Guest Editor(s)
Dr. Shuai Zhao
Email: shuai.zhao@ntu.edu.sg
Affiliation: College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore
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Research Interests: large language models (LLMs), model applications, model safety, and intelligent healthcare
Dr. Luwei Xiao
Email: luwei.xiao@nus.edu.sg
Affiliation: College of Design and Engineering, National University of Singapore, Singapore, Singapore
Homepage:
Research Interests: large language models (LLMs), affective computing, multimodal learning, and AI for healthcare
Dr. Tiesunlong Shen
Email: tiesunlong@nus.edu.sg
Affiliation: National University of Singapore, Singapore, Singapore
Homepage:
Research Interests: natural language processing, large language model (LLM) reasoning, reinforcement learning, graph mining, and AI agents
Dr. Jianzhu Bao
Email: jianzhu.bao@ntu.edu.sg
Affiliation: Nanyang Technological University, Singapore
Homepage:
Research Interests: large language models, computational argumentation, multimodal learning
Summary
Large Language Models (LLMs) have rapidly become a transformative foundation across computational science, engineering, and intelligent systems. This Special Issue aims to bring together recent advances in model architectures, training paradigms, and scalable algorithms, alongside emerging directions in AI for Science, including but not limited to applications in vehicle scheduling, healthcare, and physical sciences.
In parallel, the issue will highlight crucial topics in model robustness, security, interpretability, and responsible AI. By integrating methodological breakthroughs with domain-driven applications, this Special Issue provides a comprehensive platform for researchers to explore the potential, limitations, and future directions of LLM-powered computational technologies.
Suggested Subtopics:
· LLM architectures, training, and scalable algorithms
· Domain-specific applications of LLMs
· AI for Science in biomedicine and physical sciences
· Robustness, security, and reliability of LLMs
· Interpretability and responsible AI
Keywords
large language models, AI for science, model security & robustness, multimodal modeling, simulation and prediction
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