with E. Kimsey and R. Zerr, (2022).
29. Labeled Graph Rearrangements on Matched and Star Products, with A. Barghi, (2022).
30. Ranking Trees Based on Global Centrality Measures, with A. Barghi, (2022).
31. Complexity and Geometry of Sampling Connected Graph Partitions, with L. Najt and J. Solomon, arXiv:1908.08881, (2019).
32. Fourier transforms on SL_2(Z/P^nZ) and related numerical experiments, with B. Breen, J. Linehan, and D. Rockmore, arxiv: 1710.02687, (2017).
33. A Random Dot Product Model for Weighted Networks, with D. Rockmore, arXiv:1611.02530, (2016).

Technical Reports

34. Amicus Brief of Computational Redistricting Experts, with A. Becker and D. Gold, Merrill v. Milligan, United States Supreme Court, (2022).
35. Comparison of Districting Plans for the Virginia House of Delegates, with M. Duchin and J. Solomon, MGGG Technical Report, (2019).
36. Amicus Brief of Mathematicians, Law Professors, and Students, with M. Duchin and G. Charles et al., Rucho v. Common Cause, United States Supreme Court, (2019).
37. Study of Reform Proposals for Chicago City Council, with M. Duchin et al., MGGG Technical Report, (2019).
38. An Application of the Permanent-Determinant Method: Computing the Z-index of Arbitrary Trees, WSU Technical Report Series #2013-2, (2013).

1. Redistricting tools and gerrymandering (S. Whitlock, AAAS SciLine, 2022)
   • I was interviewed, along with with Professor Wendy K. Tam Cho and Professor Maxwell Palmer, about computational tools for evaluating partisan gerrymandering.
2. New districts for 2022 midterms, in part thanks to math (A. Zimmerman, KOAA News, 2022)
   • Brief article and video interview with one of my collaborators Beth Malmskog about our work in Colorado.
3. Pennsylvania Supreme Court relies on Daryl DeFord's Research (T. Wagoner, WSU, 2022)
   • Brief article highlighting the PA Supreme Court's reliance on my analysis in their opinions selecting a Congressional map in 2022.
4. Redistricting process was independent and fair (C. Perez, The Gazette, 2022)
   • Guest opinion piece referencing our contributions to Colorado's redistricting process.
5. Can Math Make Redistricting More Fair? (D. Strain, CU Boulder Today, 2021)
   • News article interviewing a collaborator (Dr. Jeanne Clelland) about our analysis for the Colorado Redistricting Comission.
6. Data Science for Social Good Team Builds Tools to Support Fairness in Computational Redistricting (E. Keller, UW ESciences Institute, 2021)
   • Blog post from the UW EScience Institute about the Vote Redistricting summer program that was a part of the 2021 Data Science for Social Good. I was the faculty lead for this project.
7. Open source tool can help identify gerrymandering in voting maps (S. Zaske, WSU News, 2021)
   • Press release describing the results in ReCombination: A family of Markov chains for redistricting, with M. Duchin and J. Solomon, Harvard Data Science Review, 3(1), (2021).
8. People Who Can't Vote Still Count Politically in America. What if That Changes? (E. Badger, NYT Upshot, 2019)
   • Article on representation that discusses my unpublished analysis of potential impacts of CVAP-based population balancing.
9. The Supreme Court's Math Problem (J. Ellenberg, Slate, 2019)
   • Article about the oral argument in Rucho v. Common Cause that discusses the `mathematician's brief' for which I did the computational work.
10. Hannah Croasdale Award (A. Skinner, Dartmouth Graduate News, 2018)
    • This article from the Dartmouth graduate school describes the research experiences that led to me winning the Hannah Croasdale Award, in 2018. This is a college-wide award awarded annually to the graduating PhD recipient who best exemplifies the qualities of a scholar.
11. Graduate Teaching Award (A. Skinner, Dartmouth Graduate News, 2017)
    • This article from Dartmouth focuses on my teaching experiences as a graduate student, for which I was awarded the 2017 Dartmouth Graduate Teaching Award, which is a college-wide recognition for exemplifying the qualities of a college educator.

1. Expert Report (and rebuttal report) for Pennsylvania (2022)
   • Analysis of proposed Congressional redistricting plans for Pennsylvania on behalf of Citizen Mathematicians and Scientists in litigation before the Commonwealth Court.
2. Expert Report (and Rebuttal Report) for Wisconsin (2021 and 2022)
   • Analysis of proposed Congressional and State Legislative redistricting plans for Wisconsin on behalf of Citizen Mathematicians and Scientists in litigation before the Supreme Court of Wisconsin.
3. Analysis of Prospective Districts in Colorado (2021)
   • Reports comparing Colorado's Staff maps for Legislative and Congressional districts to a large ensemble of randomly generated maps. Uses 2020 precinct data and election data from 2016-2020.
4. Applying GerryChain: A User's Guide for Redistricting Problems (2021)
   • Description of modeling methodology for applying the ensemble method using GerryChain to analyze political redistricting problems. This guide was created by a team of research fellows that I supervised through the 2021 UW Data Science for Social Good program.
5. Mathematics Job Market Notes (2020).
   • Detailed commentary about my experiences applying for academic jobs in mathematics in the US. Also includes links to some resources developed for Math 581.03 at WSU, which is a professional development course for graduating students.
6. Introduction to Discrete MCMC for Redistricting (with Scrabble) (2019).
• A friendly and interactive introduction to discrete MCMC methods, concluding with applications to political redistricting. Many of the motivating examples are explained with Scrabble tiles. Accompanying Sage-interact widgets embedded on a webpage here and also on GitHub.
7. Building Ensembles of Graph Partitions (2019).
• This is a guide to GerryChain that walks through the engineering challenges inherent in generating ensembles of districting plans. Contains numerous examples and code snippets. Frequently updated.
8. Geospatial Data Preparation for GerryChain (2019)
• Beginning to end description of the data preparation process for building an annotated dual graph for GerryChain.
9. Cribbage-Poker Rules (2019).
• A modified ruleset for the game of Cribbage that my wife and I developed. It adds a betting phase and several categories of bonus points to speed up play.
10. Pickleball Notes and Diagrams (2019).
• Some strategy thoughts about the game of pickleball, as well as a collection of diagrams for drills typeset with tikzlings. The .pdf also contains some detailed point breakdowns from a recorded 2016 Atlantic Regionals match. A flask app I maintain for running pickleball leagues and tournaments can be found here, and a widget for computing pairings for 4-24 player rotating partner round robin tournaments is here.
11. Applied Mathematics and Network Science (2018).
• This is a short piece describing my personal philosophy of applied mathematics and addressing the differences between graphs and networks.
12. The Written Qual Book (with D. Freund) (2017).
• This is a 274 page book containing solutions to all of the written qualifying exam problems that were given in the Dartmouth Math Department from 2012-2017. In addition to the solutions, we included 20 pages of expository material on how to survive graduate school and many helpful appendices. There are also 60 pages of notes, commentary, and context to supplement the formal solutions.