Hello everyone,
I hope you’re all doing well! I’m a master’s student in mathematics with experience in data engineering, data science, and artificial intelligence. I’ve developed a keen interest in Applied Category Theory, particularly its intersection with AI.
Recently, I’ve been focusing my readings on the Category for AI school and, in particular, the work of Bruno Gavranović. I’m now looking to get involved in a research project that combines applied category theory with a computational component, where algorithms are implemented and tested.
A good example of the type of project I’m seeking is the Adjoint School project, “Compositional Generalization in Reinforcement Learning” by Georgios Bakirtzis. In the project description, they mention:
“This project will tackle the problem of compositional generalization in reinforcement learning in a category-theoretic computational framework in Julia. Expected outcomes of this project are category theory-derived algorithms and concrete experiments.”
This approach, blending theoretical foundations with practical experimentation, aligns closely with what I’m looking for.
I’m reaching out to explore collaboration opportunities, potential projects, or simply to gather advice on how to proceed. Specifically, I would appreciate:
- Suggestions for ongoing or new projects that involve both theoretical and computational components in applied category theory and AI.
- Recommendations for resources, tools, or techniques that would help bridge theory and implementation.
- Advice on building collaborations with researchers or groups working on similar problems.
I’m eager to contribute and collaborate with like-minded individuals or groups in this community. If you have any ideas, opportunities, or guidance, I would be incredibly grateful!
Thank you for your time, and I look forward to hearing your thoughts.
Best regards,
Pierre R