I am a Ph.D. student in Computer Engineering at Gachon University under the supervision of Prof. Chang Choi. My research topics on Knowledge Inference, Graph Representation, and Explainable AI, specifically focusing on video understanding. Currently, my research employs scene and knowledge graphs to model semantic relationships and contextual dependencies, advancing methodologies to enhance the transparency and interpretability of AI systems. This research addresses critical challenges such as data sparsity, domain variability, and scalability, contributing to bridging the gap between low-level visual data and high-level semantic abstraction.
I am actively seeking opportunities for academic collaboration in visual understanding, knowledge inference, and related domains. Please feel free to reach out to discuss potential research partnerships.
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Gachon UniversityPh.D. Student in Computer EngineeringMar. 2023 - present -
Gachon UniversityM.S. in Computer EngineeringSep. 2021 - Feb. 2023
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Gachon UniversityB.S. in Computer EngineeringMar. 2016 - Aug. 2021
Experience
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The Catholic University of KoreaLecturer in AI Dept.Mar. 2023 - Aug. 2024-
- Discrete Mathematics for AI (Spring 2023)
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- Linear Algebra (Fall 2023)
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- Algorithm (Spring 2024)
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News
Selected Publications (view all )
Knowledge Sharing based Lightweight Transformer for Construction Safety Accident Prevention
NamGyu Jung, SaeBom Lee, Chang Choi
ACM/SIGAPP Symposium on Applied Computing (SAC) 2024
We present a lightweight transformer model with shared embeddings between encoders and decoders, designed to enhance efficiency and address expression imbalance in construction safety prediction. The model reduces parameters by 48% compared to conventional transformers and improves performance by 4% over LSTM, enabling effective correlation analysis and deployment in edge computing environments.
Knowledge Sharing based Lightweight Transformer for Construction Safety Accident Prevention
NamGyu Jung, SaeBom Lee, Chang Choi
ACM/SIGAPP Symposium on Applied Computing (SAC) 2024
We present a lightweight transformer model with shared embeddings between encoders and decoders, designed to enhance efficiency and address expression imbalance in construction safety prediction. The model reduces parameters by 48% compared to conventional transformers and improves performance by 4% over LSTM, enabling effective correlation analysis and deployment in edge computing environments.
Kiosk Recommend System Based On Self-Supervised Representation Learning of User Behaviors in Offline Retail
NamGyu Jung, Van Thuy Hoang, O-Joun Lee, Chang Choi
IEEE Internet of Things Journal 2024
We propose a context-aware hyper-personalized recommendation system for kiosk IoT devices, addressing data imbalance across domains with an efficient self-supervised learning method. The system demonstrated a 20% improvement in performance metrics and an additional 0.8% gain with self-supervised learning, ensuring high-quality recommendations and optimal resource usage.
Kiosk Recommend System Based On Self-Supervised Representation Learning of User Behaviors in Offline Retail
NamGyu Jung, Van Thuy Hoang, O-Joun Lee, Chang Choi
IEEE Internet of Things Journal 2024
We propose a context-aware hyper-personalized recommendation system for kiosk IoT devices, addressing data imbalance across domains with an efficient self-supervised learning method. The system demonstrated a 20% improvement in performance metrics and an additional 0.8% gain with self-supervised learning, ensuring high-quality recommendations and optimal resource usage.