教师主页移动版

个人经历 personal experience 工作经历 教育经历 2015年4月至2018年6月，西北工业大学，电子信息学院，系统与控制工程系，讲师2016年11月至2016年12月，韩国仁川大学，多媒体视频研究所，访问学者2018年7月至2023年9月，西北工业大学，电子信息学院，副教授2017年7月至今，西北工业大学，电子信息学院，电子科学与技术专业，硕士生导师2021年7月至今，西北工业大学，电子信息学院，电子科学与技术专业，博士生导师2023年10月至今，西北工业大学，电子信息学院，教授 2005.08～2009.07 就读于西安电子科技大学电子工程学院生物医学工程专业，获工学学士学位 2013.08～2014.08 赴荷兰莱顿大学进阶计算机科学研究所交流访问 2009.08～2015.03 就读于西安电子科技大学电子工程学院学院电路与系统专业，获工学博士学位

教育教学

科学研究 Scientific Research 研究方向：深度学习模型优化，大数据深度学习与应用，图像和信号处理，进化算法优化及应用，遥感影像解译与分析。联系方式：jiaoshi@nwpu.edu.cn科研项目：参与欧洲联盟第7框架国际合作项目（自然计算及其应用，Nature inspired computation and its applications, NICaiA）。主持科研项目国家级 4 项、省部级 5项。负责并主持国家自然科学基金面上项目，国家自然科学基金青年项 目、国家级科技前沿创新项目，中国博士后科学基金面上项目，陕西省自然科学基金项目，陕西省博士后科学基金项目，深圳市科技创新项目，重庆市自然科学基金，中央高校基本科研业务费专项资金项目，韩国三星公司合作项目等。参与自然基金重大研究计划、国家“863计划”，部委预研以及企业产学研项目若干。已在国际刊物上发表SCI/EI 检索论文70余篇。招生信息：招收电子科学与技术专业博士、硕士研究生。如果您数学好、编程能力强、英语读写能力强，碰巧还想学习人工智能的相关知识，就来找我们吧！欢迎优秀本科生来我们项目组攻读硕士学位！毕业学生可推荐至人工智能公司、研究所工作。获得荣誉：“计算智能中的协作学习与优化理论及方法”获2017教育部自然科学二等奖。2015年获中国电子学会第二十一届青年学术年会最佳论文奖。教育教学：        本科生课程《算法设计与优化》，《大数据》       硕士研究生课程《算法设计技术与智能优化方法》，《大数据分析与挖掘》       博士研究生课程《模糊控制与智能控制基础》学术兼职：现为IEEE会员，中国电子学会会员，智能空天电子系统技术工业和信息化部重点实验室秘书，中国电子学会智能无人系统分会秘书。担任遥感领域知名期刊IEEE J-STARS和Remote Sensing客座编辑，担任2023年IEEE信息物理社会智能国际会议（ICCSI）论文出版主席，担任2021年IEEE云计算与智能系统国际会议（CCIS）分论坛主席。指导学生：指导大学生创新训练项目8项（国家级2项、省级6项）。指导学生参加IEEE CEC，IJCNN等国际会议11人。指导学生与国际学者联合发表论文36篇。指导研究生刘晓冬获“硕士研究生创意创新种子基金”项目。以第一指导教师指导学生获第十二届国际仿人机器人奥林匹克大赛全国一等奖，2021年。指导本科生获校优秀毕业论文3人：谭春晖（2021年），杨楠（2022年），柴荣（2023年）。指导研究生刘晓冬获校优秀硕士学位论文，2021年。

荣誉获奖

学术成果 Academic Achievements 发表论文： 1.      Jiao Shi, Tiancheng Wu, A.K. Qin, Tao Shao, Yu Lei, Gwanggiil Joen, “Deep-Growing Neural Network with Manifold Constraints for Hyperspectral Image Classification,” IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3292537.2.      J. Shi, X. Zhao, N. Zhang, Y. Lei and L. Min, “Rough-Fuzzy Graph Learning Domain Adaptation for Fake News Detection,” IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2023.3312182.3.      Jiao Shi; Tiancheng Wu; A.K. Qin; Yu Lei*; Gwanggil Jeon; Semisupervised Adaptive Ladder Network for Remote Sensing Image Change Detection, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022,20：5408220. SCIE.4.      Jiao Shi, Tiancheng Wu, Hanwen Yu, A. K. Qin, Gwanggil Jeon and Yu Lei*, "Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images", SCIENCE CHINA Information Sciences, volume 66, Article number: 140308 (2023).5.       J. Shi, X. Zhang, X. Liu, Y. Lei*, G. Jeon. “Multicriteria Semi-supervised Hyperspectral Band Selection Based on Evolutionary Multitask Optimization”, Knowledge-Based Systems, Volume 240, 15 March 2022, 107934， 2022. 6.      Jiao Shi; Hao Wang; Chunhui Tan; Yu Lei*; Gwanggil Jeon; Spectral feature perception evolving network for hyperspectral image classification, Knowledge-based System, 2022, 256：109845. SCIE.7.      J. Shi, Y. Lei*, J. Wu, G. Jeon*. “Uncertain active contour model based on rough and fuzzy sets for auroral oval segmentation,” Information Sciences, vol. 492, pp:72-103, 2019. 8.      J. Shi, X. Zhang, C. Tan, Y. Lei*, N. Li and D. Zhou, "Multiple Datasets Collaborative Analysis for Hyperspectral Band Selection," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 5511605, doi: 10.1109/LGRS.2021.3126762. . 9.       J. Shi, Z. Zhang, C. Tan, X. Liu, Y. Lei*. “Unsupervised Multiple Change Detection in Remote Sensing Images via Generative Representation Learning Network”， IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 5001505, doi: 10.1109/LGRS.2021.3085022 10.   Yongjie Du; Deyun Zhou; Yu Xie; Yu Lei; Jiao Shi*; Prototype-Guided Feature Learning for Unsupervised Domain Adaptation, Pattern Recognition, 2022, 135：109154. SCIE. 11.   J. Shi, Z. Zhang, T. Wu, X. Zhang, Y. Lei “Collaborative Self-Perception Network Architecture for Hyperspectral Image Change Detection”, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 5512305. 12.   J. Shi*, X. Liu, Y. Lei. “SAR Images Change Detection Based on Self-adaptive Network Architecture”, IEEE Geoscience and Remote Sensing Letters, vol. 18(7), pp: 1204-1208, 2021. 13.   J. Shi, X. Liu, S. Yang, Y. Lei*, D. Tian. “An Initialization Friendly Gaussian Mixture Model based Multi-objective Clustering Method for SAR Images Change Detection”, Journal of Ambient Intelligence and Humanized Computing, 2021 14.   J Shi, T Shao, X Liu, X Zhang, Z Zhang, and Yu Lei*, “Evolutionary Multitask Ensemble Learning Model for Hyperspectral Image Classification”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 936-950, 2021. 15.   Y Zhou, Y Lei*, S Yang, Tao Shao, D Tian, J Shi. A traffic flow estimation method based on unsupervised change detection. Multimedia Systems, vol. 27: 857–865, 2021. 16.   C Han, D Zhou, Y Xie, M Gong*, Y Lei, J Shi. Collaborative representation with curriculum classifier boosting for unsupervised domain adaptation[J]. Pattern Recognition, vol. 113, 107802, 2021. 17.   J. Shi, X. Zhang, X. Liu, Y. Lei*. “Deep change feature analysis network for observing changes of land use or natural environment”, Sustainable Cities and Society, vol. 68, pp: 102760, 2021. 18.   N. Li, D. Zhou, J. Shi*, M Zhang, M Gong. Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction[J]. Remote Sensing,13(4): 706, 2021.19.   N. Li, D. Zhou, J. Shi*, T Wu, M Gong. Spectral-locational-spatial manifold learning for hyperspectral images dimensionality reduction[J]. Remote Sensing, 13(14): 2752, 2021.20.   N. Li, D. Zhou, J. Shi*, T Wu, Xiaolong Zheng and Zhen Yang,. Graph-Based Deep Multitask Few-Shot Learning for Hyperspectral Image Classification Remote Sens. 2022, 14(9), 2246; https://doi.org/10.3390/rs1409224621.   Y. Du, D. Zhou, Y. Xie, J. Shi, M. Gong*, “Federated matrix factorization for privacy-preserving recommender systems,” Applied Soft Computing, vol. 111: 107700, 2021.22.   T. Wu, X. Li*, D. Zhou, N. Li, J. Shi, “Differential Evolutionary Layer-wise Weights Pruning for Compressing Deep Neural Networks,” Sensors, vol. 21(3):880, 2021.23.   T. Wu, J. Shi*, D. Zhou, X. Zheng, N. Li. “Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network,” Sensors, vol. 21(17): 5901, 2021.24.   X. Zheng, D. Zhou, N. Li, T. Wu, Y. Lei*, J. Shi, "Self-Regulated Particle Swarm Multi-Task Optimization." Sensors, vol. 24(1): 7499, 2021. 25.   X. Zheng, D. Zhou, N. Li, T. Wu, Y. Lei*, J. Shi, "Self-Adaptive Multi-Task Differential Evolution Optimization: With Case Studies in Weapon Target Assignment Problem." Electronics, vol.10(23): 2945, 2021. 26.   J. Shi, X. Zhang, Z. Zhang, T. Shao, Y. Lei*. “Knowledge Transfer-Based Multiple Data Sets Collaborative Analysis for Hyperspectral Band Selection”， 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 2021, pp. 94-98, doi: 10.1109/CCIS53392.2021.9754661. 27.   J. Shi, Z. Zhang, X. Zhang. “Multi-Scale Features Fusion Network for Unsupervised Change Detection in Heterogeneous Optical and SAR Images”  2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 2021, pp. 270-274, doi: 10.1109/CCIS53392.2021.975466728.   C Han，D Zhou，Y Xie，Y Lei，J Shi. Label propagation with multi-stage inference for visual domain adaptation[J]. Knowledge-Based Systems, vol. 216: 106809, 2021. 29.   Y Du, D Zhou, J Shi*, Y Lei, M Gong, “Dynamic-graph-based Unsupervised Domain Adaptation”, 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-7, doi: 10.1109/IJCNN52387.2021.9534057. 30.   C Han, D Zhou, Y Xie, Y Lei, M Gong, J Shi*. Discrepancy-Aware Collaborative Representation for Unsupervised Domain Adaptation[C]// 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-6, doi: 10.1109/IJCNN48605.2020.9207726. 31.   Y. Lei, Y. Zhou, J. Shi*. “Overlapping Communities Detection of Social Network based on Hybrid C-means Clustering Algorithm,” Sustainable Cities and Society, vol. 47, pp: 101436, 2019. 32.   T. Wu, J. Shi*, D. Zhou, Y. Lei, M. Gong, “A Multi-objective Particle Swarm Optimization for Neural Networks Pruning,” in IEEE Congress on Evolutionary Computation (CEC), 570-577, 2019. 33.   X Zheng, Y Lei, J Shi*, D Zhou, M Gong, “Differential Evolutionary Multi-tasks Optimization”, 2019 IEEE Congress on Evolutionary Computation (CEC) 2019.6.5-2019. 34.   T. Wu, J. Shi, X. Jiang, D. Zhou, M. Gong*, “A multi-objective memetic algorithm for low rank and sparse matrix decomposition,” Information Sciences, 468, 2018: 172-192.35.   Y. Lei, J. Shi*, Z. Yan. “A Memetic Algorithm Based on MOEA/D for the Examination Timetabling Problem,” Soft Computing, vol. 22, pp: 1511-1523, 2018. 36.   Y. Lei, J. Shi, * Y. Zhou, M. Tao, J. Wu. “Extraction of Auroral Oval Regions Using Suppressed Fuzzy C Means Clustering,”2018 IEEE International Geoscience and Remote Sensing Symposium, 2018: 6883-6886. 37.   T Zhan; Z Tang; M Gong; X Jiang; Jiao Shi. Decomposition based multiobjective parcitle swarm optimization for change detection in SAR images, 2018 Genetic and Evolutionary Computation Conference, 1729-1736, 2018. 38.   J. Shi, J. Wu, M. Anisetti, E. Damiani, G. Jeon*. “An interval type-2 fuzzy active contour model for auroral oval segmentation”. Soft Computing, vol. 21, pp: 2325–2345, 2017.39.   J. Shi*, Y. Lei, J. Wu, A. Paul, M. Kim, G. Jeon*. “Uncertain Clustering Algorithms Based on Rough and Fuzzy Sets for Image Segmentation”, Journal of Real-Time Image Processing, vol. 13, pp: 645-663, 2017. 40.   Y. Lei, J. Shi*, J. Wu. “Region-driven distance regularized level set evolution for change detection in remote sensing images”. Multimedia Tools and Applications, vol. 76, pp: 24707-24722, 2017. 41.   F Chen, S Jiao*, Y Ma, Y Lei, M Gong. “Differential evolution algorithm with learning selection strategy for SAR image change detection”, 2017 IEEE Congress on Evolutionary Computation (CEC), 450-457, 2017. 42.   J. Shi, Y. Lei*, J. Bai, J. Wu. “Gradually Evolved Fuzzy Active Contour Model for Auroral Oval Segmentation,” 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017:3371-3374. 43.   N Li, Y Lei, J Shi*. “Fuzzy multi-objective sparse feature learning”, 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 466-473, 2017. 44.   J. Shi*, Y. Lei, Y. Zhou. “A narrow band interval type-2 fuzzy approach for image segmentation”, Journal of Systems Architecture, vol. 64, pp: 86-99, 2016.45.   J. Shi*, Y. Lei, Y. Zhou, M. Gong. “Enhanced rough–fuzzy c-means algorithm with strict rough sets properties,” Applied Soft Computing, vol. 46, pp:827-850, 2016. 46.   H. Zhang, M. Gong*, P. Zhang, L. Su and J. Shi. Feature-level Change Detection Using Deep Representation and Feature Change Analysis for Multi-spectral Imagery. IEEE Geoscience and Remote Sensing Letters, vol. 13 (11): 1666-1670, 2016.47.   W. Li, J. Wu, J. Shi*. "Fine-grained Parallel Implementation of Edge-directed Image Interpolation on GPU". 20th IEEE International Conference on Parallel and Distributed Systems, 2014:937-940.48.   M. Gong, Y. Liang, J. Shi, W. Ma, J. Ma. "Fuzzy C-Means Clustering with Local Information and Kernel Metric for Image Segmentation". IEEE Transactions on Image Processing, vol. 22, pp: 573–584, 2013.

科学研究

学术成果

综合介绍