Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Changfan Pan | Causal Inference | Best Researcher Award

Published on by Cognitive Scientist Awards

Mr. Changfan Pan | Causal Inference | Best Researcher Award

Changfan Pan (born January 26, 1998) is a researcher specializing in causal inference, synthetic data generation, and federated learning, particularly in the education domain. He is currently pursuing a Master of Engineering in Computer Science at Zhejiang Normal University, China, where he is researching interpretable models and dataset generation methods. He previously earned a Bachelor’s degree in Telecommunication Engineering from Beijing University of Posts and Telecommunications. His work experience includes roles at Zhongxing Telecom Equipment, China Mobile Communications Group, and Ericsson, focusing on 5G networking, wireless network maintenance, and backend development. He has published in top conferences such as AAAI and IEEE ICDM. He has received numerous honors, including a Kaggle Bronze Medal and a First-Class Scholarship. His technical skills include Python, C++, Pytorch, and Docker, and he is proficient in Mandarin and English. 📚🔬💻

Profile

Education 🎓

Changfan Pan is currently pursuing an M.E. in Computer Science at Zhejiang Normal University (2022-2025) with a GPA of 3.70/5. His master’s thesis focuses on interpretable models and dataset generation based on causal inference under the supervision of Prof. Jia Zhu. He has completed coursework in Artificial Intelligence (91), Data Mining (88), and Advanced Database Technology (86). Previously, he earned a B.E. in Telecommunication Engineering from Beijing University of Posts and Telecommunications (2016-2020), graduating with a GPA of 84/100. His bachelor’s thesis explored viewpoint prediction algorithms in VR, supervised by Prof. Yitong Liu. His coursework included Probability Theory (92), Programming Practices (87), and Pattern Recognition (88). He has developed strong expertise in AI, data science, and federated learning, positioning him for impactful contributions to academia and industry. 🎓📊📡

Experience 👨‍🏫

Changfan Pan has extensive experience in telecommunications and AI research. He interned at Zhongxing Telecom Equipment (2019), setting up 5G signal transceivers and deploying co-located base stations. At China Mobile (2020-2021), he maintained 2G/4G/5G networks, optimized signal coverage, and managed mobile cloud services. At Ericsson (2022), he developed backend servers using 5G protocols and designed performance test cases. His research includes developing a VR electromagnetic wave teaching platform, designing intelligent teaching assessment tools for rural education, and implementing federated knowledge graph completion systems. His technical expertise spans backend development, federated learning, and AI model interpretability. 📡🔍🤖

Research Interests 🔬

Changfan Pan’s research focuses on causal inference, synthetic data generation, and federated learning. He explores interpretable methods in knowledge tracing and stable attribution using local surrogate models. His work on synthetic data generation emphasizes fairness and privacy preservation. In federated learning, he designs decentralized frameworks for education applications, enhancing privacy-preserving knowledge graph completion. His published research includes papers in AAAI and IEEE ICDM, covering cross-domain multi-modality classification and trustworthy AI mechanisms. His projects include VR-based educational tools and intelligent assessment systems for rural teachers. His research aims to bridge AI advancements with practical applications in education and data privacy. 📊📖🔍

Awards & Recognitions 🏅

Changfan Pan has received multiple accolades for his contributions to AI and telecommunications. He was awarded the National Undergraduate Innovation and Entrepreneurship Training Program honor (2019). At China Mobile, he won First Prize for Wireless Network Maintenance Quality (2022). In AI competitions, he achieved a Bronze Medal (Top 6%) in a Kaggle Featured Code Competition (2023). Academically, he received the First-Class Scholarship at Zhejiang Normal University (2023). His consistent excellence in research, industry projects, and competitive AI challenges highlight his strong analytical and problem-solving skills. 🏅📜🎖️

Publications 📚

1. Changfan Pan, Jia Zhu, Qing Wang, Stable Attribution with Local Linear Surrogate Model, ThirtyEighth AAAI           Conference on Artificial Intelligence. (CCF-A)

2. Qing Wang, Jia Zhu, Changfan Pan, Yilong Ji, and Hanghui Guo, Dual trus