Hyunjin Seo
I'm a Ph.D. student at KAIST, advised by Prof. Sungsoo Ahn.
My research focuses on practical applications of geometric deep learning.
In particular, I explore the integration of large language models (LLMs) with graph neural networks (GNNs) to enhance graph reasoning capabilities.
Central to my work is enhancing graph data by decomposing inherent semantic relationships of binary connections between textual entities using LLMs.
Additionally, I have developed pruning and calibration techniques tailored for GNNs to address the computational challenges and network under-confidence.
My work also extends into the domain of molecules, where I develop graph transformer for ground-state molecular conformation prediction through energy-based graph rewiring.
Recently, I am designing a property-guided molecule diffusion model for controlled polymer generation.
