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    李明

    • 主要任职 : 浙江省全省智能教育技术与应用重点实验室副主任、教授、博士生导师
    • 曾获荣誉 : 浙江省高校高层次拔尖人才(第二批“浙江省高校领军人才计划”培养人选,即“5246人才工程”),浙江省“钱江人才计划”急需紧缺型人才,第三届全国高校教师教学创新大赛浙江省二等奖(新工科正高组)
    • 性别 : 男
    • 毕业院校 : 澳大利亚拉筹伯大学
    • 学历 : 博士研究生毕业
    • 学位 : 博士学位
    • 在职信息 : 在岗
    • 所在单位 : 教育学院
    • 入职时间 : 2019-11-15
    • 联系方式 : Wechat: mingl7936
    • Email :

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    个人简介

    教授博士生导师,入选浙江省高校高层次拔尖人才、浙江省"钱江人才计划"特殊急需人才,浙师大"双龙学者"特聘教授,浙江省全省智能教育技术与应用重点实验室副主任,金华市青年科学技术协会理事、副秘书长,担任国际神经网络协会会刊 Neural Networks (中科院一区TOP/CCF B类,影响因子: 7.8) 、Applied Intelligence (中科院二区/CCF C类,影响因子: 5.3)Alexandria Engineering Journal (中科院二区TOP,影响因子: 6.8) Network: Computation in Neural Systems (中科院三区,影响因子: 7.8)Soft Computing (CCF C类,影响因子: 4.1) 、Neural Processing Letters (CCF C类,影响因子: 3.1) 六个国际权威期刊副主编,担任教育技术学国际权威期刊Education and Information Technologies (中科院二区TOP,影响因子: 5.5)编委,曾任IEEE Transactions on Neural Networks and Learning Systems (中科院一区TOP,影响因子: 10.4Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications专刊首席特邀副主编在国际权威期刊IEEE TPAMI (人工智能全球TOP1期刊)、AI、IEEE TKDEIEEE TNNLS、IEEE TCYB、Information Fusion、Computers & Education (教育学全球TOP1期刊)BJET  CCF A 国际顶级会议ICML,NeurIPSIJCAI上发表论文70余篇,主持国家级及省部级纵向项目7项(含省重点研发项目1项),申请国家发明专利20余项,作为合作单位项目负责人联合承担国家自然科学基金区域创新发展联合基金重点项目1项,作为核心成员参与国家首批新文科研究与改革实践项目1项,浙江省重点研发计划项目2 项,曾参与国家自然科学基金重大研究计划培育项目1 项、国家自然科学基金面上项目2 项。曾获第三届全国高校教师教学创新大赛浙江省二等奖 (新工科正高组),指导学生获“华为杯”第五届中国研究生人工智能创新大赛三等奖。   


    论文列表详见我的英文主页English Version Homepage

                

    🔆 图神经网络、图表示学习、几何深度学习等方向代表作(含CCF A类期刊/会议11篇

     Are graph convolutional networks with random weights feasible? ESI Highly Cited Paper 🏆

       C. Huang, M. Li*, F. Cao, H. Fujita, Z. Li, X. Wu
       
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2751-2768, 2023.

     ● Multi-view graph convolutional networks with attention mechanism  [code]

       K. Yao, J. Liang, J. Liang, M. Li, F. Cao
     
     Artificial Intelligence, vol. 307, 103708, 2022.

    ● HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification 

       L. Bai, L. Cui, Y. Wang, M. Li*, J. Li, P. Yu, E. Hancock 

       IEEE Transactions on Knowledge and Data Engineering2024, DOI: 10.1109/TKDE.2024.3389966.

     ● Collaborative knowledge graph fusion by exploiting the open corpus 

        Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, M. Li, P. Yu, E. Hancock 

       IEEE Transactions on Knowledge and Data Engineeringvol. 36, no. 2, pp. 475-489, 2024.

    ● EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides  
       
    M. Li, S. Zhou, Y. Chen, C. Huang, Y. Jiang*

       Information Fusionvol. 109, 102428, 2024.

    ● Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction  

       M. Li, X. Zhuang, L. Bai, W. Ding

       Information Fusionvol. 105, 102224, 2024.

    ● How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing 

       K. Huang*, Y. G. Wang, M. Li*, P. Lio
      
     ICML, 2024.

    ● QBMK: Quantum-based matching kernels for un-attributed graphs

       L. Bai, L. Cui, M. Li, Y. Wang, E. Hancock

       ICML, 2024.

    ● Haar graph pooling [link][code]

       Y. G. Wang, M. Li*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan
     
     ICML, 2020, pp.9952-9962.

    ● How framelets enhance graph neural networks [link] [code]

       X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar
     
     ICML, 2021, pp. 12761-12771.

    ● How powerful are shallow neural networks with bandlimited random weights?

       M. Li, S. Sonoda, F. Cao, Y. G. Wang, J. Liang
       
    ICML, 2023, pp. 19960-19981.

    ● Path integral based convolution and pooling for graph neural networks [code] [PyG Implementation]

       Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio
       
    NeurIPS, 2020, pp. 16421-16433.

    ● Stability and generalization of ℓp-regularized stochastic learning for GCN 

       S. Lv, S. Liu, L. Wei, M. Li
       
    IJCAI, 2023, pp. 5685-5693.

    ● Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications [link

       M. Li, A. Micheli, Y. G. Wang, S. Pan, P. Lio, G. Gnecco, M. Sanguineti

       IEEE Transactions on Neural Networks and Learning Systemsvol. 35, no. 4, pp. 4367-4372, 2024.

    ● Permutaion equivariant graph framelets for heterophilous graph learning [code]

       J. Li, R. Zheng, F. Han, M. Li*, X. Zhuang*

       IEEE Transactions on Neural Networks and Learning Systems, 2024, DOI: 10.1109/TNNLS.2024.3370918.

    ● Towards graph self-supervised learning with contrastive adjusted zooming 

       Y. Zheng, M. Jin, S. Pan, Y. F. Li, H. Peng, M. Li, Z. Li

       IEEE Transactions on Neural Networks and Learning Systems, 2022, DOI: 10.1109/TNNLS.2022.3216630.

    ● A simple yet effective framelet-based graph neural network for directed graphs [code]

       C. Zou, A. Han, L. Lin, M. Li, J. Gao

       IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1647-1657, 2024.

    ● Multiple pedestrian tracking with graph attention map on urban road scene 

       Z. Wang, Z. Li, J. Leng, M. Li*, L. Bai

       IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8567-8579, 2023.

    ● GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification 

       B. Yang, H. Ye, M. Li*, F. Cao, S. Pan

       IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023.

    ● Fast Haar transforms for graph neural networks 

       M. Li, Z. Ma, Y. G. Wang, X. Zhuang
       
    Neural Networks, vol. 128, pp. 188-198, 2020.

     Embedding graphs on Grassmann manifold [code]

       B. Zhou, X. Zheng, Y. G. Wang, M. Li, J. Gao

       Neural Networks, vol. 152, pp. 322-331, 2022.

    ● Revisiting graph neural networks from hybrid regularized graph signal reconstruction 

       J. Miao, F. Cao, H. Ye, M. Li, B. Yang

       Neural Networks, vol. 157, pp. 444-459, 2023.

    ● BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation 

       M. Li, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu

       Pattern Recognition, vol. 144, 109874, 2023.

    ● QBER: Quantum-based entropic representations for un-attributed graphs 

       L. Cui,  M. LiL. Bai, Y. Wang, J. Li, Y. Wang,  Z. Li, Y. Chen,  E. Hancock

       Pattern Recognitionvol. 145, 109877, 2024.

    ● AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs 

       Y. Wang, C. Huang, M. Li, Q. Huang, X. Wu, J. Wu

       Pattern Recognition, vol. 151, 110387, 2024.

    ● Triplet teaching graph contrastive networks with self-evolving adaptive augmentation [code]

       J. Miao, F. Cao, M. Li, B. Yang, H. Ye

       Pattern Recognitionvol. 142, 109687, 2023.

    ● MATHNET: Haar-Like wavelet multiresolution analysis for graph representation and learning 

       X. Zheng, B. Zhou, M. Li*, Y. G. Wang*, and J. Gao

       Knowledge-Based Systems, vol. 273, 110609, 2023.

    ● A disentangled linguistic graph model for explainable aspect-based sentiment analysis  

       X. Mei, Y. Zhou, C. Zhu, M. Wu, M. Li*, S. Pan

       Knowledge-based Systems, vol. 260, 110150, 2023.


    🔆 AI赋能教育技术与应用(交叉)向代表作

      EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides  

       M. Li, S. Zhou, Y. Chen, C. Huang, Y. Jiang*

       Information Fusionvol. 109, 102428, 2024.

      Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction  

       M. Li, X. Zhuang, L. Bai, W. Ding*

       Information Fusionvol. 105, 102224, 2024.

      EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation  

       M. Li, Z. Li*, C. Huang*, Y. Jiang, X. Wu

       IEEE Transactions on Big Data2024.

     Framelet based dual hypergraph neural networks for student engagement prediction [poster] [Best Short Paper Award]

       M. Li*, J. Shi

       AI4ED-AAAI, 2024, accept. Best Short Paper Award 🏆


    🔆 教育技术学实证研究方向代表作

     From motivational experience to creative writing: A motivational AR-based learning approach to promoting Chinese  writing performance and positive writing behaviours

       M. Li, Y. Chen, C. Huang, G. Hwang, M. Cukurova

       Computers & Education, vol. 202, 104844, 2023. (SSCI&SCI中科院一区TOP,影响因子: 12.0,教育学全球TOP1期刊) ▪ Journal Citation Reports (Clarivate, 2023): 1/269 (Education & Educational Research (Social Science))

     Incorporation of peer-feedback into the pedagogical use of spherical video-based virtual reality in writing education 

       Y. Chen, M. Li*, M. Cukurova, M. Jong

       British Journal of Educational Technology,  vol. 55, no. 5, pp. 519-540, 2024. (SSCI中科院一区TOP,影响因子6.6,教育技术学国际顶级期刊) ▪ 12/269 (Education & Educational Research (Social Science))

    ● Promoting deep writing with immersive technologies: An SVVR‐supported Chinese composition writing approach for primary schools 

       Y. Chen, M. Li*, C. Huang, Z. Han, G. Hwang, G. Yang

       British Journal of Educational Technology, vol. 53, no. 6, pp. 2071-2091, 2022.  (SSCI中科院一区TOP,影响因子: 6.6,教育技术学国际顶级期刊)

    ● Unleashing imagination: An effective pedagogical approach to integrate into spherical video-based virtual reality to improve students' creative writing 

       Y. Chen, M. Li*, M. Cukurova

       Education and Information Technologiesvol. 29, pp. 6499-6523, 2024. (SSCI中科院二区TOP,影响因子: 5.5)

    ● A systematic review of research on immersive technology-enhanced writing education: The current state and a research agenda

       Y. Chen, M. Li*, C. Huang, M. Cukurova, Q. Ma

       IEEE Transactions on Learning Technologies, vol. 17, pp. 919-938, 2024.  (SSCI&SCI影响因子: 3.7)



    🔆 学术活动及相关服务

    国际权威期刊副主编(Associate Editor)、编委(Editorial Board Member)

        ▪ Neural Networks 副主编

        ▪ Applied Intelligence 副主编

        ▪ Alexandria Engineering Journal 副主编

        ▪ Network: Computation in Neural Systems 副主编

        ▪ Soft Computing 副主编

        ▪ Neural Processing Letters 副主编

        ▪ Education and Information Technologies 编委

    ● 中国计算机协会 (China Computer Federation,CCF) 会员

    ● 中国人工智能学会 (Chinese Association for Artificial Intelligence,CAAI) 会员

    ● 中国人工智能学会——机器学习专委会通讯委员

    ● 中国人工智能学会——知识工程与分布智能专委会委员

    ● The Institute of Electrical and Electronics Engineers (IEEE) 会员

    ● Australian Mathematical Society (AusMS), 澳大利亚数学会会员

    ● PC Members for AAAI (2022)、ICML (2020-2024), NeurIPS (2020-2023), ICLR (2022-2024)、IJCAI(2023)、LoG (2022, 2023)、KDD (2022、2024)、WSDM (2022-2024)、ICASSP (2023)、AJCAI (2021)、NCIIP (2021、2023)

    期刊审稿人(审稿篇数400+)

        IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 23.6)

       ▪ IEEE Transactions on Neural Networks and Learning Systems (Impact Factor: 10.4)

       ▪ IEEE Transactions on Cybernetics (Outstanding Reviewer in 2016 and 2017) (Impact Factor: 11.8)

       ▪ IEEE Transactions on Knowledge and Data Engineering (Impact Factor: 8.9)

       ▪ IEEE Transactions on Intelligent Transportation Systems (Impact Factor: 8.5)

       ▪ IEEE Journal on Selected Areas in Communications (Impact Factor: 16.4)

       ▪ IEEE Transactions on Big Data (Impact Factor: 7.2)

       ▪ IEEE Journal of Selected Topics in Signal Processing (Impact Factor: 7.5)

       ▪ IEEE Transactions on Affective Computing (Impact Factor: 11.2)

       ▪ IEEE Computational Intelligence Magazine (Impact Factor: 9.0)

       ▪ IEEE Transactions on Emerging Topics in Computational Intelligence (Impact Factor: 5.5)

       ▪ IEEE Transactions on Network Science and Engineering (Impact Factor: 6.6)

       ▪ IEEE Transactions on Neural Systems & Rehabilitation Engineering (Impact Factor: 4.9)

       ▪ IEEE Transactions Services Computing (Impact Factor: 8.1)

       ▪ IEEE Transactions Software Engineering (Impact Factor: 7.4)

       ▪ IEEE Transactions on Mobile Computing (Impact Factor: 7.9)

       ▪ IEEE Intelligent Systems (Impact Factor: 6.4)

       ▪ IEEE Signal Processing Letters (Impact Factor: 3.9)

       ▪ IEEE Transactions on Artificial Intelligence

       ▪ IEEE/CAA Journal of Automatica Sinica (Impact Factor: 11.8)

       ▪ ACM Transactions on Knowledge Discovery from Data (Impact Factor: 4.4)

       ▪ Artificial Intelligence Review (Impact Factor: 12.0)

        SCIENCE CHINA Information Sciences (Impact Factor: 8.8)

       ▪ Neural Networks (Impact Factor: 7.8)

       ▪ Pattern Recognition (Impact Factor: 8)

       ▪ Neurocomputing (Impact Factor: 6.0)

       ▪ Information Sciences (Impact Factor: 8.1)

       ▪ Expert Systems with Applications (Impact Factor: 8.5)

       ▪ Knowledge-Based Systems (Impact Factor: 8.8)

       ▪ Applied Intelligence (Impact Factor: 5.3)

       ▪ Applied Soft Computing (Impact Factor: 8.7)

       ▪ Soft Computing (Impact Factor: 4.1)

       ▪ International Journal of Machine Learning and Cybernetics (Impact Factor: 5.6)

       ▪ British Journal of Educational Technology (Impact Factor::6.6)

       ▪ Education and Information Technologies (Impact Factor::5.5)



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