Talks

Invited talks, conference presentations, and workshop sessions, most recent first. Papers are on the full bibliography.

  1. Fairbanks, J. P. (2026). Compositional Modeling: Structures, Dynamics, Optimization. American Control Conference, New Orleans, LA.
  2. Rauta, G., Fairbanks, J. P., & Kuzendorf, W. (2026). Low-Mach Compressible Navier-Stokes Using Discrete Exterior Calculus on Rectilinear Grids. WCCM-ECCOMAS, Munich, Germany.
  3. Cohen, S., & Fairbanks, J. P. (2026). Federated Learning with Cellular Sheaves. UF Undergraduate Research Symposium, Gainesville, FL.
  4. Hanks, T., Riess, H., Cohen, S., Gross, T., Hale, M., & Fairbanks, J. P. (2026). Distributed Multi-Agent Coordination over Cellular Sheaves. AMS Special Session on Applied Category Theory Joint Mathematics Meeting, Washington DC. https://doi.org/10.1109/cdc57313.2025.11312066
  5. Gross, T. (2026). Quadrotor Coordination Using Cellular Sheaves and Linearized LQR. Center for Undergraduate Research Spring Symposium, Gainesville, FL, USA.
  6. Hanks, T., Nino, C., Bou Barcelo, J., Copeland, A., Dixon, W., & Fairbanks, J. P. (2026). Heterogeneous Multi-Agent Multi-Target Tracking. INFORMS Optimization Society Conference, Atlanta, GA.
  7. Hanks, T., Klawonn, M., Patterson, E., Hale, M., & Fairbanks, J. P. (2026). Categorical Foundations of Distributed Optimization and Learning. AMS Special Session on Mathematical Foundation of Machine Learning, Joint Mathematics Meeting, Washington DC.
  8. Hanks, T. (2026). Coordination Sheaves. Core Lab Georgia Tech, Atlanta.
  9. Fairbanks, J. P., & Zare, A. (2025). Domain Transfer for Continuity of Performance Across SAS Systems. ONR Code 32 Program Review, Online.
  10. Leal, W., & Fairbanks, J. P. (2025). Sheaves in Dynamical Systems: Enumerating Fixed Points of CTLNs. Kallies Research Group, Toledo, OH.
  11. Leal, W. (2025). Sheaves in Time Varying Data and Dynamical Systems. NCR Lab UF MAE Department, Gainesville, FL.
  12. Fairbanks, J. P., & Leal, W. (2025). Computing Fixed Points of CTLNs with Separated Presheaves, Sheaves and Dynamic Programming. Category Theory Octoberfest 2025, Online.
  13. Fairbanks, J. P., Morris, L., & Rauta, G. (2025). Building Modeling and Simulation Tools on Discrete Exterior Calculus Foundations [Talk]. IMSI Workshop on DEC, Differential Geometry, and Applications, Chicago, Ill..
  14. Wall, A., & Fairbanks, J. P. (2025). A Category-Theoretic Approach to Resource Optimization. University Mathematics Society, Gainesville, FL.
  15. Fairbanks, J. P., & Leal, W. (2025). Sheaf Cohomology on Simplicial Complexes. CODAC COE, Gainesville, FL.
  16. Leal, W. (2025). Temporal Analysis of Data Using Category Theory and the Hidden Connection Between Persistence and Accumulation. Seminario de Matemáticas Aplicadas, Bucaramanga, Colombia.
  17. Fairbanks, J. P. (2025). Going beyond graphs: simplicial, hyper, and relational structure [Talk]. JuliaCon 2025, Pittsburg, PA.
  18. Fairbanks, J. P. (2025). New Approaches in Computational Physics: Multiphysics and Multiscale with Discrete Exterior Calculus [Talk]. Army Research Laboratory Seminar, Adelphi, MD.
  19. Fairbanks, J. P. (2025). Scientific Modeling with Categories [Workshop]. American Control Conference, Denver, CO.
  20. Fairbanks, J. P. (2025). Applied Category Theory for Dynamics and Control [Workshop]. American Control Conference, Denver, CO.
  21. Fairbanks, J. P. (2025). Introduction to Applied Category Theory for Compositional Decision Making. ACC Conference Workshop, Denver, CO.
  22. Fairbanks, J. P. (2025). Compositional Modeling in Applied Category Theory for Compositional Decision Making. ACC Conference Workshop, Denver, CO.
  23. Rauta, G. (2025). Modeling of Coupled Weakly Compressible Navier-Stokes and Thermal Equation in the Discrete Exterior Calculus. Army Research Lab Poster Session, Adelphi, MD.
  24. Fairbanks, J. P. (2025). Modeling with ACT for Compositional Decision Making [Talk]. American Control Conference, Denver, CO.
  25. Fairbanks, J. P., & Patterson, E. (2025). Compositional Development of Compositional Mathematics [Talk]. Applied Category Theory, Gainesville, FL.
  26. Hanks, T. (2025). Category Theory for Distributed Optimization. Applied Category Theory, Gainesville, FL.
  27. Zelko, J., Cuffaro, M., & Wu, S. L. (2025). A Case Study in Public Health Research. Applied Category Theory, Gainesville, FL.
  28. Fairbanks, J. P. (2025). Multiagent Control with Cellular Sheaves [Talk]. GTRI Seminar, Atlanta, GA.
  29. Wall, A., & Fairbanks, J. P. (2025). Structured Decompositions. Undergraduate Mathematics Research Symposium, Gainesville, FL.
  30. Carlson, K. (2025). Multigrid Methods for Structure Preserving Discretizations [Talk]. 22nd Copper Mountain Conference on Multigrid Methods, Copper Mountain, CO.
  31. Fairbanks, J. P., & Kim, N. (2025). T6: Validation, Uncertainty Quantification, Uncertainty Budget, Workflow and Data Management. CM3C: Center for Multiscale Modeling of Multiphase Combustion, Gainesville, FL.
  32. Hanks, T., Klawonn, M., Hale, M., Patterson, E., & Fairbanks, J. P. (2024). A compositional framework for first-order optimization. Air Force Research Laboratory, Rome, NY.
  33. Fairbanks, J. P., Aduddell, R., Kumar, A., Ocal, P. S., Patterson, E., & Shapiro, B. T. (2024). A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks. AMS Southeastern Sectional Meeting, Tallahassee, FL.
  34. Morris, L., Rauta, G., & Fairbanks, J. P. (2024). Categorical Composition of Discrete Exterior Calculus Climate Models [Extended abstract]. Programming for the Planet (PROPL) at POPL 2024, London, UK. https://popl24.sigplan.org/home/propl-2024#event-overview
  35. Morris, L., Baas, A., Fairbanks, J. P., Arias, J., & Gaitlin, M. (2023). Abstraction and Composition in Modeling and Simulation. Mechanical and Aerospace Engineering Seminar, Gainesville, FL.
  36. Morris, L., & Nathan, E. (2023). Discourse Sheaves for Opinion Dynamics over Social Media Data. CA.
  37. Aduddell, R., Fairbanks, J. P., Ocal, P. S., Kumar, A., Patterson, E., & Shapiro, B. T. (2023). A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks. Applied Category Theory [extended abstract].
  38. Libkind, S., Bumpus, B. M., Garcia, J. L., Sorkatti, L. H., & Tenka, S. (2023). Additive Invariants of Open Petri Nets. arXiv. https://doi.org/10.48550/arxiv.2303.01643

    A complete classification of the additive invariants of open Petri nets – the natural-number-valued quantities that add under both sequential and parallel composition. Two theorems: for open Petri nets the invariants are fixed by their values on single-transition nets, and for monically open nets by their values on transitionless nets together with all single-transition nets. The modeling project composes Petri nets constantly, and this says exactly which numerical quantities survive that composition – which ones can be computed on the parts and added, instead of recomputed on the whole.

    Read it for The two classification theorems, and the definition of what it means for an invariant of an open system to be additive.

  39. Fairbanks, J. P., Hanks, T., She, B., Patterson, E., Hale, M., & Klawonn, M. (2023). A Compositional Framework for Convex Model Predictive Control. Applied Category Theory, College Park, MD.
  40. Bumpus, B. M., Fairbanks, J. P., Rosiak, D., & Althaus, E. (2023). Compositional Algorithms on Compositional Data: Deciding Sheaves on Presheaves. Applied Category Theory, College Park, MD.
  41. Lynch, O., Brown, K., Fairbanks, J. P., & Patterson, E. (2023). Gatlab.jl: Symbolic computing with categories using generalized algebraic theories. Applied Category Theory, College Park, MD.
  42. Fairbanks, J. P., Morris, L., & Rauta, G. (2023). Computational Multiphysics in a Categorical Framework. Applied Category Theory, College Park, MD.
  43. Fairbanks, J. P., Hanks, T., She, B., Hale, M., Patterson, E., & Klawonn, M. (2023). A Compositional Framework for Model Predictive Control. AFOSR Center of Excellence Program Review, Gainesville, FL.
  44. Fairbanks, J. P. (2023). Decapodes.jl: a framework for multiphysics simulation. MAE Department AFOSR Visit.
  45. Morris, L., Baas, A., Fairbanks, J. P., Arias, J., & Gaitlin, M. (2023). Abstraction and Composition in Modeling and Simulation. SIAM Conference on Computational Science and Engineering, Amsterdam, NL.
  46. Bumpus, B. M., & Fairbanks, J. P. (2023). Chopping things up to decide stuff fast. 54th Southeastern International Conference on Combinatorics, Graph Theory and Computing, Boca Raton, Florida, USA.
  47. Fairbanks, J. P., Aduddell, R., Ocal, P. S., Patterson, E., Shapiro, B. T., & Kumar, A. (2023). A Categorical Framework for (Gene) Regulatory Networks. Joint Mathematics Meetings, Boston, MA.
  48. Morris, L., Baas, A., Fairbanks, J. P., Arias, J., & Gaitlin, M. (2023). Abstraction and Composition in Modeling and Simulation. Graduate Mathematics Association, Gainesville, FL.
  49. Fairbanks, J. P., & Lynch, O. (2023). Computational category theory in applied mathematics [Invited]. Joint Mathematics Meetings, Boston, MA.
  50. Fairbanks, J. P. (2022). Scientific and engineering modeling with applied category theory. DARPA Young Faculty Colloquium.
  51. Fairbanks, J. P., & Patterson, E. (2022). Enkix task reasoning. DARPA Program Review.
  52. Fairbanks, J. P. (2022). Computational physics with categories. Institute of Theoretical Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg.
  53. Fairbanks, J. P., & Patterson, E. (2022). Enkix task reasoning. DARPA Site Visit.
  54. Fairbanks, J. P. (2022). Using category theory to design computational mathematics software. UF Numerical Analysis and SIAM Seminar.
  55. Hanks, T. (2022). Compositional Convex Optimization. Air Force Research Laboratory, Rome, NY.
  56. Fairbanks, J. P. (2022). Applied category theory for the mathematics of disease. Canadian Network for Modeling Infectious Disease.
  57. Fairbanks, J. P. (2022). Scientific and engineering modeling with applied category theory. MAE Control Theory Working Group.
  58. Patterson, E., Hosgood, T., Baas, A., & Fairbanks, J. P. (2022). Diagrammatic differential equations: Formal categorical framework and applications to multiphysics simulation. Applied Category Theory, Glasgow, UK. https://doi.org/10.3934/mine.2023036
  59. Libkind, S., Baas, A., Halter, M., Patterson, E., & Fairbanks, J. P. (2022). Typed and stratified models with slice categories. In Applied Category Theory (pp. 1-3). https://msp.cis.strath.ac.uk/act2022/papers/ACT2022_paper_3530.pdf

    The construction behind stratification. One Petri net is chosen as a type system, a typed model is then an object of the category of Petri nets sliced over it, and the product in that slice category is the stratified model – so stratifying by age, region or host and vector is a categorical operation rather than a hand edit to a reaction network. The flagship paper says what stratification buys; this is the four-page account of the construction that AlgebraicPetri.jl actually implements, and of how it reproduces stratified models already in the epidemiological literature.

    Read it for The slice-category definition and the host/vector example. It is a technical demonstration, so there is little to skip.

    Read first An algebraic framework for structured epidemic modelling

  60. Fairbanks, J. P. (2022). Diagrammatic equations in physics: Directly computable models. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing.
  61. Fairbanks, J. P. (2022). Model aware scientific computing with categories. Air Force Research Laboratory Information Directorate, Rome, NY.
  62. Fairbanks, J. P., & Zare, A. (2022). Diagrammatic equations for complex machine learning formulations. ECE Department ONR Site Visit.
  63. Fairbanks, J. P. (2022). Computational modeling with category theory. Systems Medicine Laboratory Seminar, UF College of Medicine.
  64. Fairbanks, J. P. (2022). Diagrammatic equations in numerical multiphysics. Simula Research Laboratory, Numerical Analysis Research Seminar (Oslo, NO).
  65. Fairbanks, J. P. (2022). Introduction to applied category theory. Simula Research Laboratory, Coffee and Theorems (Oslo, NO).
  66. Fairbanks, J. P. (2022). Scientific modeling with AlgebraicJulia. rel.ai Research Seminar.
  67. Bumpus, B. M., & Fairbanks, J. P. (2022). Structured Decompositions: Recursive Data and Recursive Algorithms. Joint Mathematics Meetings, Boston, MA.
  68. Brown, K., & Fairbanks, J. P. (2022). Automated model space exploration. Topos Institute and UW-IHME Compositional Epidemiology Modeling Working Group.
  69. Libkind, S., & Fairbanks, J. P. (2022). Compositional modeling of disease dynamics. Topos Institute and UW-IHME Compositional Epidemiology Modeling Working Group.
  70. Wu, S. L., Libkind, S., Brown, K., Patterson, E., & Fairbanks, J. P. (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
  71. Jackson, M., Halter, M., Goodyear, T., O’Donnell, B., & Fairbanks, J. P. (2021). Accelerating automatic target recognition performance evaluation with a relational database. Tri-Service Radar Symposium.
  72. Patterson, E., & Fairbanks, J. P. (2021). Compositional modeling with AlgebraicJulia. NIH IMAG MSM Viral Pandemic Meetings. https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings
  73. Lynch, O., Patterson, E., & Fairbanks, J. P. (2021). Shaped data with acsets. JuliaCon, Virtual. https://pretalx.com/juliacon2021/talk/NWRPGY/
  74. Libkind, S., & Fairbanks, J. P. (2021). AlgebraicDynamics: Compositional dynamical systems. JuliaCon, Virtual. https://pretalx.com/juliacon2021/talk/ARURL8/
  75. Fairbanks, J. P. (2021). The Algebraic Julia Ecosystem, a categorical approach to technical computing. Topos Institute Berkeley Seminar. https://topos.site/berkeley-seminar/
  76. Fairbanks, J. P. (2021). Computational categorical algebra with catlab. GReTA: Graph Transformation, Theory, and Applications Seminar. https://www.irif.fr/~greta/talk/may7th2021-fairbanks/
  77. Fairbanks, J. P. (2021). Progress towards the GroMet specification for semantic model exchange. DARPA Automating Scientific Knowledge Extraction Principal Investigator Meeting, Arlington, VA.
  78. Fairbanks, J. P. (2021). Generalized Algebraic Theories for Enhancing Multiphysics: An Introductory Deep Dive. DARPA Directly Computable Models Program Review, Arlington, VA.
  79. Fairbanks, J. P. (2021). Categorical Scientific Knowledge Representation. DARPA Automating Scientific Knowledge Extraction Stakeholder Workshop, Arlington, VA.
  80. Fairbanks, J. P. (2021). Introduction to the AlgebraicJulia software ecosystem. UF CISE and LLNL Advisory Board Annual Meeting.
  81. Fairbanks, J. P. (2021). Model aware scientific computing with categories. DARPA Young Faculty Award Principal Investigators Meeting Poster Session.
  82. Fairbanks, J. P. (2021). Rethinking set theory and applications. UF University Math Society.
  83. Halter, M., Patterson, E., Baas, A., & Fairbanks, J. P. (2020). Compositional Scientific Computing with Catlab and SemanticModels. In Applied Category Theory. http://arxiv.org/abs/2005.04831

    An early statement of the programme: applied category theory supplies reusable software components for scientific computing, with Catlab.jl as the categorical infrastructure and SemanticModels.jl as the modeling layer on top, composing systems as cospan algebras. It is the origin point for Catlab and, through it, for most of what the lab has built since. Read it as history rather than as current practice.

    Read it for The framing in the introduction. The software described has been superseded.

  84. Fairbanks, J. P. (2020). Rethinking Set Theory and Computational Mathematics. Undergraduate Math Society, Gainesville, FL.
  85. Fairbanks, J. P. (2020). Automating model fusion with decorated cospan categories. MIT Category Theory Seminar.
  86. Halter, M., Raparti, S., Cao, K., Herlihy, C., & Fairbanks, J. P. (2020). SemanticModels. jl: a julia package for scientific model augmentation. In Proceedings of the JuliaCon conferences (pp. 57).

    The software half of the SemanticModels work: a Julia package that automates model augmentation and creation by metamodeling and metaprogramming. The argument for Julia is the substance – its type system, its reachable syntax tree, and the embedded domain-specific languages that multiple dispatch makes possible let a model be manipulated at run time and still compile to efficient code. It is the implementation the rest of the SemanticModels papers describe, and the lab’s first attempt to treat model manipulation as a programming-language problem rather than a modeling one.

    Read it for The argument for Julia as the host language. The package itself has been superseded by AlgebraicPetri.jl.

    Read first A Compositional Framework for Scientific Model Augmentation

  87. Herlihy, C., & Fairbanks, J. P. (2019). semanticmodels.jl: Not just another modeling framework. JuliaCon, Baltimore, MD. https://www.youtube.com/watch?v=WJneK7OjqMQ
  88. Fairbanks, J. P., Davis, E., & Morrison, C. (2019). Model IR working group: Initial progress. DARPA ASKE Program Meeting.
  89. Fairbanks, J. P. (2019). Semantic program analysis for scientific model augmentation. Lawrence Livermore National Laboratory.
  90. Nadolski, M., & Fairbanks, J. P. (2019). Complex Systems Analysis of Hybrid Warfare. Conference on Systems Engineering Research.
  91. Fairbanks, J. P. (2019). Program analysis for scientific model augmentation. University of Florida Informatics Institute Spring Symposium.
  92. Herlihy, C., Cao, K., Reparti, S., Briscoe, E., & Fairbanks, J. P. (2019). Semantic Program Analysis for Scientific Model Augmentation. Modeling the World’s Systems, 7.

    SemanticModels.jl builds a knowledge graph linking elements of scientific code – variables, values, functions, expressions – to elements of scientific understanding, and reasons over it to augment, synthesize and validate epidemiological models. The earliest paper in the project, and the clearest statement of the extraction approach the lab later moved away from.

    Read it for The knowledge graph construction. Historical interest unless you work on model extraction.

  93. Fairbanks, J. P. (2018). Data science and graph analytics with julia. University of Florida Informatics Institute.
  94. Fairbanks, J. P. (2018). Solving applied graph theory problems in the JuliaGraphs ecosystem. MIT CSAIL Seminar.
  95. Fairbanks, J. P. (2018). The JuliaGraphs ecosystem: Move fast and don't break things. JuliaCon, London, UK. https://youtu.be/OZuQoxTPoyM
  96. Besançon, M., & Fairbanks, J. P. (2018). Graph interfaces: Bespoke graphs for every occasion. JuliaCon, London, UK. https://youtu.be/OD-BSn4FZ2A
  97. Bromberger, S., & Fairbanks, J. P. (2017). LightGraphs: Our network, our story. JuliaCon, Berkeley, CA. https://youtu.be/MFD-qmApXl8
  98. Frederick, T., Herlihy, C., & Fairbanks, J. P. (2017). Using big data to predict and analyze cooperation and conflict. The Conflict Conference, University of Texas, Austin, TX.
  99. Fairbanks, J. P., 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
  100. Bader, D. A., Michalewicz, A., Green, O., Birkett-Rees, J., Riedy, J., Fairbanks, J. P., & Zakrzewska, A. (2016). Semantic database applications at the samtavro cemetery, georgia. In The 44th Computer Applications and Quantitative Methods in Archaeology Conference (CAA). Archaeopress. https://2016.caaconference.org/session-11-supporting-researchers-in-the-use-and-re-use-of-archaeological-data-continuing-the-ariadne-thread/

Panels

Panel appearances, where the work being discussed is the field’s rather than ours.

  1. Fairbanks, J. P., Ames, A., & Moeller, J. (2026). Panel on Applied Category Theory for Compositional Decision Making. American Control Conference, New Orleans, LA.
  2. Fairbanks, J. P. (2022). HWCOE Early Career Researcher Award Panel. UF ECR Development Workshop.
  3. Cohen, P. R., Davis, E., & Nielson, A. (2019). Abstract Representations of Scientific Models. DARPA ASKE Principal Investigator Meeting.
  4. Elliot, J., Bachman, J. A., Davis, E., Morrison, C., & Fairbanks, J. P. (2019). Toward the Modeling Stack Panel. Modeling the World's Systems 2019. http://sci.pitt.edu/news/05-01-2019/