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Pierre R (Jan 24 2025 at 17:12):Hello everyone,
I hope this message finds you well! I’m a second-year master’s student in mathematics with experience in data engineering, data science, and artificial intelligence. Recently, I’ve been exploring Geometric Deep Learning and have become deeply interested in the nascent field of Categorical Deep Learning, which I discovered through the Category for AI School videos and the papers by Bruno Gavranović et al.
I’m now seeking to get involved in a research project that bridges applied category theory with a computational component, involving the development and testing of algorithms derived from category-theoretic methods. (I am a beginner in category theory, so I apologize if my terminology lacks precision.)
An example of the type of project I’m interested in is the 2025 Adjoint School project “Compositional Generalization in Reinforcement Learning” by Georgios Bakirtzis. This project addresses compositional generalization in reinforcement learning through a category-theoretic computational framework in Julia, producing both category theory-derived algorithms and experimental results. This blend of theoretical depth and practical experimentation is precisely what excites me, and I would love to explore similar opportunities.
Unfortunately, I couldn’t apply to the Adjoint School in time, but I’m keen to find other avenues to engage with similar research. I’d greatly appreciate:
Suggestions for ongoing or upcoming projects that combine theoretical and computational aspects of applied category theory and AI.
Recommendations for resources, tools, or techniques to help bridge the gap between theory and implementation.
Advice on how to build collaborations with researchers or groups working on these intersections.
I’m eager to contribute and collaborate with like-minded individuals or groups. If you have any ideas, opportunities, or guidance, I’d be deeply grateful!