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Bridging Structural Data and Language Models for Scientific Discovery

Speaker: Dr. Dongkwan Kim @ Texas A&M University

Time : 10:30 - 11:30, May 12th, 2026

Location :Online: https://hli.skku.edu/InvitedTalk260512 

Language: English speech & English slides

Abstract: 

As machine learning models are increasingly used to understand complex natural and social systems, the integration of structural data and human knowledge has become essential. In this talk, I will present my research trajectory, moving from the question of how to encode structure to the more challenging task of interfacing structure with knowledge. My early work focused on graph representation learning to capture higher-order patterns while addressing challenges in noise and scalability. These efforts led to my current research, where I explore how to link structural information with the reasoning capabilities of large language models. I will discuss our recent work in this direction, including mechanistic reasoning for single-cell perturbations, protein function explanations, and U.S. political lobbying network analysis. These studies demonstrate how combining structured data and large language models can decode complex interactions across biological and social systems.

Bio:

Dongkwan Kim is a postdoctoral researcher at Texas A&M University, working with Professor Yang Shen. He earned his PhD from the School of Computing at KAIST under the supervision of Professor Alice Oh. His research focuses on developing foundational models that leverage complex relational and hierarchical structures to accelerate scientific discovery.