Data Mining, Optimization Algorithms and Applications in the Era of Foundation Models

Dear Colleagues,

Foundation models—including large language models, vision transformers, and multi-modal architectures—have reshaped AI applications across language, image, and cross-modal tasks. However, in many domain-specific or low-resource scenarios—such as ancient script analysis, biomedical imaging, and scientific visualization—available data are often limited, highly structured, or weak in statistical regularity, making it difficult for large-scale pretraining to deliver reliable performance. Even the construction of vertical-domain foundation models faces challenges due to data scarcity and adaptation costs. These gaps underscore the continued relevance of classical and domain-adapted models—including CNNs, graph models, and other task-specific architectures—alongside optimization-driven approaches and data-centric learning. This Special Issue invites research that tackles complex, sparse, or structured data using innovative data mining techniques, optimization algorithms, and hybrid modeling strategies. We especially welcome studies that integrate algorithmic rigor with modern AI paradigms to enhance learning in domains where foundation models fall short or must be complemented.

Prof. Dr. Minghao Yin
Dr. Shuli Hu
Guest Editors

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