Publications

This list is the same data as the bibliography table

Journal Articles

13. 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

12. 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

11. 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

10. 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.

9. 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

8. 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

7. 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

6. 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.

5. Briscoe, E., & Fairbanks, J. (2020). Artificial scientific intelligence and its impact on national security and foreign policy. Orbis, 64(4), 544–554.

4. Nadolski, M., & Fairbanks, J. (2019). Complex systems analysis of hybrid warfare. Procedia Computer Science, 17th Annual Conference on Systems Engineering Research (CSER), 153, 210–217. https://doi.org/10.1016/j.procs.2019.05.072

3. Fairbanks, J. P., Bader, D. A., & Sanders, G. D. (2017). Spectral partitioning with blends of eigenvectors. Journal of Complex Networks, 5(4), 551–580.

2. Fairbanks, J. P., Kannan, R., Park, H., & Bader, D. A. (2015). Behavioral clusters in dynamic graphs. Parallel Computing, 47, 38–50.

1. Fairbanks, J. (2011). A Ramsey theorem for indecomposable matchings. arXiv Preprint arXiv:1110.3314.

Conference Proceedings

26. 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. Submitted. IFAC.

25. Hanks, T., Nino, C., Barcelo, J. B., Copeland, A., Dixon, W., & Fairbanks, J. (2026). Heterogeneous Multi-agent multi-target tracking using cellular sheaves. Submitted. European Control Conference.

24. Zhao, Y., Hanks, T., Riess, H., Cohen, S., Hale, M., & Fairbanks, J. (2026). Asynchronous nonlinear sheaf diffusion for mult-agent coordiantion. Accepted. IEEE American Control Conference.

23. Lary, M., Samuelson, R., Wilentz, A., Zare, A., Klawonn, M., & Fairbanks, J. (2025). Learning diagrams: a graphical language for compositional training regimes. The Thirteenth International Conference on Learning Representations. International Conference on Learning Representations. https://openreview.net/forum?id=dqyuCsBvn9

22. Hanks, T., Riess, H., Cohen, S., Gross, T., Hale, M., & Fairbanks, J. (2025, April 4). Distributed Multi-agent Coordination over Cellular Sheaves. IEEE Conference on Decision and Control. IEEE Conference on Decision and Control. https://doi.org/10.48550/arXiv.2504.02049

21. 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.

20. 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. 2024 American Control Conference (ACC), 4850–4857. https://doi.org/10.23919/ACC60939.2024.10644848

19. Lynch, O., Brown, K., Fairbanks, J., & Patterson, E. (2024). GATlab: Modeling and Programming with Generalized Algebraic Theories. Electronic Notes in Theoretical Informatics and Computer Science, 4.

18. Hanks, T., Klawonn, M., & Fairbanks, J. (2024, March 28). Generalized Gradient Descent is a Hypergraph Functor. Applied Category Theory. https://doi.org/10.48550/arXiv.2403.19845

17. 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]. Proceedings of the AAAI Symposium Series, 2, 491–497. https://doi.org/10.1609/aaaiss.v2i1.27718

16. She, B., Hanks, T., Fairbanks, J., & Hale, M. (2023). Characterizing Compositionality of LQR from the Categorical Perspective. 2023 62nd IEEE Conference on Decision and Control (CDC), 1680–1685. https://doi.org/10.1109/CDC49753.2023.10383467

15. Brown, K., Hanks, T., & Fairbanks, J. (2022). Compositional Exploration of Combinatorial Scientific Models. Applied Category Theory. https://doi.org/10.48550/ARXIV.2206.08755

14. Brown, K., Patterson, E., Hanks, T., & Fairbanks, J. (2022). Computational Category-Theoretic Rewriting [Best Paper]. Graph Transformation: 15th International Conference, ICGT 2022, Held as Part of STAF 2022, Nantes, France, July 7–8, 2022, Proceedings, 155–172. https://doi.org/10.1007/978-3-031-09843-7_9

13. 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. Fairbanks, J. P., Fitch, N., Bradfield, F., & Briscoe, E. (2020). Credibility Development with Knowledge Graphs. In C. Grimme, M. Preuss, F. W. Takes, & A. Waldherr (Eds.), Lecture Notes in Computer Science (Vol. 12021, pp. 33–47). Springer International Publishing. https://doi.org/10.1007/978-3-030-39627-5_4

11. Cao, K., & Fairbanks, J. (2019). Unsupervised Construction of Knowledge Graphs From Text and Code. SIGKDD Conference on Knowledge Discovery and Data Mining International Workshop on Mining and Learning with Graphs, 15.

10. Campbell, N., Goodyear, T., Messer, W., Stuart, E., & Fairbanks, J. (2018). Digital Witness: Remote Method for Volunteering Digital Evidence on Mobile Devices. 2018 IEEE International Symposium on Technologies for Homeland Security (HST), 1–5. https://doi.org/10.1109/THS.2018.8574119

9. Fairbanks, J. P., Fitch, N., Knauf, N., & Briscoe, E. (2018). Credibility Assessment in the News: Do We Need to Read? WSDM/MIS2, 2.

8. Nathan, E., Fairbanks, J., & Bader, D. (2018). Ranking in dynamic graphs using exponential centrality. Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (the Sixth International Conference on Complex Networks and Their Applications), 378–389.

7. Thankachan, R. V., Swenson, B. P., & Fairbanks, J. P. (2018). Performance effects of dynamic graph data structures in community detection algorithms. 2018 IEEE High Performance Extreme Computing Conference (HPEC), 1–7.

6. Ediger, D., & Fairbanks, J. P. (2017). Deriving Streaming Graph Algorithms from Static Definitions. 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Graph Algorithms Building Blocks, 637–642. https://doi.org/10.1109/IPDPSW.2017.146

5. Fairbanks, J., Thankachan, R. V., Hein, E., & Swenson, B. (2017). Integrating productivity-oriented programming languages with high-performance data structures. 2017 IEEE High Performance Extreme Computing Conference (HPEC), 1–8.

4. 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, International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, 108, 68–78. https://doi.org/10.1016/j.procs.2017.05.021

3. Fairbanks, J. P., Zakrzewska, A., & Bader, D. A. (2016). New stopping criteria for spectral partitioning. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 25–32.

2. Zakrzewska, A., Nathan, E., Fairbanks, J., & Bader, D. A. (2016). A local measure of community change in dynamic graphs. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 349–353.

1. Fairbanks, J., Ediger, D., McColl, R., Bader, D. A., & Gilbert, E. (2013). A statistical framework for streaming graph analysis. 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), 341–347. https://doi.org/10.1145/2492517.2492620

Talks and Conference Talks

23. Carlson, K. (2025, April 16). Multigrid Methods for Structure Preserving Discretizations [Talk]. 22ND Copper Mountain Conference on Multigrid Methods, Copper Mountain, CO.

22. Fairbanks, J. (2025, July). Modeling with ACT for Compositional Decision Making [Talk]. American Control Conference, Denver, CO.

21. Fairbanks, J., & Patterson, E. (2025, July). Compositional Development of Compositional Mathematics [Talk]. Applied Category Theory, Gainesville, FL.

20. Fairbanks, J. P., Aduddell, R., Kumar, A., Ocal, P. S., Patterson, E., & Shapiro, B. T. (2024, February). A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks. AMS Southeastern Sectional Meeting, Tallahassee, FL.

19. Aduddell, R., Ocal, P. S., Fairbanks, J. P., Patterson, E., Shapiro, B., & Kumar, A. (2023). A categorical framework for (gene) regulatory networks. Joint Mathematics Meeting, Boston, MA.

18. Fairbanks, J. P., & Lynch, O. (2023). Computational category theory in applied mathematics [Invited]. Joint Mathematics Meetings, Boston, MA.

17. Libkind, S., Baas, A., Halter, M., Patterson, E., & Fairbanks, J. (2022). Typed and stratified models with slice categories. Applied Category Theory, 1–3. https://msp.cis.strath.ac.uk/act2022/papers/ACT2022_paper_3530.pdf

16. Wu, S. L., Libkind, S., Brown, K., Patterson, E., & Fairbanks, J. (2022). Individual. jl: Rewriting individual-based models for epidemiology using graph rewriting [Extended Abstract]. Applied Category Theory, Glasgow, UK. https://msp.cis.strath.ac.uk/act2022/papers/ACT2022_paper_3642.pdf

15. Patterson, E., Hosgood, T., Baas, A., & Fairbanks, J. (2022, July). Diagrammatic differential equations: Formal categorical framework and applications to multiphysics simulation. Applied Category Theory, Glasgow, UK. https://doi.org/10.3934/mine.2023036

14. Libkind, S., & Fairbanks, J. (2021, July). AlgebraicDynamics: Compositional dynamical systems. JuliaCon, Virtual. https://pretalx.com/juliacon2021/talk/ARURL8/

13. Lynch, O., Patterson, E., & Fairbanks, J. (2021, July). Shaped data with acsets. JuliaCon, Virtual. https://pretalx.com/juliacon2021/talk/NWRPGY/

12. Jackson, M., Halter, M., Goodyear, T., O’Donnell, B., & Fairbanks, J. (2021, September). Accelerating automatic target recognition performance evaluation with a relational database. Tri-Service Radar Symposium.

11. Halter, M., Raparti, S., Cao, K., Herlihy, C., & Fairbanks, J. (2020). SemanticModels. jl: a julia package for scientific model augmentation. Proceedings of the JuliaCon Conferences, 1, Article 1.

10. Halter, M., Patterson, E., Baas, A., & Fairbanks, J. (2020, June 29). Compositional Scientific Computing with Catlab and SemanticModels. Applied Category Theory. http://arxiv.org/abs/2005.04831

9. Halter, M., Herlihy, C., & Fairbanks, J. (2019). A compositional framework for scientific model augmentation. arXiv Preprint arXiv:1907.03536.

8. Herlihy, C., Cao, K., Reparti, S., Briscoe, E., & Fairbanks, J. (2019). Semantic Program Analysis for Scientific Model Augmentation. Modeling the World’s Systems, 7.

7. Herlihy, C., & Fairbanks, J. (2019, July). semanticmodels.jl: Not just another modeling framework. JuliaCon, Baltimore, MD. https://www.youtube.com/watch?v=WJneK7OjqMQ

6. Besançon, M., & Fairbanks, J. (2018). Graph interfaces: Bespoke graphs for every occasion. JuliaCon, London, UK. https://youtu.be/OD-BSn4FZ2A

5. Fairbanks, J. (2018). The JuliaGraphs ecosystem: Move fast and don’t break things. JuliaCon, London, UK. https://youtu.be/OZuQoxTPoyM

4. Bromberger, S., & Fairbanks, J. (2017). LightGraphs: Our network, our story. JuliaCon, Berkeley, CA. https://youtu.be/MFD-qmApXl8

3. Fairbanks, J., Knauf, N., Fitch, N., Herlihy, C., & Briscoe, E. (2017). Assessing credibility in the global news media. http://resources.basistech.com.s3.amazonaws.com/hltcon-presentations/2017/Fairbanks_Georgia_Tech_HLTCon.pdf

2. Frederick, T., Herlihy, C., & Fairbanks, J. (2017). Using big data to predict and analyze cooperation and conflict. The Conflict Conference, University of Texas, Austin, TX.

1. Bader, D., Michalewicz, A., Green, O., Birkett-Rees, J., Riedy, J., Fairbanks, J., & Zakrzewska, A. (2016, April 2). Semantic database applications at the samtavro cemetery, georgia. The 44th Computer Applications and Quantitative Methods in Archaeology Conference (CAA). The 44th Computer Applications and Quantitative Methods in Archaeology Conference (CAA). https://2016.caaconference.org/session-11-supporting-researchers-in-the-use-and-re-use-of-archaeological-data-continuing-the-ariadne-thread/

Posters

8. Lynch, O., Fairbanks, J. P., & Patterson, E. (2021, June). Graphical semantic modeling with semagrams.jl. Applied Category Theory, Cambridge, UK.

7. Perez, J., Baas, A., Ferrall-Fairbanks, M. C., Platt, M. O., & Fairbanks, J. P. (2021, October). Parameter estimation by minimizing the loss with respect to a finite difference approximation on the vector field. Biomedical Engineering Society Annual Meeting, Orlando, FL.

6. Fairbanks, J. P. (2019, May). Semantic model understanding for scientific model augmentation. Systems Biology of Human Disease, Berlin, DE.

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.

4. Fairbanks, J. P. (2017). QueryGarden: growing healthy applications in well prepared SQL. OHDSI Symposium, New York, NY.

3. 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

2. Fairbanks, J. P. (2015, March). Discovering block structure with approximate eigenvectors. SIAM Computational Science and Engineering.

1. Fairbanks, J. P. (2012). Ramsey theorem for indecomposable matchings. Graph Theory at Georgia Tech (GT@GT), Atlanta, GA.

Preprints

6. Arlin, K., Fairbanks, J., Hosgood, T., & Patterson, E. (2024). The diagrammatic presentation of equations in categories. arXiv Preprint arXiv:2401.09751.

5. Bumpus, B. M., Capucci, M., Fairbanks, J., & Rosiak, D. (2024). Failures of compositionality: a short note on cohomology, sheafification and lavish presheaves. arXiv Preprint arXiv:2407.03488.

4. Bumpus, B. M., Fairbanks, J., Karvonen, M., Leal, W., & Simard, F. (2024). Towards a Unified Theory of Time-Varying Data (arXiv:2402.00206). arXiv. https://doi.org/10.48550/arXiv.2402.00206

3. Bumpus, B. M., Fairbanks, J., & Turner, W. J. (2024). Pushing Tree Decompositions Forward Along Graph Homomorphisms (arXiv:2408.15184). 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:2403.05711). arXiv. https://doi.org/10.48550/arXiv.2403.05711

1. Althaus, E., Bumpus, B. M., Fairbanks, J., & Rosiak, D. (2023). Compositional Algorithms on Compositional Data: Deciding Sheaves on Presheaves (arXiv:2302.05575). arXiv. https://doi.org/10.48550/arXiv.2302.05575