浙江师范大学教师教育学院导师教师师资介绍简介-李明

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李明

主要任职 : 浙江省智能教育技术与应用重点实验室“双龙****”****
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 : 教师教育学院
入职时间 : 2019-11-15
联系方式 : Wechat: mingl7936
Email :

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个人简介李明, 博士, 浙江省钱江人才，浙江省智能教育技术与应用重点实验室“双龙****”****，博士生导师，博士毕业于澳大利亚拉筹伯大学 (La Trobe University) 计算机科学与信息技术系，DavidMyers Research 奖学金获得者，分别在澳大利亚拉筹伯大学数学与统计系、华南师范大学教育技术系完成两站博士后研究工作。

欢迎计算机/应用数学/教育技术学/统计学等相关专业的同学报考我的研究生，优秀毕业生依学生个人意愿可帮助推荐去海外攻读博士或从事博士后研究。
欢迎访问我的Google Scholar主页、ResearchGate主页 English Version Homepage

Recent News!!!
We are organizing a special issue on"Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications"in IEEE Transactions on Neural Networks and Learning Systems,Submission Deadline: July 31, 2021.[Call for Papers]
Guest Editors:
Ming Li, Zhejiang Normal University, China (Leading Guest Editor)
Alessio Micheli, University of Pisa, Italy
Yu Guang Wang, Max Planck Institute for Mathematics in the Sciences, Germany
Shirui Pan, Monash University, Australia (co-author of hot survey paper on GNNs) 图神经网络领域热点综述论文作者之一
Pietro Liò, University of Cambridge, UK (co-author of GAT）-图注意力网络作者之一
Giorgio Stefano Gnecco, IMT School for Advanced Studies, AXES Research Unit, Italy
Marcello Sanguineti, University of Genoa, Italy.

研究方向与兴趣
机器学习、深度学习（算法与理论）、图神经网络、图表示学习、神经网络随机训练算法、推荐系统、社交大数据挖掘、社交化学习数据分析（在线学习者情感分析，学情预测，知识追踪等）、图像处理、应用及计算调和分析（球面调和分析、小波分析、球面散乱数据插值与逼近）
论文选列：
[图神经网络与图表示学习相关代表作]
[1]Y. Song, H. Ye,M. Li, F. Cao,Deep multi-graph neural networks with attention fusion for recommendation, submitted toExpert Systems with Applications (SCI, 中科院一区，Top期刊)，2021.
[2] B. Zhou,X. Zheng,Y. G. Wang,M. Li,J. Gao,Embedding graphs on Grassmann manifold,submitted toThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases-ECMLPKDD(CCF B类会议)，2021

[3] B. Zhou,X. Zheng, Y. G. Wang, M. Li,J. Gao,Grassmann graph embedding,ICLR Workshop on Geometrical and Topological Representation Learning (GTRL), 2021. [Link]
[4]X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio(图注意力网络模型-GAT 作者之一), M. Li, G. Montufar, How Framelets Enhance Graph Neural Networks, submitted to ICML(CCF A类国际顶级会议), 2021.[Link]
[5]K. Yao, J. Liang (山西大学梁吉业教授，CCF会士), J. Liang,M. Li, F. Cao,GAN-GMC: Multi-Graph Representation Learning with GANs for Geometric Matrix Completion,submitted toIJACI(CCF A类国际顶级会议), 2021.[Link]
[6]X. Zheng, B. Zhou,M. Li, Y. G. Wang, and J. Gao,MATHNET: Haar-Like wavelet multiresolution-analysis for graph representation and learning,submitted toPattern Recognition(SCI，中科院一区，Top期刊), 2021.[Link]
[7]C. Huang,M. Li(通讯作者), F. Cao, H.Fujita, Z. Li, X. Wu,Are graph convolutional networks with random weights feasible?, submitted to IEEETransactions on Pattern Analysis and Machine Intelligence(SCI，中科院一区，Top期刊，CCF A类期刊), 2020.
[8]K. Yao, J. Liang (山西大学梁吉业教授，CCF会士), J. Liang,M. Li, F. Cao,Multi-view graph convolutional networks with attention mechanism, submitted toArtificial Intelligence (CCF A类期刊),2020.[Link]
[9]Z. Ma, J. Xuan, Y. G. Wang,M. Li, P. Lio (图注意力网络模型-GAT 作者之一),Path integral based convolution and pooling forgraph neural networks,NeurIPS (CCF A类国际顶级会议),2020，pp. 16421-16433.[Link][Code]
[10]Y. G. Wang,M. Li (通讯作者、共同一作),Z. Ma, G. Montufar, X. Zhuang, Y. Fan,Haar Graph Pooling,ICML (CCF A类国际顶级会议),2020, pp.9952-9962.[Link][Code]
[11]M. Li,Z. Ma, Y. G. Wang, X. Zhuang,Fast Haar transforms for graph neural networks,Neural Networks(SCI，国际神经网络协会会刊、欧洲神经网络协会会刊、日本神经网络协会会刊),vol. 128, pp. 188-198, 2020.[Link]
[12]Z. Ma,M. Li, Y. Wang, PAN: Path integral based convolution for deep graph neural networks, InWorkshop on Learning and Reasoning with Graph-Structured Representations,ICML(CCF A类国际顶级会议),2019. [Link]

--2021年投稿：
1. Y. Song, H. Ye,M. Li, F. Cao,Deep multi-graph neural networks with attention fusion for recommendation, submitted toExpert Systems with Applications (SCI, 中科院一区，Top期刊)，2021.
2. B. Zhou,X. Zheng,Y. G. Wang,M. Li,J. Gao,Embedding graphs on Grassmann manifold,submitted toThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases-ECMLPKDD(CCF B类会议)，2021
3.X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio(图注意力网络模型-GAT 作者之一),M. Li, G. Montufar, How Framelets Enhance Graph Neural Networks, submitted toICML(CCF A类国际顶级会议), 2021.[Link]
4. K. Yao, J. Liang (山西大学梁吉业教授，CCF会士), J. Liang,M. Li, F. Cao,GAN-GMC: Multi-Graph Representation Learning with GANs for Geometric Matrix Completion,submitted toIJACI2021(CCF A 类会议), 2021.
5.X. Zheng, B. Zhou,M. Li, Y. G. Wang, and J. Gao,MATHNET: Haar-Like wavelet multiresolution-analysis for graph representation and learning,submitted toPattern Recognition(SCI，中科院一区，Top期刊), 2021.[Link]
6.M. Li,P. Broadbridge, A. Olenko, Y. G. Wang, Fast tensor needlet transforms for tangent vector fields on the sphere, submitted toIEEE Transactions on Information Theory(SCI，中科院Top期刊, CCF A类期刊), 2021. [Link]
7.Y. Song, H. Ye,M. Li, F. Cao,Deep multi-graph neural networks with attention fusion for recommendation, submitted toExpert Systems with Applications (SCI, 中科院一区，Top期刊)，2021.

--2021年接收/发表：
8. B. Zhou,X. Zheng, Y. G. Wang, M. Li,J. Gao,Grassmann graph embedding,ICLR Workshop on Geometrical and Topological Representation Learning (GTRL),2021.[Link]
9. C. Huang,M. Li(通讯作者), D. Wang, Stochastic configuration network ensembles with selective base models, Neural Networks (SCI，中科院二区, CCF推荐人工智能与模式识别领域 B类期刊，国际神经网络协会会刊、欧洲神经网络协会会刊、日本神经网络协会会刊), vol.137,pp. 106-118, 2021.
10.Q. T. Le Gia,M. Li (通讯作者), Y. G. Wang, FaVeST: Fast vector spherical harmonic transforms, ACM Transactions on Mathematical Softwares(SCI，CCF 计算机科学理论领域 B类期刊), accepted,2021. [Link]，
11.Y. Liu, W. Yu,T. Dillon, W. Rahayu,M. Li (通讯作者),Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring, IEEE Transactions onIndustrial Informatics(SCI，中科院一区，Top期刊), accepted with minor revision,2021.
12. C. Huang, Z, Han, M. Li, X. Wang, and W. Zhao,Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis,AJET (SSCI, JCR: Q1), accepted, 2020.

--2020年投稿：
13.Z. Wang,M. Li(通讯作者), Y. Lu, Y. Bao, Z. Li, J. Zhao,Effective multiple pedestrian tracking system in video surveillance with monocular stationary camera, submitted toExpert Systems with Applications(SCI，中科院二区，Top期刊), 2020.
14. C. Huang, M. Li(通讯作者), F. Cao, H.Fujita, Z. Li, X. Wu,Are graph convolutional networks with random weights feasible?, submitted to IEEE TPAMI (SCI，中科院一区，Top期刊，CCF A类期刊), 2020.
15. K. Yao, J. Liang (山西大学梁吉业教授，CCF会士), J. Liang, M. Li, F. Cao,Multi-view graph convolutional networks with attention mechanism, submitted toArtificial Intelligence (CCF A类期刊),2020.[Link]
16.S. Sonoda, M. Li(通讯作者), F. Cao, C. Huang, Y. G. Wang, On the approximation lower bound for neural nets with random weights,submitted to IEEE TNNLS(SCI，中科院一区，Top期刊), 2020.[Link]

--2014-2020已发表（SCI 16篇-中科院一区6篇-ESI高被引1篇，EI 4篇-含CCF A类2篇，SSCI 2篇）：
17. Z. Ma, J. Xuan, Y. G. Wang,M. Li, P. Lio (图注意力网络模型-GAT 作者之一),Path integral based convolution and pooling forgraph neural networks,NeurIPS (CCF A类国际顶级会议)，2020，pp. 16421-16433.[Link]
18.Z. Wu,M. Li,Y. Tang, Q. Liang, Exercise recommendation based on knowledgeconcept prediction,Knowledge-Based Systems(SCI，中科院一区，Top期刊)，vol. 210, 106481,2020. [Link]
19.D. Fryer,M. Li, A. Olenko,rcosmo: R package for analysis of spherical, HEALPix and cosmological data,R Journal，vol. 12, no. 1, pp. 206-225, 2020. [Link]
20.Y. G. Wang,M. Li (通讯作者、共同一作),Z. Ma, G. Montufar, X. Zhuang, Y. Fan,Haar Graph Pooling,ICML (CCF A类国际顶级会议)，2020，pp.9952-9962.[Link][Code]
21.M. Li, Z. Ma, Y. G. Wang, X. Zhuang,Fast Haar transforms for graph neural networks,Neural Networks, vol. 128, pp. 188-198, 2020.(SCI收录，中科院二区,CCF推荐人工智能与模式识别领域 B类期刊，国际神经网络协会会刊、欧洲神经网络协会会刊、日本神经网络协会会刊)
22.Z. Ma, M. Li, Y. Wang, PAN: Path integral based convolution for deep graph neural networks, In Workshop on Learning and Reasoning with Graph-Structured Representations, The 36th International Conference on Machine Learning (ICML),2019.
23. M. Li, C. Huang, Q. Hu, J. Zhu, Y. Tang, Improved randomized learning algorithms for imbalanced and noisy educational data classification, Computing, pp. 1-15, 2019.(SCI)
24. M. Li, C. Huang, and D. Wang, Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression, Information Sciences, vol. 473, pp. 73-86, 2019.(SCI，中科院一区，Top期刊)
25.M. Li and D. Wang, 2-D stochastic configuration networks for imagedata analytics, IEEE Transactions on Cybernetics, vol. 51, no. 1, pp. 359-372, 2021.(SCI，中科院一区，Top期刊)
26. D. Wang, M. Li, Deep stochastic configuration networks with universal approximationproperty, in Proceedings of the International Joint Conference on Neural Networks, pp. 1-8, IEEE, 2018.(EI）
27. D. Wang, M. Li, Stochastic configuration networks: Fundamentals and algorithms,IEEE Transactions on Cybernetics, vol. 47, no. 10, pp. 3466-3479, 2017 (ESI Highly Cited Paper). （SCI，中科院一区，Top期刊，ESI高被引用论文）
28.M. Li, D. Wang, Insights into randomized algorithms for neural networks:Practical issues and common pitfalls, Information Sciences, vol. 382-383, pp.170-178, 2017.(SCI，中科院一区，Top期刊)
29. D. Wang, M. Li, Robust stochastic configuration networks with kernel densityestimation for uncertain data regression, Information Sciences, vol. 412-413, pp.210-222, 2017.(SCI，中科院一区，Top期刊)
30. F. Cao and M. Li, Spherical data fitting by multiscale moving least squares,Applied Mathematical Modelling, vol. 39, no. 12, pp. 3448-3458, 2015.(SCI，中科院二区)
31. Z. Chen, F. Cao, and M. Li, Scattered data quasi-interpolation on spheres,Mathematical Methods in the Applied Sciences, vol. 38, no. 12, pp. 2527-2536,2015.(SCI)
32.M. Li and F. Cao, Multiscale interpolation on the sphere: Convergence rate andinverse theorem, Applied Mathematics and Computation, vol. 263, pp. 134-150,2015.(SCI，中科院一区，Top期刊)
33. F. Cao, Y. Zhang, M. Li. Approximation by diffuse functional of generalized moving least squares on the sphere, Acta Mathematica Sinica (Chinese version), 57(3): 607-614, 2014.
34. M. Liand F. Cao, Local uniform error estimates for spherical basis functionsinterpolation, Mathematical Methods in the Applied Sciences, vol. 37, no. 9, pp.1364-1376, 2014.(SCI)
35. M. Li, F. Cao, and Z. Chen, The equivalent theorem for jackson-type operatorson spherical cap, Journal of Computational Analysis and Applications, vol. 15, no.4, pp. 778-787, 2013.(SCI)
36. C. Ding, M. Li,F. Cao, Pointwise error estimates for spherical hybrid interpolation,Journal of Computational Analysis and Applications, vol. 26, no. 1, pp. 77-84, 2019.(SCI)
37. C. Ding, M. Li,F. Cao, Lp approximation errors for hybrid interpolation on theunit sphere, Journal of Computational Analysis and Applications, vol. 27, no. 1, pp.104-117, 2019.(SCI)
38. Y. Tan, C. Deng, X. Wang, M. Shi., X, Bai, M. Li,Urban computing for smart city based on space-time load distribution information, in Proceedings of the2nd IEEE Conference on Energy Internet and Energy System Integration, pp. 1-6, IEEE, 2018.(EI)
39. T. He, C. Huang, M. Li, Y. Zhou, S. Li, Social participation of the elderly in China: The roles of conventional media, digital access and social media engagement,Telematics and Informatics,vol. 48, 101347, 2020.(SSCI，JCR Q1)
40. J. Yu, C. Huang, Z. Han, T. He, M. Li, Investigating the influence of interaction on learning persistence in online settings: Moderation or mediation of academic emotions?International Journal of Environmental Research and Public Health, vol. 17, no. 7, 2320, 2020.(SSCI，JCR Q2)

专利：
1. 李明，黄昌勤，张捷，朱佳，基于图卷积神经网络和动态权重的课程推荐方法和系统，2019，发明专利，已受理；
2. 李明，张林，黄昌勤，梁吉业，社交数据的分析方法、系统及存储介质，2021，发明专利，已受理；
3.李明，陈逸璇，黄昌勤，梁吉业，基于多模态图神经网络的语义分析方法、系统和存储介质，2021，发明专利，已受理；
4. 黄昌勤，黄琼浩，李明，王希哲，何涛，基于图卷积的知识追踪数据处理方法、系统和存储介质，2020，发明专利，已受理。

科研项目
1. 2020-2021，中国-意大利 (国际理论物理中心-国家高等数学研究院ICTP-INdAM)-"Research in Pairs"项目 (合作者Giorgio Stefano Gnecco, IMT School for Advanced Studies, AXES Research Unit, Italy；Marcello Sanguineti, University of Genoa, Italy, IEEE TNNLS 副编辑)
2. 2019-2021，国家自然科学基金青年项目，主持，在研；
3. 2020-2022, 浙江省"钱江人才"计划，主持，在研；
4. 2018-2019，中国博士后科学基金特别资助，主持，已结题；
5. 2018-2019，中国博士后科学基金一等资助，主持，已结题

教育经历：
1. 2013.12-2017.9, 澳大利亚拉筹伯大学，计算机科学与信息技术系，工学博士;
2. 2010.9-2013.7, 中国计量大学，应用数学系，理学硕士；
3. 2006.9-2010.7, 山东师范大学，信息与计算科学系，理学学士

工作经历：
1. 2019.11-至今, 浙江师范大学，教师教育学院，“双龙****”****、博导；
2.2019.7-2019.9, 澳大利亚新南威尔士大学，数学与统计系，访问****；
3. 2017.12-2019.11, 华南师范大学，教育信息技术学院，博士后；
4. 2017.7-2018.12, 澳大利亚拉筹伯大学，数学与统计系，访问****；

所获荣誉
1. 浙江师范大学“双龙****”****（2019.11）；
2.华南师范大学优秀出站博士后（2019.11）；
3.IEEE Transactions on Cybernetics 优秀审稿专家（2016，2017）
4. 澳大利亚拉筹伯大学优秀博士论文提名（2017）；
5. 澳大利亚拉筹伯大学 David Myers全额奖学金（2013-2017）；
6. 中国计量大学优秀硕士毕业论文（2014）

学术活动及相关服务
1.中国计算机协会(China Computer Federation，CCF) 会员
2. 中国人工智能学会(Chinese Association for Artificial Intelligence，CAAI) 会员
3. 中国人工智能学会---知识工程与分布智能专委会委员
4. The Institute of Electrical and Electronics Engineers (IEEE) 会员
5. Australian Mathematical Society, 澳大利亚数学会会员
6. 国际知名期刊及国际顶级会议审稿人
IEEETransactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Cybernetics
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions onIndustrial Informatics
Information Sciences
Neural Networks
Neurocomputing
Applied Soft Computing
Applied Mathematical Modelling
WIREs Data Mining and Knowledge Discovery
International Journal of Machine Learning and Cybernetics
ICML, NeurIPS, ICLR (CFF A 类会议)

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浙江师范大学教师教育学院导师教师师资介绍简介-李明

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