Yeon-Kug Moon | Artificial Intelligence | Research Excellence Award

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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

Dr. Hassen Nigatu | Robotics and Automation | Best Industrial Research Award

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Dr. Hassen Nigatu | Robotics and Automation | Best Industrial Research Award

Yuyao Robot Research Center | China

Dr. Hassen Nigatu is a distinguished robotics researcher and Young Thousand Talents award recipient, specializing in theoretical kinematics and high-precision parallel robots. His work integrates Lie algebra, screw theory, and dynamic modeling with emerging areas such as machine learning and quantum computing for advanced robotic optimization and control. He has over a decade of research experience at KIST and Zhejiang University, leading projects on parasitic-motion-free mechanisms, quantum optimization, VR haptic systems, Robotics and Automation. His research bridges rigorous theory with real-world applications, resulting in impactful publications and funded projects advancing next-generation robotics.

Citation Metrics (Scopus)

60
45
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15
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Citations
55

Documents
15

h-index
4

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Documents

h-index


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Featured Publication

Dr. Chao Sun | Artificial Intelligence | Research Excellence Award

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Dr. Chao Sun | Artificial Intelligence | Research Excellence Award

Anhui University | China

Dr. Chao Sun is a Lecturer at the School of Artificial Intelligence, Anhui University, China. He earned his PhD in Control Science and Engineering from Tongji University, following degrees in Mechanical Engineering from NUAA and the University of Jinan. His research focuses on visual SLAM, reinforcement learning, computer vision, Artificial Intelligence and robotics. He has received multiple academic honors and serves as an invited reviewer for leading IEEE journals. His work contributes to advanced intelligent systems and autonomous robotics research.

Citation Metrics (Scopus)

200
150
100
50
0

Citations
172

Documents
16

h-index
8

Citations

Documents

h-index


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Featured Publication

Mr. Nitin Desai | Industrial Robotics | Best Researcher Award

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Mr. Nitin Desai | Industrial Robotics | Best Researcher Award

Indian Institute of Technology Kharagpur | India

Dr. Nitin Desai is a distinguished researcher in industrial robotics and manufacturing automation, with a strong record of interdisciplinary contributions in both software and hardware domains. He is currently pursuing a Ph.d. in robotics at the Indian Institute of Technology Kharagpur, focusing on high-precision 6d pose estimation for robot manipulators using deep learning and 3d vision techniques. His expertise spans computer vision, vision-language models, large language models, reinforcement learning, Industrial Robotics and generative ai—applied to robotic motion planning, grasping, and autonomous decision-making. prior to his doctoral work, he earned his M.Tech. in cad cam and automation and robotics from veermata jijabai technological institute, mumbai, B.E. In mechanical engineering from dkte society’s textile & engineering institute, Kolhapur. Dr. Desai has served as a research scholar at the centre of excellence in advanced manufacturing technology, iit kharagpur, and as a technical consultant at scale ai, contributing to generative ai model training and data annotation for advanced automation systems. With 1 citation, 3 published documents, and an h-index of 1, his scholarly work reflects growing recognition in the academic community. his ongoing research continues to bridge artificial intelligence, robotics, and industrial automation for next-generation intelligent systems.

Profile: Scopus

Featured Publication

Desai, N. (2025). Implementation of Soft Computing-Based Metaheuristic Algorithms in Multiobjective Environmentally-Conscious Machining Operation Sequence Optimization with Carbon Emission Reduction.

Ms. Juman Alsadi | Smart Manufacturing Systems | Best Researcher Award

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Ms. Juman Alsadi | Smart Manufacturing Systems | Best Researcher Award

Khalifa University | United Arab Emirates

Ms. Juman Alsadi is a Ph.d. candidate in engineering systems and management at Khalifa University, Abu Dhabi, UAE, with a research focus on the digitalization of operational excellence methodologies, particularly the integration of lean six sigma and industry 4.0. her academic journey includes a master’s degree in engineering systems and management from the same university, she continues to maintain outstanding academic performance. Her research aims to develop a conceptual framework for lss 4.0 adoption, emphasizing organizational maturity, change management, and sustainable digital transformation. beyond research, Dr. Alsadi has served as a teaching and research assistant, mentoring students, supporting course design, and contributing to scholarly publications. she has also gained professional experience in logistics, supply chain, Smart Manufacturing Systems and project management through roles at digital qatalyst, reda chemicals, and julphar pharmaceuticals. Her academic influence is reflected in her 7 published documents, 41 citations, and an h-index of 3, highlighting her growing contribution to the fields of industrial engineering, lean transformation, and digital innovation. driven by a passion for excellence, Dr. Alsadi continues to bridge research with real-world impact across academia and industry.

Profiles: Scopus | Orcid

Featured Publications

Alsadi, J., Antony, J., Goonetilleke, R., Romero, D., Chiarini, A., Tortorella, G., & Maalouf, M. (2025). A qualitative global study on the digitalization of Lean Six Sigma: Insights from scholars and practitioners. Production Planning & Control.

Alsadi, J., Alkhatib, F., Antony, J., Garza-Reyes, J. A., Tortorella, G., & Cudney, E. A. (2024). A systematic literature review with bibliometric analysis of Quality 4.0. The TQM Journal.

Saad, A., Alsadi, J., Al Absi, D., Almulla, M., Simsekler, M. C. E., Sadeq, A. A., Omar, F., Basha, M., Khattab, I., & Abu Khater, N., et al. (2024). An integrative risk assessment approach to enhancing patient safety in continuous renal replacement therapy (CRRT). Journal of Safety Science and Resilience.

Alkhatib, F. Y., Alsadi, J. K., Ramadan, M. A., Antony, J., & Swarnakar, V. (2024). Industry 4.0 applications in the healthcare sector. In Industry 4.0 and Quality Management (pp. 27–48). Routledge.

Alsadi, J., Antony, J., Mezher, T., Jayaraman, R., & Maalouf, M. (2023). Lean and Industry 4.0: A bibliometric analysis, opportunities for future research directions. Quality Management Journal.

Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

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Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

Goethe University, Frankfurt | Germany

Tamanna is a Ph.d. researcher at Goethe University, Frankfurt, Germany, specializing in geochemistry with a focus on integrating data science and machine learning into geo-scientific research. Her work aims to bridge the gap between traditional geoscience and modern computational methodologies by leveraging data-driven approaches to analyze complex geochemical systems. She holds a bs-ms degree in earth and environmental sciences from the Indian institute of science education and research, Bhopal. her expertise spans elemental and isotopic geochemistry, geospatial analysis, statistical modeling, and predictive analytics for geochemical processes. She is proficient in programming and data visualization using python, Neural Networks for Robot Control, matlab, qgis, and arcgis. Tamanna has authored two research documents with approximately one citation and an h-index of 1 for those publications; overall, her author-level record includes 99 citations and an h-index of 6, according to google scholar. Beyond her technical skills, she is actively involved in interdisciplinary research that combines quantitative methods, laboratory work, and field investigations to enhance the understanding of earth’s chemical evolution. fluent in english and hindi, with working knowledge of german, she represents the next generation of data-driven geoscientists.

Profile: Orcid

Featured Publications

Tamanna, Hezel, D. C., & Marschall, H. R. (2025, October). MRMinerals and MineralTD: Machine‐Readable Mineral Formula and Compositions Data Set for Data‐Driven Research. Geoscience Data Journal.

Tamanna, Hezel, D. C., Srivastava, N., & Faber, J. (2025, August 13). Using machine learning for automatic rock classification. American Mineralogist.