黄黎明，博士毕业于英国布里斯托大学电气与电子工程专业。主要研究方向为多模态智能检测，光谱数据处理与通用大模型，数字孪生，原生AI智能6G，伦理人工智能等。累计发表论文26篇，第一作者发表论文10余篇，ESI高被引论文5篇。参与多项英国科技创新部资助的研究项目（总经费超3亿人民币），以及国家自然科学基金项目。长期担任IEEE TII, IEEE JSAC, IEEE TMM, IEEE TCSVT, IEEE TSMC, IEEE TCYBER, IEEE JAS, IEEE TNSM, IEEE TNSE, IEEE TVT, IEEE Communications Magazine, IEEE Network, IEEE SPL, Information Fusion, KBS, EAAI, PR审稿人。

详细论文发表情况请参阅：谷歌学术主页

代表性论文：

L. Huang, Y. Wu, D. Simeonidou. Large Models Empowered End-to-End AI for Split Learning in Native AI Future Networks. IEEE Transactions on Network Science and Engineering, 2026.

L. Huang, Y. Wu, D. Simeonidou. Optimizing System Performance for Autonomous Recovery in AI-Native Networks Using Large Language Models. IEEE Transactions on Cognitive Communications and Networking, 2026.

L. Huang, A. Gong. A fuzzy feature integration-enhanced network for surface defect detection of no-service rails. Computers in Industry, 173:104382, 2025.

L. Huang, Y. Wu. Ethically Responsible Decision Making for Anomaly Detection in Complex Driving Scenes. Information Fusion, 103226, 2025.

L. Huang, A. Gong. Trigonometric feature learning for RGBD and RGBT image salient object detection. Knowledge-Based Systems, 310:112935, 2025.

L. Huang, Y. Wu, D. Simeonidou. Reasoning AI Performance Degradation in 6G Networks with Large Language Models. IEEE Wireless Communications and Networking Conference (WCNC), pp.1-6, 2025.

L. Huang, Y. Wu, J.M. Parra-Ullauri, R. Nejabati, D. Simeonidou. AI model placement for 6G networks under epistemic uncertainty estimation. IEEE International Conference on Communications (ICC), pp.5521-5526, 2024.

L. Huang, A. Gong. Surface defect detection for no-service rails with skeleton-aware accurate and fast network. IEEE Transactions on Industrial Informatics, 20(3):4571-4581, 2023.

L. Huang, Y. Wu, N. Tempini. A Knowledge Flow Empowered Cognitive Framework for Decision Making With Task-Agnostic Data Regulation. IEEE Transactions on Artificial Intelligence, 5(5):2304-2318, 2023.

L. Huang, K. Song, A. Gong, Y. Yan. Multiple Graph Affinity Interactive Network and A Variable Illumination Dataset for RGBT Image Salient Object Detection. IEEE Transactions on Circuits and Systems for Video Technology, 2022.

L. Huang, K. Song, J. Wang, M. Niu, Y. Yan. Multi-graph fusion and learning for RGBT image saliency detection. IEEE Transactions on Circuits and Systems for Video Technology, 32(3):1366-1377, 2021.

L. Huang, K. Song, A. Gong, C. Liu, Y. Yan. RGB-T saliency detection via low-rank tensor learning and unified collaborative ranking. IEEE Signal Processing Letters, 27:1585-1589, 2020.