Yeon-Kug Moon | Artificial Intelligence | Research Excellence Award

Assoc. Prof. Dr. Yeon-Kug Moon | Artificial Intelligence | Research Excellence Award

Sejong University | South Korea

Yeon-kug Moon is a distinguished researcher and academic specializing in artificial intelligence, affective computing, and multimodal emotion recognition. Currently serving as an Associate Professor in the Department of Artificial Intelligence and Data Science at Sejong University, he has contributed extensively to advanced AI-driven human interaction systems, multimodal large language models, digital twins, and virtual production technologies. With industrial experience at Samsung Electronics and leadership roles in national AI initiatives, his work bridges academic innovation and real-world intelligent systems applications.

Professional Profile 

Education

Dr. Moon earned his Ph.D. in Bio-microsystem Technology from Korea University, where he developed expertise in interdisciplinary AI and intelligent system technologies. He also completed both his Bachelor’s and Master’s degrees in Electronics Engineering from Inha University. His educational background combines electronics, bio-systems, and artificial intelligence, providing a strong foundation for his research in multimodal computing and human-centered AI technologies.

Professional Experience

Throughout his professional career, Dr. Moon has held significant academic and industrial positions in the field of artificial intelligence and data science. He currently serves as an Associate Professor at Sejong University and additionally leads advanced AI initiatives as Director of the KETI Data Convergence Platform Center. Prior to academia, he worked as a Senior Research Engineer at Samsung Electronics, where he contributed to intelligent systems and next-generation technology development. He also leads the HEART Lab as Principal Investigator, focusing on multimodal emotion recognition and human-AI interaction research.

Research Interest

Dr. Moon’s research interests primarily focus on Multimodal Emotion Recognition, Affective Computing, Human-AI Interaction, Multimodal Large Language Models, Digital Twin systems, and Virtual Production technologies. His work integrates graph neural networks, transformers, cross-modal attention mechanisms, and adaptive AI frameworks to improve emotional intelligence in machines and immersive digital environments. Through interdisciplinary research, he aims to develop intelligent systems capable of understanding human emotions, behaviors, and contextual interactions in real-world applications.

Award and Honor

Dr. Moon has received multiple international academic recognitions for his impactful contributions to artificial intelligence and multimodal computing research. Notably, he received the Highly Cited Paper Award in 2025, reflecting the global influence and scholarly impact of his research publications. His innovative contributions in emotion recognition, AI-based interaction systems, and virtual production technologies have also earned him several international academic awards and patents, establishing him as a recognized researcher in the field of advanced AI systems.

Conclusion

With strong academic credentials, extensive industrial experience, and impactful interdisciplinary research contributions, Yeon-kug Moon has established himself as a leading researcher in artificial intelligence and multimodal emotion recognition. His work continues to advance human-centered AI technologies through innovative approaches in affective computing, digital twins, and immersive intelligent systems. Through research excellence, industry collaboration, and technological innovation, he significantly contributes to the future development of intelligent interactive systems and AI-driven applications.

Publications Top Noted

  • Energy-Efficient Optimization-Based and Low-Complexity Learning-Oriented Hybrid Broadband Millimeter-Wave Precoding Designs for Maximizing Spectral Efficiency in Multirelay MIMO–OFDM Networks
    Authors: Not specified
    Year: 2026
    Citation: IEEE Internet of Things Journal
  • A Comprehensive Dataset of Infant Facial Expressions of Pain Intensity
    Authors: Not specified
    Year: 2026
    Citation: PeerJ Computer Science
  • Graph-Based Representation Learning with Beta Uncertainty for Enhanced Multimodal Emotion Recognition
    Authors: Not specified
    Year: 2026
    Citation: IEEE Transactions on Affective Computing
  • Problems with Quality Using the Analytical Hierarchy Approach, Vendors' Perspective on Software Outsourcing Priorities
    Authors: Not specified
    Year: 2026
    Citation: PeerJ Computer Science