Curriculum Vitae

Contact Information

Email: t.hanks@ufl.edu

Address: MAE 206, 601 Gale Lemerand Dr. Gainesville, FL, 32611

Research Areas

Category Theory, Type Theory, Distributed Optimization, Control, and Learning

Education

Publications

• [Under review] Compositional Semantics of Distributed First-Order Optimization. Tyler Hanks, Matthew Klawonn, Evan Patterson, Matthew Hale, James Fairbanks. SIAM Journal on Optimization (2024).
• Generalized Gradient Descent is a Hypergraph Functor. Tyler Hanks, Matthew Klawonn, James Fairbanks. Proceedings of the 7th International Conference on Applied Category Theory (2024).
• Modeling Model Predictive Control: A Category Theoretic Framework for Multistage Control Problems. Tyler Hanks, Baike She, Evan Patterson, Matthew Klawonn, Matthew Hale, James Fairbanks. Proceedings of the American Control Conference (2024).
• Characterizing the Compositionality of LQR from the Categorical Perspective. Baike She, Tyler Hanks, Matthew Hale, James Fairbanks. Proceedings of the IEEE Conference on Decision and Control (2023).
• Computational Category-Theoretic Rewriting. Kris Brown, Tyler Hanks, Evan Patterson, James Fairbanks. Journal of Logical and Algebraic Methods in Programming (2023).
• Compositional Exploration of Combinatorial Scientific Models. Kris Brown, Tyler Hanks, James Fairbanks. Proceedings of the 5th International Conference on Applied Category Theory (2022).
• Computational Category-Theoretic Graph Rewriting. Kris Brown, Tyler Hanks, Evan Patterson, James Fairbanks. Proceedings of the International Conference on Graph Transformation (2022).
• PoCo: A Language for Specifying Obligation-Based Policy Compositions. Danielle Ferguson, Yan Albright, Daniel Lomsak, Tyler Hanks, Kevin Orr, Jay Ligatti. Proceedings of the 9th International Conference on Software and Computer Applications (2020).

Research Experience

• Study mathematical optimization, control theory, and machine learning from a sheaf and category theoretic perspective.
• Write papers for conference and journal submissions, collaborate with other researchers, and write code to support theoretical developments.
• Contribute to the development of the AlgebraicJulia suite of scientific computing software.
• Oversee undergraduate senior projects and mentor incoming graduate students.

• Mentor: Dr. Matthew Klawonn.
• Worked on compositional optimization and control theory.
• Work under review at SIAM Journal on Optimization and published in the proceeding of the Applied Category Theory Conference.

• Mentor: Dr. Daniel Marsden
• Researched finite model theory and game comonads.

• Mentor: Dr. Matthew Klawonn.
• Worked on compositional planning and optimization.
• Work published in the proceedings of the American Control Conference.

• Mentor: Dr. Matthew Klawonn.
• Worked on a neural architecture search algorithm based on graph rewriting.
• Work resulted in an invited seminar talk to the AFRL Efficient and Robust Machine Learning Center of Excellence.

Mentorship Experience

• Oversaw two student projects:
1. Implementing Network Flow on Stochastic Block Models. Leonard Seto.
2. Comparative Analysis of K-Means and Gaussian Mixture Models: Empirical Insights Using Julia. Forrest Chai.
• Guided research projects and oversaw the creation of papers, posters, and talks about the projects.

Awards and Honors

• ICGT Best Paper Award: Computational Category-Theoretic Graph Rewriting (2022).
• Recipient of a National Science Foundation Graduate Research Fellowship (2022).
• University of Florida Herbert Wertheim College of Engineering Dean’s Research Award (2021).

Talks

• A Compositional Framework for First-Order Optimization. SIAM Conference on Mathematics of Data Science. 2024.
• A Category Theoretic Framework for Multistage Control Problems. American Control Conference. 2024.
• Generalized Gradient Descent is a Hypergraph Functor. Applied Category Theory. 2024.
• A Compositional Framework for First-Order Optimization. Applied Category Theory. 2024.
• Characterizing Compositionality of LQR from the Categorical Perspective. IEEE Conference on Decision and Control. 2023.
• A Compositional Framework for Convex Model Predictive Control. Applied Category Theory. 2023.
• A Compositional Framework for Convex Model Predictive Control. International Conference on Robotics and Automation. Workshop on Compositional Robotics. 2023.
• Towards Systematic Understanding of Deep Learning Architectures. AFRL Efficient and Robust Machine Learning Center of Excellence. 2021.

Technical Skills

• High performance computing: C/C++, Julia, Cuda, Assembly (MIPs and x86)
• Distributed computing: Actor model (Go and Pony), MPI (Julia and C)
• Machine learning: Python/PyTorch, Julia/Lux.jl
• Back-end: Go, SQL, C#
• Front-end: HTML/CSS/Typescript, Tailwindcss, Svelte, Clojurescript
• Type theory/theorem provers: SML, Lean, Coq
• Math: Linear Algebra, Calculus, Distributed Optimization, Category Theory, Randomized Algorithms