正规赌足球的软件(中国)集团有限公司

个人简介

吴迪，工学博士，教授，正规赌足球的软件含弘研究员，中科院西部青年学者，美国路易斯安娜大学拉法叶分校访问学者，研究聚焦不完备流数据智能计算。主持国家自然科学基金面上和青年项目各1项、中科院人才项目1项、省部级项目2项、横向1项，累积负责科研经费260余万元；第一/通讯作者发表论文中科院一区9篇、二区5篇、CCF推荐会议4篇(含9篇IEEE Trans汇刊T-KDE、T-NNLS、T-SMC、T-SC、T-II，1篇国内代表中科院一区期刊IEEE/CAA JAS，1篇数据挖掘领域代表会议IEEE ICDM，2篇入选ESI高被引，累积影响因子大于100)；谷歌学术引用1700余次，H指数23，国际期刊Frontiers in   Neurorobotics副编辑(SCI,   IF 3.493)；获授权发明专利7项、软件著作权1项，参编行业标准1项、学术专著1本；获中国人工智能学会优秀博士论文提名奖、重庆市优秀博士学位论文、中国科学院经理优秀奖、首届川渝科技学术大会优秀论文三等奖(1/5)。更多信息请访问主页：https://wudi1989.github.io/Homepage/

教学情况

教授课程《智能决策分析》，主要从图学习的角度出发介绍如何针对图数据开展图表示学习和智能决策，包括图学习的基本概念、基本分析方法和最新相关研究进展。

科研情况

一、主持项目

[1]      2022.01-2025.12    国家自然科学基金面上项目       58万元    主持

[2]      2018.01-2020.12    国家自然科学基金青年基金       24万元    主持

[3]      2020.03-2022.12    中国科学院西部青年学者    15万元    主持

[4]      2019.08-2021.12    重庆市自然科学基金面上项目    10万元    主持

[5]      2016.01-2016.12    重庆市应用开发计划项目课题    重庆市科技局 75.5万元主持

[6]      2020.06-2020.12    能源战略演变模型开发研究 国网能源研究院有限公司59万元 主持

二、代表成果

[1]       Di   Wu, Xin   Luo, Yi He, and MengChu Zhou, A Prediction-sampling-based   Multilayer-structured Latent Factor Model for Accurate Representation of   High-dimensional and Sparse Data, IEEE Transactions on Neural Networks and   Learning Systems, 2022, 10.1109/TNNLS.2022.3200009. (中科院一区，IF   14.255)

[2]       Di   Wu, Peng   Zhang, Yi He, and Xin Luo, A Double-Space and Double-Norm Ensembled Latent   Factor Model for Highly Accurate Web Service QoS Prediction, IEEE   Transactions on Services Computing, 2022, doi: 10.1109/TSC.2022.3178543 (中科院一区，IF   11.019)

[3]       Di   Wu,   Qiang. He, Xin. Luo, Mingsheng. Shang, Yi. He, and Guoyin. Wang, A   posterior-neighborhood-regularized latent factor model for highly accurate   web service QoS prediction, IEEE Transactions on Services Computing, vol. 15,   no. 2, pp. 793-805, 2022. (中科院一区，IF 11.019, ESI 高被引)

[4]       Di   Wu, Xin   Luo, Mingsheng Shang, Yi He, Guoyin. Wang, and Xindong Wu, A   Data-Characteristic-Aware Latent Factor Model for Web Service QoS Prediction,   IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp.   2525-2538, 2022. (CCF-A期刊，IF 9.235)

[5]       Di   Wu, Yi   He, Xin Luo, and MengChu Zhou, A Latent Factor Analysis-based Approach to   Online Sparse Streaming Feature Selection, IEEE Transactions on Systems, Man,   and Cybernetics: Systems, 2021, DOI: 10.1109/TSMC.2021.3096065 (中科院一区, IF 11.471)

[6]       Di   Wu,   Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and MengChu Zhou, A Deep Latent   Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems,   IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 7,   pp. 4285-4296, 2021. (中科院一区, IF   11.471, ESI高引)

[7]       Di   Wu,   Mingsheng Shang, Xin Luo, and Zidong. Wang, An L₁-and-L₂-Norm-Oriented Latent   Factor Model for Recommender Systems, IEEE Transactions on Neural Networks   and Learning Systems, 2021, doi: 10.1109/TNNLS.2021.3071392. (中科院一区，IF   14.255)

[8]         Di   Wu   and Xin Luo, Robust Latent Factor Analysis for Precise Represen-tation of   High-dimensional and Sparse Data, IEEE/CAA Journal of Automatica Sinica, vol.   8, no. 4, 2021. DOI:   10.1109/JAS.2020.1003533. (中国科技期刊卓越行动计划世界一流重点建设期刊, IF=7.847，中科院一区)

[9]       Di   Wu,   Xin Luo, Guoyin Wang, Mingsheng Shang, Ye Yuan, and Huyong Yan, A   Highly-Accurate Framework for Self-Labeled Semi-Supervised Classification in   Industrial Applications, IEEE Transactions on Industrial Informatics, 2018,   14 (3): 909-920. (中科院一区, IF   11.648)

[10]    Di   Wu,   Long Jin, and Xin Luo, PMLF: Prediction-Sampling-based Multilayer-Structured   Latent Factor Analysis, In proceeding of the 2020 IEEE International   Conference on Data Mining, ICDM, 2020. (长文, 接受率9.8%, CCF-B会议，core-rank   A*)

[11]    Song   Deng, Jiantang Zhang, Di Wu*, Yi He, Xiangpeng Xie, and Xindong Wu, A   Quantitative Risk Assessment Model for Distribution Cyber Physical System   under Cyber Attack, IEEE Transactions on Industrial Informatics, 2022. DOI:   10.1109/TII.2022.3169456. (中科院一区, IF   11.648)

[12]    Bo   Sun, Di Wu*, Mingsheng Shang, and Yi He, Toward Auto-learning   Hyperparameters for Deep Learning-based Recommender Systems, International   Conference on Database Systems for Advanced Applications. Springer, Cham,   2022. (CCF-B会议)

[13]    Song   Deng, Fulin Chen, Di Wu*, Yi He, Hui Ge, Yuan Ge, Quantitative   combination load forecasting model based on forecasting error optimization,   Computers and Electrical Engineering, vol 101, pp.108125, 2022. (IF 4.152)

[14]    Di   Wu,   Minsheng Shang, Xin Luo, Ji Xu, Huyong Yan, Weihui Deng, and Guoyin Wang,   Self-training semi-supervised classification based on density peaks of data,   Neurocomputing, 2018, 275:180-191. (中科院二区, IF   5.779 )

[15]      Di   Wu,   Huyong Yan, Mingsheng Shang, Kun Shan, and Guoyin Wang, Water eutrophication   evaluation based on semi-supervised classification: A case study in Three   Gorges Reservoir, Ecological Indicators, 2017, 81: 362-372. (中科院二区, IF 6.263)

[16]      Di   Wu,   Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, A Data-Aware   Latent Factor Model for Web Service QoS Prediction, In proceeding of the 23rd   Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD, 2019. (CCF推荐会议, 接受率24.1%，core-rank A)

[17]      Di   Wu,   Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, Online Feature   Selection with Capricious Streaming Features: A General Framework, In   proceeding of the 2019 IEEE international conference on big data, Bigdata,   2019. (CCF推荐会议)

获奖情况

[1]        中国人工智能学会优秀博士论文提名奖，2021年度

[2]        重庆市优秀博士学位论文，2020年度

[3]        首届川渝科技学术大会优秀论文，三等奖，2020年度 

备注

[1]        《Frontiers in Neurorobotics》副编辑，SCI检索

[2]        《Frontiers in Computational Neuroscience》客座编辑，SCI检索[3]        《Symmetry-Basel》客座编辑，SCI检索

[4]        担任IEEE TNNLS、TSC、TITS、TII、TCSS、THMS等近20个国际期刊审稿人，AAAI 2023、ECML-PKDD 2021、2022程序委员会委员，IEEE ICNSC Session Chair。