Chaeyoon Kim developed a foundational data structures library for the postechDNN/postechDNN repository, focusing on efficient data processing and scalable performance in deep neural network workflows. Over the course of one month, Chaeyoon implemented core structures such as AVLTree, BBST, DCEL, RBTree, SegL1Voronoi, SegTree, and a compressed quadtree, using C++ and algorithmic design principles to support fast graph and geometry operations. This work established a reusable, high-performance core for future features, addressing the need for reliable, performance-critical components in DNN pipelines. The initial library and project scaffolding laid a solid technical foundation for ongoing development.