Key Research Areas
- Large Language Models and Generative AI
- Network Science and Graph Learning
- AI‑assisted Programming and Code Intelligence
- Educational Technology and AI in Education
- Health Informatics and AI in Public Health
- Trustworthy and Explainable AI
- Machine Learning and Data Science
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Core Areas of Expertise
Dr. HANG Ching Nam specialises in artificial intelligence (AI), data science, and network science, with a particular focus on large language models (LLMs), graph-based learning, and socially impactful AI applications. His research explores the development and application of intelligent systems for trustworthy information processing, AI-assisted programming, education, healthcare, and public health decision-making.
His recent work spans graph-based retrieval-augmented generation (GraphRAG) for fact-checking, LLM applications in personalized learning and code intelligence, graph-based machine learning, and trustworthy AI for public health. Through these research directions, Dr. Hang aims to develop scalable, trustworthy, and explainable AI technologies for education, health, and society.
Recent Publications
2026
- C. N. Hang, C. W. Tan and D. M. Chiu, LLM Teams: Harnessing Large Language Models as Multi-Agent Teammates for Joint Problem-Solving, ACM Conference on Learning at Scale (L@S '26), 2026.
2025
- C. N. Hang, P. -D. Yu and C. W. Tan, TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking, IEEE Transactions on Artificial Intelligence, Vol. 6, No. 11, pp. 3148-3162, 2025.
- C. N. Hang, P. -D. Yu, C. W. Tan and D. M. Chiu, When Ideas Go Viral: Measuring Scholarly Novelty and Viral Influence via Citation Network Analysis, IEEE Global Communications Conference (GLOBECOM), 2025.
- C. N. Hang, P. -D. Yu, C. W. Tan and D. M. Chiu, Beyond Search: Measuring LLM Performance for Scientific Literature Discovery, IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 2025.
- C. N. Hang and S. M. Ho, Personalized Vocabulary Learning through Images: Harnessing Multimodal Large Language Models for Early Childhood Education, IEEE Integrated STEM Education Conference, 2025.
2024
- C. N. Hang, P. -D. Yu, R. Morabito and C. W. Tan, Large Language Models Meet Next-Generation Networking Technologies: A Review, Future Internet, special issue on Featured Papers in the Section Internet of Things, Vol. 16, No. 10, 2024.
- C. N. Hang, C. W. Tan and P. -D. Yu, MCQGen: A Large Language Model-Driven MCQ Generator for Personalized Learning, IEEE Access, Vol. 12, pp. 102261-102273, 2024.
- C. N. Hang, P. -D. Yu and C. W. Tan, TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking, 58th Annual Conference on Information Sciences and Systems (CISS), 2024.
2023
- C. N. Hang, P. -D. Yu, S. Chen, C. W. Tan and G. Chen, MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management, IEEE Journal of Biomedical and Health Informatics, Vol. 27, No. 12, pp. 6100-6111, 2023.
- C. N. Hang, Y. -Z. Tsai, P. -D. Yu, J. Chen and C. W. Tan, Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review, Big Data and Cognitive Computing, special issue on Digital Health and Data Analytics in Public Health, Vol. 7, No. 2, 2023.
- M. F. Wong, S. Guo, C. N. Hang, S. W. Ho and C. W. Tan, Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review, Entropy, special issue on Statistical Machine Learning with High-Dimensional Data and Image Analysis, Vol. 25, No. 6, 2023.
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