一、个人简介
李朝博⼠，IET Fellow, 国家级人才，国科大杭州高等研究院产业教授，博士生导师，中国计算机学会杰出会员。主持和参与过多项国家⾃然科学基⾦和科技部重⼤科学研究计划，发表包括TPAMI等⼈⼯智能国际顶 级学术论⽂200余篇，荣获AAAI最佳应用论文奖，WWW最佳演示论文奖，IEEE-CCF服务计算最佳应用奖，荣获3个国际⼤赛(CIKM,WWW,OGB)冠军。曾获IEEE会议杰出领导⼒奖，IEEE开源科学奖，中国产学研合作创新和促进奖，中国科技新锐⼈物杰出成就奖，吴⽂俊中国⼈⼯智能科学技术进步奖，中国计算机学会科技进步奖杰出奖等多项荣誉, 多年入选Stanford 全球前 2% 顶尖科学家榜单。
二、研发方向
⦁AI新能源：锂电池大模型、端侧智能诊断
⦁图神经网络（GNN）：异常检测、图大语言模型
⦁智能物联网：端云协同、边缘计算、智能调度
三、代表性文章
[1] Huang C, Li M, Cao F, Li Z, et al. Are graph convolutional networks with random weights feasible?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(3): 2751-2768.
[2] Yang L, Chen H, Li Z, et al. Give us the facts: Enhancing large language models with knowledge graphs for fact-aware language modeling[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7): 3091-3110.
[3] Zhou S, Xu H, Zheng Z, ..., Li Z, et al. A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions[J]. ACM Computing Surveys, 2024, 57(3): 1-38.
[4] Li Z, Sun H, Xiong Z, et al. Noah: Reinforcement-learning-based rate limiter for microservices in large-scale e-commerce services[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 34: 5403-5417. 🏆 IEEE-CCF Best Application Paper
[5] Deng J, Li Z, Zhang J, et al. EGPlace: An efficient macro placement method via evolutionary search with greedy repositioning guided mutation[C]. International Conference on Machine Learning (ICML), 2025: 13237-13255.
[6] Li Z, Jiao Y, Shi Y, et al. RideSmart: Pre-trained large models for delivery route planning[C]. Companion Proceedings of the ACM on Web Conference 2025, 2025. 🏆 Best Demo Paper
[7] Ding D, Li Z, Luo L, et al. Large lithium-ion battery model for secure shared electric bike battery in smart cities[J]. Nature Communications, 2025, 16(1): 8415.
[8] Li H, Lo K M, Xuyang S, Wang Z, Zheng W, Zhang H, Li Z, et al. Mixture-of-experts can surpass dense LLMs under strictly equal resource[C]. The Fourteenth International Conference on Learning Representations (ICLR), 2026.
[9] Li Z, et al. LiBrain: LLM-powered li-ion battery diagnostics with time-series-aware retrieval-augmented framework for e-bikes[C]. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI/IAAI), 2026. 🏆 Best Application Award
[10] Li Z, et al. Talking trails: LLM-enhanced spatiotemporal trajectory modeling for e-bike delivery route planning[C]. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI/IAAI), 2026. 🏆 Best Application Award
