袁野

2022-09-22 11:10 作者: 审核: 浏览:

姓名:

袁野

性别:

       

学历:

博士

职称:

副教授

部门:

计算机科学系



邮件地址:

yuanyekl@swu.edu.cn、yuanyekl@gmail.com



研究方向:

数据挖掘,图表示学习







个人简介

副教授,硕士生导师,新时代三千名流·缙云英才优秀青年人才。主要研究方向为受扰图表示学习方向,在KDDWWWTNNLSTCYBTKDE等会议和期刊上发表SCI/EI检索论文42篇(ESI高引论文2篇)。主持包括国家自然科学基金面上项目和青年项目、国家重点研发子课题、重庆市人工智能重大专项课题、军科委项目等项目,累计经费超过700万元。申请国家发明专利10项,授权7项,在航天新通科技有限公司(隶属于航天科工集团)、猪八戒股份有限公司、国器智眸(重庆)科技有限公司(隶属于浪潮集团)实现了落地应用,产生直接经济效益2000余万元。获重庆市自然科学一等奖、吴文俊人工智能科技进步一等奖、重庆市科技进步一等奖、中国自动化大会最佳论文奖等奖励。目前担任IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man and Cybernetics: SystemsIEEE/CAA Journal of Automatica Sinica等多个SCI期刊的审稿人。

目前为学院领航团队“图与社会计算”团队成员,团队负责人为罗辛教授,团队简介链接:http://cis.swu.edu.cn/info/1037/2188.htm

教学情况

软件工程(本科)、机器学习(本科)、数据挖掘与仓库(本科)、机器学习与模式识别(硕士研究生)

科研情况

Ø 代表性论文

(1) Minglian Han, Ling Wang, Ye Yuan*(袁野), Xin Luo. SGD-DyG: Self-Reliant Global Dependency Apprehending on Dynamic Graphs, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025, 802-813. CCF-A类会议

(2) Jinli Li, Ye Yuan*(袁野), and Xin Luo. Learning Error Refinement in Stochastic Gradient Descent-based Latent Factor Analysis via Diversified PID Controllers, IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2025.3547854, 2025. IF=6.5,中科院JCR分区二区

(3) Jiufang Chen, Ye Yuan(袁野), Xin Luo*, and Xinbo Gao*. An Adaptive Neighborhood-Resonated Graph Convolution Network for Undirected Weighted Graph Representation, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2025.3589224. IF=8.9,中科院JCR分区一区

(4) Ye Yuan(袁野),Ying Wang, and Xin Luo*, A Node-Collaboration-Informed Graph Convolutional Network for Highly Accurate Representation to Undirected Weighted Graph, IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(6): 11507-11519. IF=8.9,中科院JCR分区一区

(5) Ye Yuan(袁野), Siyang Lu*, and Xin Luo*. A Proportional Integral Controller-Enhanced Non-negative Latent Factor Analysis Model, IEEE/CAA Journal of Automatica Sinica, 2025, 12(6): 1246-1259. IF=19.2,中科院JCR分区一区

(6) Ling Wang, Kechen Liu, Ye Yuan*(袁野). GT-A2T: Graph Tensor Alliance Attention Network, IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/JAS.2024.124863, 2024. IF=19.2,中科院JCR分区一区

(7) Ye Yuan(袁野)#, Jinli Li#, and Xin Luo*. A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis. IEEE Transactions on Fuzzy Systems, 2024, 32(7): 4049-4061. IF=11.9,中科院JCR分区一区

(8) Ye Yuan(袁野), Xin Luo*, and MengChu Zhou. Adaptive Divergence-based Non-negative Latent Factor Analysis of High-Dimensional and Incomplete Matrices from Industrial Applications. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(2): 1209-1222. IF=6.5,中科院JCR分区二区

(9) Jiufang Chen, Ye Yuan*(袁野), and Xin Luo*, SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks, IEEE/CAA Journal of Automatica Sinica, 2024, 11(7): 1717-1719. IF=19.2,中科院JCR分区一区

(10) Xin Luo*, Jiufang Chen, Ye Yuan(袁野), and Zidong Wang. Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis. IEEE Transactions on Systems Man Cybernetics: Systems, 2024, 54(4): 2213-2226. IF=8.7,中科院JCR分区一区

(11) Jinli Li, Xin Luo*, Ye Yuan(袁野), and Shangce Gao. A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis. IEEE Transactions on Automation Science and Engineering, 2024, 21(3): 3742-3756. IF=6.4,中科院JCR二区

(12) Jiufang Chen, Kechen Liu, Xin Luo*, Ye Yuan(袁野), Khaled Sedraoui, Yusuf Al-Turki, and MengChu Zhou*. A State-migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data, IEEE/CAA Journal of Automatica Sinica, 2024, 11(11): 2220-2235. IF=11.9,中科院JCR分区一区

(13) Ye Yuan(袁野), Xin Luo*, Mingsheng Shang, and Zidong Wang. A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data, IEEE Transactions on Cybernetics, 2023, 53(9): 5788-5801. IF=10.5,中科院JCR分区一区

(14) Ye Yuan(袁野), Renfang Wang*, Guangxiao Yuan, and Xin Luo. An Adaptive Divergence-based Non-negative Latent Factor Model. IEEE Transactions on System Man Cybernetics: Systems, 2023, 53(10): 6475-6487. IF=8.7,中科院JCR分区一区

(15) Ye Yuan#(袁野), Qiang He#, Xin Luo#,*, and Mingsheng Shang*. A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices, IEEE Transactions on Big Data, 2022, 8(3): 784-794. IF=5.7,中科院JCR分区二区,ESI高引

(16) Xin Luo, Ye Yuan(袁野), Sili Chen, Nianyin Zeng, and Zidong Wang. Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis, IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3958-3970. IF=10.4,中科院JCR分区一区,ESI高引

(17) Mingsheng Shang, Ye Yuan(袁野), Xin Luo*, and Mengchu Zhou. An α-β-divergence-generalized Recommender for Highly-accurate Predictions of Missing User Preferences, IEEE Transactions on Cybernetics, 2022, 52(8): 8006-8018. IF=10.5,中科院JCR分区一区

(18) Xin Luo#, Ye Yuan#(袁野), MengChu Zhou*, Zhigang Liu, and Mingsheng Shang*. Non-negative Latent Factor Model based on β-divergence for Recommender Systems. IEEE Transactions on System Man Cybernetics: Systems, 2021, 51(8): 4612-4623. IF=8.7,中科院JCR分区一区

(19) Ye Yuan(袁野), Xin Luo*, Mingsheng Shang, and Di Wu. A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems. The Web Conference, 2020, 498-507. CCF-A类会议

(20) Ye Yuan(袁野), Mingsheng Shang, and Xin Luo*. Temporal Web Service QoS Prediction via Kalman Filter-Incorporated Dynamic Latent Factor Analysis. European Conference on Artificial Intelligence, 2020, 561-568. CCF-B类会议

(21) Jiufang Chen#, Ye Yuan#(袁野), Tao Ruan#, Jia Chen, and Xin Luo*. Hyper-Parameter-Evolutionary Latent Factor Analysis for High-Dimensional and Sparse Data from Recommender Systems. Neurocomputing, 2020, 421: 316-328. IF=6.5,中科院JCR分区二区

(22) Jinli Li, Ye Yuan(袁野), Tao Ruan, Jia Chen, and Xin Luo*. A Proportional-Integral-Derivative-Incorporated Stochastic Gradient Descent-Based Latent Factor Analysis Model. Neurocomputing, 2020, 427: 29-39. IF=6.5,中科院JCR分区二区

(23) Ye Yuan(袁野), Xin Luo*, and Mingsheng Shang. Effects of Preprocessing and Training Biases in Latent Factor Models for Recommender Systems. Neurocomputing, 2018, 275: 2019-2030. IF=6.5,中科院JCR分区二区

Ø 主持参与科研项目

(1) 重庆市人工智能重大专项,汽车资讯智能服务关键技术与平台研发及应用(CSTB2025TIAD-STX0032),270万,2025.06-2028.06,主持(课题负责人)

(2) 国家重点研发计划项目,企业出海知识产权安全风险评估技术研发与应用((2024YFF0908200),2025.04-2028.0316万,主持(子课题负责人)

(3) 国家自然科学基金面上项目,面向新型配电网的异质图表示学习方法及应用研究(62372385),2024.01-2027.1250万,主持

(4) 国家电网公司总部科技项目,全球煤油气电耦合下我国能源安全风险识别与战略路径优化技术研究(1400-202357341A-1-1-ZN),2023.01-2024.1240万,主持(课题2负责人)

(5) 国家自然科学基金青年项目,基于用户动态兴趣的参数自适应推荐模型研究(62002337),

(6) 2021.01-2023.1224万,主持

(7) GFKJ创新项目,嵌入式XXXX检测技术(19-163-18-XX-003-XXX-04),2020.12-2021.1280万,主持

(8) 重庆市自然科学基金面上项目,基于用户动态兴趣的长效推荐模型研究(CSTB2022NSCQ-MSX1486),2022.08-2025.0710万,主持

(9) 企业委托横向,基于不完备张量低秩学习的机器视觉算法软件系统技术开发(202309091048YGL),2023.09-2024.11169.85万,主持


Ø 英文专著

(1) Ye Yuan(袁野), Xin Luo. Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach, Springer, 978-981-19-6703-0, 2022


Ø 国家发明专利

(1) 袁野,罗辛,尚明生,吴迪,一种视频数据多维非负隐特征的提取装置和方法,ZL201710930280.X,授权

(2) 袁野,李超华,罗辛,尚明生,吴迪,一种视频数据线性偏差主特征提取装置和方法,ZL201710895442.0,授权

(3) 袁野、许明、罗辛、尚明生,一种Web服务吞吐量时变隐特征分析装置和方法,ZL20201102649.7,授权

(4) 张能锋、袁野、罗辛、尚明生,一种基于多层随机隐特征模型的网页广告投放装置和方法,ZL202011012586.5,授权

(5) 赵涛、贾云健、周继华、温万里、袁野,无线传感网络主特征提取方法、系统、装置及存储介质,ZL202411041143.7,授权

(6) 袁野、罗辛、吴昊,一种基于广义动量的产品智能推荐装置和方法,202011042490.3,受理

杨大堂、袁野、罗辛,基于自增强图神经网络的电网设备异常检测装置和方法,2025100424914,受理


获奖情况

(1) 2024年,新时代三千名流·缙云英才优秀青年人才

(2) 2024年,重庆市自然科学一等奖:高阶非标张量网络计算理论与方法5/5

(3) 2024年,中国自动化大会最佳论文奖

(4) 2020年,重庆市科技进步一等奖:猪八戒网众创平台智能服务关键技术及应用,11/15

(5)2018年,中国人工智能学会吴文俊人工智能科技进步一等奖:智慧金融中的集成生物识别关键技术及应用,11/15

备注

工作经历:

2022.08~至今,西南大学,副教授

2013.07~2022.08,中国科学院重庆绿色智能技术研究院,助理研究员

学习经历:

2017.09~2022.01,中国科学院大学,计算机学院,博士

2010.09~2013.07,电子科技大学,电子信息工程学院,硕士

2006.09~2010.07,电子科技大学,电子信息工程学院,学士