Research, and also just stuff I'm interested in.
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Applied Sheaf Theory
Recently, I've been fascinated by the mathematical structures called sheaves. Originating in algebraic geometry, sheaves are incredibly powerful tools that can connect seemingly distant ideas in beautiful ways. Their ability to fit into so many mathematical concepts comes at the cost of being very abstract and hard to pin down. Just look at the mathematical definition of a sheaf. It makes like no sense unless you already know what a sheaf is. In short, a sheaf is a tool that allows us to look at something at a very small resolution and stitch together our "local" observations to construct a "global" understanding of the object in question.

Currently, I'm thinking of ways we can use sheaves and all that they can do for us to study neural systems and neural populations. It makes sense
Here are some papers I've been looking at on the topic
Jakob Hansen's Gentle Introduction to Sheaves on Graphs
Jakob Hansen, Prof Rob Ghrist, Learning Sheaf Laplacians from Smooth Signals
Prof Michael Robinson's presentation
Information Theory
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Here are some papers I've been looking at on the topic
Shannon lol
Shannon lol
Shannon lol
Shannon lol
walnuts
Past Projects
As an undergraduate, I used simplicial homology to study how certain neural populations maintain synchronous behavior. Using a idea from this study to quantify