Tyler Hanks
Tyler joined the GATAS Lab in the summer of 2021 as a graduate research assistant, and worked at the intersection of optimization, control theory and machine learning. He looks for the compositional structure in a problem – multi-agent systems, distributed optimization, deep network architectures – and uses category theory, abstract algebra and type theory to turn it into a specification that can be reused across problems rather than rebuilt for each one.
He also wrote a good deal of the lab’s software: the Julia code implementing the categorical abstractions behind the GATAS papers is largely his, and it went into the lab’s open-source repositories rather than staying in a branch.
In 2022 he was awarded a National Science Foundation Graduate Research Fellowship. He presented at the American Control Conference, the Conference on Decision and Control, and Applied Category Theory, and mentored undergraduates – Sam Cohen and Trevor Gross – and newer graduate students, including Richard Samuelson. His hobbies include gaming, cycling, and playing music with his band Infinite Eights.
Selected publications
- Modeling Model Predictive Control: A Category-Theoretic Framework for Multistage Control Problems (2024). Tyler Hanks, Baike She, Evan Patterson, Matthew Hale, Matthew Klawonn, James Fairbanks. American Control Conference (ACC 2024). DOI: 10.48550/arXiv.2305.03820 (see ACC 2024 proceedings).
- Characterizing Compositionality of LQR from the Categorical Perspective (2023). Baike She, Tyler Hanks, James Fairbanks, Matthew Hale. IEEE Conference on Decision and Control (CDC 2023). DOI: 10.48550/arXiv.2305.01811 (see CDC 2023 proceedings).
- Compositional Exploration of Combinatorial Scientific Models (2022). K. Brown, Tyler Hanks, James Fairbanks. Applied Category Theory conference (ACT 2022). DOI: 10.48550/arXiv.2206.08755.
- Computational Category-Theoretic Rewriting (2022). K. Brown, Evan Patterson, Tyler Hanks, James Fairbanks. International Conference on Graph Transformation (ICGT 2022) – Best Paper Award. DOI: 10.48550/arXiv.2111.03784.
Tyler graduated in Spring 2026 and started a faculty position at the Florida Institute of Technology in Fall 2026!
Talks
- 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
- 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.
- 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.
- Hanks, T. (2026). Coordination Sheaves. Core Lab Georgia Tech, Atlanta.
- Hanks, T. (2025). Category Theory for Distributed Optimization. Applied Category Theory, Gainesville, FL.
- 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.
- 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.
- 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.
- Hanks, T. (2022). Compositional Convex Optimization. Air Force Research Laboratory, Rome, NY.
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