Full Bibliography

This list is the same data as the bibliography table, exported from Zotero and rendered in an APA-flavored style.

Journal articles

  1. Morris, L., Baas, A., Arias, J., Gatlin, M., Patterson, E., & Fairbanks, J. P. (2024). Decapodes: A diagrammatic tool for representing, composing, and computing spatialized partial differential equations. Journal of Computational Science, 81, 102345. https://doi.org/10.1016/j.jocs.2024.102345
  2. Aduddell, R., Fairbanks, J., Kumar, A., Ocal, P. S., Patterson, E., & Shapiro, B. T. (2024). A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks. Compositionality, 6, 2. https://doi.org/10.32408/compositionality-6-2
  3. Brown, K., Patterson, E., Hanks, T., & Fairbanks, J. (2023). Computational category-theoretic rewriting. Journal of Logical and Algebraic Methods in Programming, 134, 100888. https://doi.org/10.1016/j.jlamp.2023.100888
  4. Garrett, R. K., Fairbanks, J. P., Loper, M. L., & Moreland, J. D. (2023). The application of applied category theory to quantify mission success. Simulation, 99(2), 201–220. https://doi.org/10.1177/00375497221114861
  5. Patterson, E., Baas, A., Hosgood, T., & Fairbanks, J. (2023). A diagrammatic view of differential equations in physics. Mathematics in Engineering, 5(2), 1–59. https://doi.org/10.3934/mine.2023036
  6. Patterson, E., Lynch, O., & Fairbanks, J. (2022). Categorical Data Structures for Technical Computing. Compositionality, Volume 4 (2022). https://doi.org/10.32408/compositionality-4-5
  7. Libkind, S., Baas, A., Halter, M., Patterson, E., & Fairbanks, J. P. (2022). An algebraic framework for structured epidemic modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233), 20210309. https://doi.org/10.1098/rsta.2021.0309
  8. Mordecai, Y., Fairbanks, J. P., & Crawley, E. F. (2021). Category-theoretic formulation of the model-based systems architecting cognitive-computational cycle. Applied Sciences, 11(4), 1945.
  9. Briscoe, E., & Fairbanks, J. (2020). Artificial scientific intelligence and its impact on national security and foreign policy. Orbis, 64(4), 544–554.
  10. Fairbanks, J. P., Bader, D. A., & Sanders, G. D. (2017). Spectral partitioning with blends of eigenvectors. Journal of Complex Networks, 5(4), 551–580.
  11. Fairbanks, J. P., Kannan, R., Park, H., & Bader, D. A. (2015). Behavioral clusters in dynamic graphs. Parallel Computing, 47, 38–50.
  12. Fairbanks, J. (2011). A Ramsey theorem for indecomposable matchings. arXiv:1110.3314.

Conference proceedings

  1. Hanks, T., Nino, C., Barcelo, J. B., Copeland, A., Dixon, W., & Fairbanks, J. (2026). Heterogeneous Multi-Agent Multi-Target Tracking using Cellular Sheaves. In European Control Conference. IEEE. (to appear).
  2. Zhao, Y., Hanks, T., Riess, H., Cohen, S., Hale, M., & Fairbanks, J. (2026). Asynchronous Nonlinear Sheaf Diffusion for Multi-Agent Coordination. In IEEE American Control Conference. IEEE. (accepted).
  3. Currier, K., Leal, W., Rauta, G., Copeland, A., Dixon, W., & Fairbanks, J. (2026). Whitney Control Barrier Functions: A Mesh-based Geometric approach via Discrete Exterior Calculus. In IFAC. (in press).
  4. Hanks, T., Riess, H., Cohen, S., Gross, T., Hale, M., & Fairbanks, J. (2025). Distributed Multi-agent Coordination over Cellular Sheaves. In IEEE Conference on Decision and Control. IEEE. https://doi.org/10.48550/arXiv.2504.02049

    Coordination of a multi-agent system can be posed as a cellular sheaf over the communication graph, making the coordinated state the harmonic extension of the sheaf Laplacian rather than the output of a purpose-built controller: the coordinated state solves $H q^\star = -B p$, where $H$ is the agent block of the Laplacian and $p$ the current targets. It is the problem statement the rest of our coordination work refines: every later paper either solves that linear system faster or relaxes an assumption it makes.

    Read it for The sheaf construction and the Laplacian. The experiments can wait.

  5. Lary, M., Samuelson, R., Wilentz, A., Zare, A., Klawonn, M., & Fairbanks, J. (2025). Learning diagrams: a graphical language for compositional training regimes. In The thirteenth international conference on learning representations. https://openreview.net/forum?id=dqyuCsBvn9
  6. Hanks, T., She, B., Hale, M., Patterson, E., Klawonn, M., & Fairbanks, J. (2024). Modeling Model Predictive Control: A Category Theoretic Framework for Multistage Control Problems. In 2024 American Control Conference (ACC) (pp. 4850-4857). IEEE. https://doi.org/10.23919/ACC60939.2024.10644848
  7. Bumpus, B. M., Fairbanks, J., Genovese, F., Puca, C., & Rosiak, D. (2024). How nice is this functor? Two squares and some homology go a long way. Proceedings of Applied Category Theory, 2024.
  8. Lynch, O., Brown, K., Fairbanks, J., & Patterson, E. (2024). GATlab: Modeling and Programming with Generalized Algebraic Theories. In Electronic Notes in Theoretical Informatics and Computer Science. Episciences. org. https://oxford24.github.io/assets/mfps-papers/MFPS24-11.pdf
  9. She, B., Hanks, T., Fairbanks, J., & Hale, M. (2023). Characterizing Compositionality of LQR from the Categorical Perspective. In 2023 62nd IEEE Conference on Decision and Control (CDC) (pp. 1680-1685). https://doi.org/10.1109/CDC49753.2023.10383467
  10. Aguinaldo, A., Patterson, E., Fairbanks, J., Regli, W., & Ruiz, J. (2023). A Categorical Representation Language and Computational System for Knowledge-Based Robotic Task Planning [Best Paper Award]. In Proceedings of the AAAI Symposium Series (pp. 491-497). https://doi.org/10.1609/aaaiss.v2i1.27718
  11. Libkind, S., Baas, A., Patterson, E., & Fairbanks, J. (2022). Operadic Modeling of Dynamical Systems: Mathematics and Computation. Electronic Proceedings in Theoretical Computer Science, 372, 192-206. https://doi.org/10.4204/EPTCS.372.14
  12. Brown, K., Patterson, E., Hanks, T., & Fairbanks, J. (2022). Computational Category-Theoretic Rewriting [Best Paper]. In Graph Transformation: 15th International Conference, ICGT 2022, Held as Part of STAF 2022, Nantes, France, July 7–8, 2022, Proceedings (pp. 155–172). Springer-Verlag. https://doi.org/10.1007/978-3-031-09843-7_9
  13. Brown, K., Hanks, T., & Fairbanks, J. (2022). Compositional Exploration of Combinatorial Scientific Models. In Applied Category Theory. https://doi.org/10.48550/ARXIV.2206.08755
  14. Halter, M., Herlihy, C., & Fairbanks, J. (2020). A Compositional Framework for Scientific Model Augmentation. In Electronic Proceedings in Theoretical Computer Science (pp. 172-182). Opn Publishing Association. https://doi.org/10.4204/EPTCS.323.12
  15. Fairbanks, J. P., Fitch, N., Bradfield, F., & Briscoe, E. (2020). Credibility Development with Knowledge Graphs. In Lecture Notes in Computer Science (pp. 33-47). Springer International Publishing. https://doi.org/10.1007/978-3-030-39627-5_4
  16. Cao, K., & Fairbanks, J. (2019). Unsupervised Construction of Knowledge Graphs From Text and Code. In SIGKDD Conference on Knowledge Discovery and Data Mining International Workshop on Mining and Learning with Graphs. ACM.
  17. Nadolski, M., & Fairbanks, J. (2019). Complex systems analysis of hybrid warfare. Procedia Computer Science, 153, 210-217. https://doi.org/10.1016/j.procs.2019.05.072
  18. Campbell, N., Goodyear, T., Messer, W., Stuart, E., & Fairbanks, J. (2018). Digital Witness: Remote Method for Volunteering Digital Evidence on Mobile Devices. In 2018 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-5). IEEE. https://doi.org/10.1109/THS.2018.8574119
  19. Thankachan, R. V., Swenson, B. P., & Fairbanks, J. P. (2018). Performance Effects of Dynamic Graph Data Structures in Community Detection Algorithms. In 2018 IEEE High Performance extreme Computing Conference (HPEC) (pp. 1-7). IEEE. https://doi.org/10.1109/HPEC.2018.8547528
  20. Fairbanks, J. P., Fitch, N., Knauf, N., & Briscoe, E. (2018). Credibility Assessment in the News: Do We Need to Read? In WSDM/MIS2 (pp. 8). ACM. https://dl.acm.org/doi/10.1145/3159652.3160597
  21. Nathan, E., Fairbanks, J., & Bader, D. (2018). Ranking in Dynamic Graphs Using Exponential Centrality. In Complex Networks & Their Applications VI (pp. 378-389). Springer International Publishing. https://doi.org/10.1007/978-3-319-72150-7_31
  22. Thankachan, R. V., Hein, E. R., Swenson, B. P., & Fairbanks, J. P. (2017). Integrating productivity-oriented programming languages with high-performance data structures. In 2017 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-8). IEEE. https://doi.org/10.1109/HPEC.2017.8091068
  23. Ediger, D., & Fairbanks, J. P. (2017). Deriving Streaming Graph Algorithms from Static Definitions. In 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 637-642). IEEE. https://doi.org/10.1109/IPDPSW.2017.146
  24. Nathan, E., Sanders, G., Fairbanks, J., Henson, V. E., & Bader, D. A. (2017). Graph Ranking Guarantees for Numerical Approximations to Katz Centrality. Procedia Computer Science, 108, 68-78. https://doi.org/10.1016/j.procs.2017.05.021
  25. Fairbanks, J. P., Zakrzewska, A., & Bader, D. A. (2016). New stopping criteria for spectral partitioning. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 25-32). IEEE. https://doi.org/10.1109/ASONAM.2016.7752209
  26. Zakrzewska, A., Nathan, E., Fairbanks, J., & Bader, D. A. (2016). A local measure of community change in dynamic graphs. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 349-353). IEEE. https://doi.org/10.1109/ASONAM.2016.7752257
  27. Fairbanks, J., Ediger, D., McColl, R., Bader, D. A., & Gilbert, E. (2013). A statistical framework for streaming graph analysis. In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) (pp. 341-347). https://doi.org/10.1145/2492517.2492620

Posters

  1. Perez, J., Baas, A., Ferrall-Fairbanks, M. C., Platt, M. O., & Fairbanks, J. P. (2021). Parameter estimation by minimizing the loss with respect to a finite difference approximation on the vector field. Biomedical Engineering Society Annial Meeting, Orlando, FL.
  2. Lynch, O., Fairbanks, J. P., & Evan, P. (2021). Graphical semantic modeling with semagrams.jl. Applied Category Theory, Cambridge, UK.
  3. Fairbanks, J. P. (2019). Semantic model understanding for scientific model augmentation. Systems Biology of Human Disease,, Berlin, DE.
  4. Fairbanks, J. P. (2017). QueryGarden: growing healthy applications in well prepared SQL. OHDSI Symposium, New York, NY.
  5. Brown, C. S., Duke, J., Fairbanks, J. P., Herlihy, C., Mukadam, K., Poovey, J., & Rost, M. (2017). Implementing real-time patient level predictions using PLP models. OHDSI Symposium.
  6. Fairbanks, J. P. (2015). Discovering block structure with approximate eigenvectors. SIAM Computational Science and Engineering.
  7. Fairbanks, J., & Sanders, G. (2015). Discovering block structure in graphs with approximate eigenvectors [Poster]. SIAM Computational Science and Engineering, Salt Lake City, UT. https://jpfairbanks.com/doc/siam-cse-2015.pdf
  8. Fairbanks, J. P. (2012). Ramsey theorem for indecomposable matchings. Graph Theory at Georgia Tech (GT@GT), Atlanta, GA.

Preprints

  1. Bumpus, B. M., Fairbanks, J., & Turner, W. J. (2024). Pushing Tree Decompositions Forward Along Graph Homomorphisms. arXiv. https://doi.org/10.48550/arXiv.2408.15184
  2. Hanks, T., Klawonn, M., Patterson, E., Hale, M., & Fairbanks, J. (2024). A Compositional Framework for First-Order Optimization. arXiv. https://doi.org/10.48550/arXiv.2403.05711
  3. Arlin, K., Fairbanks, J., Hosgood, T., & Patterson, E. (2024). The diagrammatic presentation of equations in categories. arXiv:2401.09751.
  4. Bumpus, B. M., Capucci, M., Fairbanks, J., & Rosiak, D. (2024). Failures of compositionality: a short note on cohomology, sheafification and lavish presheaves. arXiv:2407.03488.
  5. Althaus, E., Bumpus, B. M., Fairbanks, J., & Rosiak, D. (2023). Compositional Algorithms on Compositional Data: Deciding Sheaves on Presheaves. arXiv. https://doi.org/10.48550/arXiv.2302.05575

Talks, extremely comprehensive list

Talks now have their own page. This heading is kept so that old links still resolve.