研究方向
招收2025年硕士研究生2人，欢迎对计算机视觉和人工智能交叉应用感兴趣的同学联系我 个人简介： 信息与机电工程学院讲师，硕士生导师，ACM/CCF/CAA/IEEE学会会员，网络信息服务专业委员会秘书。主要研究方向是Petri网理论及应用、图神经网络、大语言模型和人工智能教育等。2023年毕业于同济大学嵌入式系统与服务计算教育部重点实验室，获工学博士学位，导师为中国工程院院士蒋昌俊教授。曾在IEEE TSMC、IEEE TASE、IEEE TNNLS、IJCAI等国际高水平学术期刊和会议上发表科研成果10余篇，其中中科院top期刊3篇，CCF A类会议1篇，CCF B类期刊1篇。授权国家发明专利2项，参与国家重点研发专项2项，上海市高新技术领域项目1项，主持校级项目1项。 学历： (1) 2018-09 至 2023-12, 同济大学, 计算机科学与技术, 博士 导师：蒋昌俊教授 (2) 2015-09 至 2018-06, 山东科技大学, 计算机软件与理论, 硕士（推荐免试） 导师：杜玉越教授 (3) 2011-09 至 2015-06, 山东科技大学, 计算机科学与技术, 学士
工作经历： (1) 2024-04 至今, 上海师范大学, 信息与机电工程学院, 讲师 学术主页： 谷歌学术 researchgate ORCiD Github 代表成果： [2] H. Qi, M. Guang, J. Wang, C. Yan, and C. Jiang, “Probabilistic reachability prediction of unbounded petri nets: A machine learning method,” IEEE Transactions on Automation Science and Engineering, vol. 21, no. 3, pp. 3012–3024, 2024. (SCI，中科院二区, TOP期刊，影响因子5.9) [3] H Qi, C Jiang*. A Perspective on Petri Net Learning. Frontiers of Computer Science, 2023,17(6), 176351. (CCFB类期刊, 影响因子4.2) [4] P Wu, H Qi, S. Huang, D An, J Lian, and Q Zhao*, Wave-driven Graph Neural Networks with Energy Dynamics for Over-smoothing Mitigation, in Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-25), 2025. (已录用，共同一作，CCF-A类会议) [5] J Wang, H Qi, M Guang, C Zhang, C Yan, C Jiang*. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(12): 7380-7389. (SCI，中科院一区, TOP期刊，影响因子14.255) 2025年： [1] P Wu, H Qi, S. Huang, D An, J Lian, and Q Zhao*, Wave-driven Graph Neural Networks with Energy Dynamics for Over-smoothing Mitigation, in Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-25), 2025. (已录用，共同一作，CCF-A类会议) [2] Q. Zhao, G. Yang, Y. Miao, J. Lian, H. Qi*, and Z. Kou, Adaptive Sampling-based Heterogeneous Graph Enhancement, Computing and Informatics. (已录用) [3] Q. Zhao, J. Liu, X. Yang, H. Qi, and J. Lian,“Spatiotemporal pm2.5 forecasting via dynamic geographical graph neural network,” Environmental Modelling & Software, vol. 186, p. 106351, 2025. [4] D. An, Y. Yang, X. Gao, H. Qi, Y. Yang, X. Ye,M. Li, and Q. Zhao, “Reinforcement learning-based secure training for adversarial defense in graph neural networks,”Neurocomputing, vol. 630, p. 129704, 2025. 2024年： [1] H. Qi, J. Wang, C. Yan, and C. Jiang, “The probabilistic liveness decision method of unbounded petri nets based on machine learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 1070–1081, 2024.(SCI, 中科院一区, TOP期刊, 影响因子8.7) [2] H. Qi, M. Guang, J. Wang, C. Yan, and C. Jiang, “Probabilistic reachability prediction of unbounded petri nets: A machine learning method,” IEEE Transactions on Automation Science and Engineering, vol. 21, no. 3, pp. 3012–3024, 2024.(SCI，中科院二区, TOP期刊，影响因子5.9) [3] R. Duan, M. Guang, J. Wang, C. Yan, H. Qi, W. Su, C. Tian,and H. Yang, “Unifying homophily and heterophily for spectral graph neural networks via triple filter ensembles,” in Advancesin Neural Information Processing Systems, vol. 37, 2024.(CCF A类会议) [4] D. An, Y. Yang, W. Liu, Q. Zhao, J. Liu, H. Qi, and J. Lian,“A secure gnn training framework for partially observable graph,” Electronics, vol. 13, no. 14,p. 2721, 2024. 2023年： [1] H. Qi and C. Jiang, “A perspective on petri net learning,”Frontiers of Computer Science, vol. 17, no. 6, p. 106351 2023.(CCFB类期刊, 影响因子4.2) 科研项目: [1]上海师范大学校级一般科研项目:机器认知智能驱动的智能制造动态建模方法研究，2025-2027,在研，项目负责人. 欢迎各年级优秀研究生和本科生加入上海师范大学智能大数据与物联网实验室！
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