Gyeongsang National University | South Korea
Mr. kangwon lee is a senior undergraduate student in Computer Engineering at Gyeongsang National University, specializing in artificial intelligence, music technology, audio signal processing, and natural language processing. he has pursued impactful research projects, including the development of an AI-based sentiment analysis and depression risk detection platform that integrates Valence, Arousal, and Dominance (VAD) metrics for more nuanced prediction models. As a co-author, he contributed to a peer-reviewed paper on AI-based emotion detection and expert-linked platforms, published in the Journal of the Korea Information Technology Society (2025), and his work earned the Excellence Prize at the 33rd Software Contest hosted by Gyeongsang National University in 2024. Professionally, Mr. lee has demonstrated strong technical and problem-solving skills across both civilian and military roles. At Appen Limited, he currently works as a Quality Assurance specialist, where he ensures data quality and optimizes annotation processes. During his military service, he served as an RPA Developer and Convergence Systems Developer for the Republic of Korea Air Force, achieving major efficiency gains by enhancing automation workflows with Python and UiPath. Additionally, he gained hands-on IT support experience at Samdong Heungsan Co., Ltd., managing system maintenance, software deployment, and network configuration for DB Insurance. Proficient in Python, PyTorch, SQL, and UiPath, Mr. lee holds certifications such as SQLD and TOEIC Speaking (AL). His career reflects a strong commitment to integrating AI technologies into human-centered applications, advancing innovative solutions that bridge technical advancement with social impact.
Featured Publications
Web-Based Platform for Quantitative Depression Risk Prediction via VAD Regression on Korean Text and Multi-Anchor Distance Scoring
Development of an AI-based Sentiment Analysis and Expert-Linked Platform for Early Detection of Socially Isolated and Depression Risk Groups