Chaeyoon Kim developed a foundational data structures library for the postechDNN/postechDNN repository, focusing on efficient data processing within 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 ensure scalability and performance. This work established reusable, high-performance components that support graph and geometry operations critical to DNN pipelines. By laying the groundwork for future features, Chaeyoon enabled the project to build upon a robust, extensible core, though no bugs were addressed during this period.