(1)获奖情况 [1]2026年中国电子学会教学成果大赛三等奖; [2]2025年云南省自然科学奖一等奖; [3]2022年入选云南省“兴滇英才支持计划”青年人才; [4]2017年中国电子学会科技进步奖三等奖; (2)教学/科研项目 [1]主持国家自然科学基金地区项目(6246070439)“面向大规模复杂多模态数据的高效能检索方法研究”; [2]主持国家自然科学基金地区项目(62162033)“面向复杂多视图数据表示的深度矩阵/张量分解方法研究”; [3]主持国家自然科学基金青年项目(61603159)“面向低质量图像数据的稀疏低秩矩阵回归与分解方法研究”; [4]主持云南省重大科技专项课题(202402AD080001-3)“物理机理引导深度学习的工业设备故障辨识分析技术研究”; [5]主持云南省基础研究计划重点项目(202601AS070030)“多模态大模型驱动的高效跨模态检索方法研究”; [6]主持云南省基础研究计划面上项目(202101AT070438)“面向大规模复杂跨模态数据的语义表示与检索方法研究”; [7]主持云南省科技厅-昆明理工大学“双一流”创建联合专项面上项目(202101BE070001-056)“基于稳健深度矩阵分解的肿瘤基因选择方法研究”; [8]主持2022年云南省“兴滇英才支持计划”青年人才项目“面向复杂大规模跨模态数据检索的哈希方法研究”; [9]主持江苏省自然科学基金青年项目(BK20160293)“基于稀疏低秩的鲁棒矩阵回归与分解方法研究”; [10]主持中国博士后科学基金项目(2017M611695)“基于稀疏低秩理论的图像回归与分解理论研究”; [11]主持江苏省博士后科学基金项目(1701094B)“面向高维图像鲁棒表示的稀疏低秩理论与方法研究”; [12]主持江苏省双创博士计划项目(科技副总类); (3)论文 [1] Z.Shu,etal.Spectral-guidedmulti-scalefeatureaware Transformer forhyperspectralimageclassification.IEEE Transactions on Neural Networks and Learning Systems,2026. [2]Y.Wang#(硕士生), M.Tai#(硕士生), Z.Liu, Zhenqiu Shu*,etal. Mid-rangeconvolutionalmodulated Transformernetwork forhyperspectralimageclassification.IEEE Transactions on Geoscience and Remote Sensing, 2025. [# co-first author] [3]Z.Shu, et al. DynaClean: Amulti-scalegraph-driven noisy label correction framework for mechanical fault diagnosis.IEEE Transactions on Instrumentation and Measurement, 2026. [4]Z.Shu*, et al.Predictive completionenhanceddeephashing withauxiliarycodeguidance forincompletecross-modalretrieval.IEEE Transactionson Big Data,2026. [5]Z.Shu, et al. Dual-domain synergistic guided attention network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing,2025. [6]Z.Shu,et al. Ambiguousinstance-awarecontrastivenetwork withmulti-levelmatching formulti-viewdocumentclustering,The 39th Annual AAAI Conference on Artificial Intelligence(AAAI),2025. [7]Z.Shu*,etal. Dualfeatures aggregation network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing,2025. [8]Z.Shu,etal.Robustcross-modaldeephashing withrankinglearning fornoisylabels.IEEE Transactionson Big Data,2024. [9] Z.Shu,et al. sigRGCN: A robust residual graph convolutional network for scRNA-seq data clustering.IEEE/ACM Transactions on Computational Biology and Bioinformatics,2025. [10] G.Zhuo(硕士生),Z.Shu*, et al.Multi-modal coupling prompt learning for zero-shot sketch-based image retrieval.IEEE Transactionson Big Data,2024. [11]Y.Bai(硕士生),Z.Shu*, et al. Proxy-based graph convolutional hashing for cross-modal retrieval.IEEE Transactionson Big Data,2024: 10(4): 371 - 385. [12]T.Sun(硕士生),Z.Shu*, et al. Semanticfeaturegraphconsistency withcontrastiveclusterassignments formultilingualdocumentcustering.ACM Transactions on Asian and Low-Resource Language Information Processing,2025. [13]M. Xia#(硕士生), Q. Long#(硕士生), Zhenqiu Shu*, et al. scSAG2E: Sparse autoencoders with gene graph embedding for scRNA-seq data clustering.IEEE/ACM Transactions on Computational Biology and Bioinformatics,2025.[# co-first author] [14]X. Yu, D. Peng, Z. Shu*, et al. Proxy-based dynamic view alignment with cross-view structure preservation for multi-view clustering.IEEE Transactionson Big Data,2026 [15]K. Tan, Y. Bai, Y. Zhang, Z. Shu*, et al. scDGCL: A dual-level and graph-constrained contrastive learning method for single-cell RNA sequencing data clustering.IEEE/ACM Transactions on Computational Biology and Bioinformatics,2026. [16]Z.Luo(硕士生),Z.Shu*.Retrieval-guided completion hashing with token–patch alignment for incomplete cross-modal retrieval.The 35th International Joint Conference on Artificial Intelligence (IJCAI), 2026. [17]T. Sun(硕士生), Y. Luo,Z.Shu*, et al.Hierarchical attention fusion with synergistic adversarial contrastive learning for incomplete multi-view clustering.Pattern Recognition, 2026. [18]Z.Shu*,et al,Adaptive centroid guided hashing for cross-modal retrieval.Pattern Recognition, 2026. [19]K.Yong(硕士生),Z.Shu*, et al. Two-stage zero-shot sparse hashing with missing labels for cross-modal retrieval.Pattern Recognition, 2024. [20]L.Li(硕士生),Z.Shu*, et al. Robust online hashing via label semantic enhancement for cross-modal retrieval.Pattern Recognition, 2024, 145: 109972. [21]Z.Shu,et al. Structural consistency contrast based on multi-stream GCN for scRNA-seq clustering.Briefings in Bioinformatics, 2024. (4)知识产权 [1]舒振球等.无参数自动加权多图正则化非负矩阵分解及图像识别方法.发明专利,授权号:CN107609596, 2020.(已授权) [2]舒振球等.封顶概念分解方法及图像聚类方法.发明专利,申请号:201711257431.6,2017.(已授权) [3]舒振球等.面向多视图聚类的多图正则化深度矩阵分解方法.发明专利,申请号:20180607971.0, 2018.(已授权) [4]舒振球等.基于局部学习正则化的深度矩阵分解方法及图像聚类方法,发明专利,申请号:201810905948.X,2018.(已授权) [5]舒振球等.一种稀疏对偶约束的高光谱图像解混方法,发明专利,专利号:ZL 201910514472.1, 2023(已授权) [6]舒振球等.一种基于对偶局部一致的约束稀疏概念分解的聚类方法,专利号:2020105078760, 2023(已授权) [7]舒振球等.一种基于相似性零样本哈希的跨模态检索方法,专利号:ZL202210696434.4, 2024(已授权) [8]舒振球等.基于空间-光谱混合自注意力机制的高光谱图像分类方法.专利号:ZL 2023 1 0902900.4, 2025.(已授权) [9]舒振球等.一种鲁棒哈希的不配对零样本图文跨模态检索方法.专利号:202310902853.3, 2025.(已授权) [10]舒振球等.一种鲁棒哈希的不配对零样本图文跨模态检索方法.专利号:ZL 2023 1 0902853.3, 2025.(已授权) |