I am Seunghyun Yoo, a master’s student at the WINE Lab, Korea University. My research focuses on developing robust, autonomous AI systems capable of high-level reasoning in complex or data-constrained environments.
My recent research mainly focused on establishing a strong foundation in AI for Science, specifically in cross-modal alignment for molecular identification, where I addressed the generalization bottleneck in mass spectrometry. I am currently expanding this expertise to Agentic AI and Embodied Intelligence, investigating the interaction of Large Language Models and Vision-Language Models to build autonomous UAV systems that seamlessly bridge digital reasoning with real-world physical actions.
Multi-modal Deep Learning
Representation Learning for Unstructured Data
Agentic AI & Physical AI Systems
seunghyunyoo AT korea DOT ac DOT kr
"Lightweight QRS-attention network for efficient real-time detection of ECG anomalies"
Namkyung Yoon, Sanghong Kim, Seunghyun Yoo, and Hwangnam Kim
Discover Computing, Springer Nature, accepted, 2026
"A Study on the analysis of quantization-induced small object degradation in edge device VLM and compensation effect of hierarchical prompting"
Seunghyun Yoo, Sanghong Kim, Seokmin Kim, Junyeop Bang, and Hwangnam Kim
Proc. Korea Institute of Communications and Information Sciences, June 2026
"HALO: Heterogeneous Admission through Localized Obligations for Safe Agentic Execution"
Taewoo Park, Kyeonghyun Yoo, Kiseok Kim, Seunghyun Yoo, Hwangnam Kim
arXiv:2607.27636 announced July 2026.
"Selective Agentic Recovery for UAV Autonomy with a Persistent Mission Runtime"
Taewoo Park, Kyeonghyun Yoo, Seunghyun Yoo, Hwangnam Kim
arXiv:2606.14219 announced June 2026.
Languages: C, Java, Python, Go, JavaScript, SQL
Libraries: React, Spring boot, TensorFlow
Tools: Visual Studio, IntelliJ
Operating Systems: MS Windows, Linux (Ubuntu), MacOS
Korean(Native)
English(Fluent)