1. 最优化的理论与方法；
2. 数学规划在人工智能与机器学习中的应用

招收最优化、人工智能、大数据挖掘方向的博士研究生与硕士研究生

最优化方法及其在人工智能中的应用
大数据挖掘与人工智能
机器学习

2004-09--2009-06   中国科学院数学与系统科学研究院   理学博士
2000-09--2004-07   西安交通大学信息与计算科学专业   理学学士

中国科学院数学与系统科学研究院 --  研究生

中国科学院数学与系统科学研究院 -- 博士

2007.4 - 2007.9 德国 Zuse Institute Berlin 研究所，访问

2020-01 至 今, 中国科学院大学经济与管理学院, 中国科学院大学虚拟经济与数据科学研究中心, 研究员

2012-05 至 2020-01, 中国科学院大学经济与管理学院, 中国科学院大学虚拟经济与数据科学研究中心， 副研究员 

2009-07 至 2012-05, 中国科学院大学经济与管理学院, 中国科学院大学虚拟经济与数据科学研究中心, 助理研究员

实用最优化算法及其应用

• Ruizhi Zhou, Lingfeng Niu, Dachuan Xu. Sparse loss-aware ternarization for neural networks. Inf. Sci. 693: 121668 (2025)

• Pei Quan, Lei Zheng, Wen Zhang, Yang Xiao, Lingfeng Niu, Yong Shi. ExGAT: Context extended graph attention neural network. Neural Networks 181: 106784 (2025)

• Yong Shi, Lei Zheng, Pei Quan, Yang Xiao, Lingfeng Niu. A universal network strategy for lightspeed computation of entropy-regularized optimal transport. Neural Networks 184: 107038 (2025))

• Yong Shi, Anda Tang, Lingfeng Niu, Ruizhi Zhou: Sparse optimization guided pruning for neural networks. Neurocomputing 574: 127280 (2024)

• Ruibin Zeng, Minglong Lei, Lingfeng Niu, and Lan Cheng. A unified pre-training and adaptation framework for combinatorial optimization on graphs. Science China Mathematics 67, no. 6: 1439-1456 (2024)

• Chengxi Song, Lingfeng Niu, Minglong Lei. Two-level adversarial attacks for graph neural networks. Inf. Sci. 654: 119877 (2024)

• Yong Shi, Lei Zheng, Pei Quan, Lingfeng Niu. Wasserstein distance regularized graph neural networks. Inf. Sci. 670: 120608 (2024)

• 师瑞阳,牛凌峰,戴彧虹. 胶囊聚合注意力机制求解车辆路径规划问题.Science China Mathematics,1-26. (2024)

• Anda Tang, Lingfeng Niu, Jianyu Miao, Peng Zhang. Training Compact DNNs with ℓ1/2 Regularization. Pattern Recognit. 136: 109206 (2023)

• Yuhan Lin, Lingfeng Niu, Yang Xiao, Ruizhi Zhou. Diluted binary neural network. Pattern Recognit. 140: 109556 (2023)

• Yong Shi, Yuanying Zhang, Peng Zhang, Yang Xiao, Lingfeng Niu. Federated learning with ℓ1 regularization. Pattern Recognit. Lett. 172: 15-21 (2023)

• Yong Shi, Pei Quan, Yang Xiao, Minglong Lei, Lingfeng Niu. Graph Influence Network. IEEE Trans. Cybern. 53(10): 6146-6159 (2023)

• Jianyu Miao, Tiejun Yang, Jun-Wei Jin, Lijun Sun, Lingfeng Niu, Yong Shi. Towards Compact Broad Learning System by Combined Sparse Regularization. Int. J. Inf. Technol. Decis. Mak. 21(1): 169-194 (2022)

• Yang Xiao, Pei Quan, Minglong Lei, Lingfeng Niu. Latent neighborhood-based heterogeneous graph representation. Neural Networks 154: 413-424 (2022)

• Jianyu Miao, Tiejun Yang, Lijun Sun, Xuan Fei, Lingfeng Niu, Yong Shi. Graph regularized locally linear embedding for unsupervised feature selection. Pattern Recognit. 122: 108299 (2022)

• Ruiyang Shi, Lingfeng Niu, Ruizhi Zhou. Sparse CapsNet with explicit regularizer. Pattern Recognit. 124: 108486 (2022)

• Minglong Lei, Pei Quan, Rongrong Ma, Yong Shi, Lingfeng Niu. DigGCN: Learning Compact Graph Convolutional Networks via Diffusion Aggregation. IEEE Trans. Cybern. 52(2): 912-924 (2022)

• Xiaofei Zhou, Lingfeng Niu, Qiannan Zhu, Xingquan Zhu, Ping Liu, Jianlong Tan, Li Guo. Knowledge Graph Embedding by Double Limit Scoring Loss. IEEE Trans. Knowl. Data Eng. 34(12): 5825-5839 (2022)

• Ruiyang Shi, Jie Huang, Shulun Li, Lingfeng Niu, Junbo Yang. ForwardPrediction and Inverse Design of Nanophotonic Devices Based on Capsule Network. IEEE Photonics Journal. 14(4): 1-8 (2022) 

• Ruizhi Zhou, Lingfeng Niu, Hong Yang. Unsupervised feature selection for attributed graphs. Expert Syst. Appl. 168: 114402 (2021)

• Jianyu Miao, Yuan Ping, Zhensong Chen, Xiao-Bo Jin, Peijia Li, Lingfeng Niu. Unsupervised feature selection by non-convex regularized self-representation. Expert Syst. Appl. 173: 114643 (2021)

• Ruizhi Zhou, Qin Zhang, Peng Zhang, Lingfeng Niu, Xiaodong Lin. Anomaly detection in dynamic attributed networks. Neural Comput. Appl. 33(6): 2125-2136 (2021)

• Yong Shi, Yang Xiao, Pei Quan, Minglong Lei, Lingfeng Niu. Distant Supervision Relation Extraction via adaptive dependency-path and additional knowledge graph supervision. Neural Networks 134: 42-53 (2021)

• Yong Shi, Yang Xiao, Pei Quan, Minglong Lei, Lingfeng Niu. Document-level relation extraction via graph transformer networks and temporal convolutional networks. Pattern Recognit. Lett. 149: 150-156 (2021)

• Ruizhi Zhou, Lingfeng Niu. Feature Selection of Network Data VIA ℓ2, p Regularization. Cogn. Comput. 12(6): 1217-1232 (2020)

• Yong Shi, Minglong Lei, Rongrong Ma, Lingfeng Niu. Learning Robust Auto-Encoders With Regularizer for Linearity and Sparsity. IEEE Access 7: 17195-17206 (2019)

• Jianyu Miao, Tiejun Yang, Junwei Jin, Lingfeng Niu. Graph-Based Clustering via Group Sparsity and Manifold Regularization. IEEE Access 7: 172123-172135 (2019)

• Jianyu Miao, Heling Cao, Xiao-Bo Jin, Rongrong Ma, Xuan Fei, Lingfeng Niu. Joint Sparse Regularization for Dictionary Learning. Cogn. Comput. 11(5): 697-710 (2019)

• Yong Shi, Peijia Li, Hao Yuan, Jianyu Miao, Lingfeng Niu. Fast kernel extreme learning machine for ordinal regression. Knowl. Based Syst. 177: 44-54 (2019)

• Yong Shi, Jianyu Miao, Lingfeng Niu. Feature selection with MCP $$^2$$ 2 regularization. Neural Comput. Appl. 31(10): 6699-6709 (2019)

• Rongrong Ma, Jianyu Miao, Lingfeng Niu, Peng Zhang. Transformed ℓ1 regularization for learning sparse deep neural networks. Neural Networks 119: 286-298 (2019)

• Yong Shi, Minglong Lei, Hong Yang, Lingfeng Niu. Diffusion network embedding. Pattern Recognit. 88: 518-531 (2019)

• Ruizhi Zhou, Xin Shen, Lingfeng Niu. A fast algorithm for nonsmooth penalized clustering. Neurocomputing 273: 583-592 (2018)

• Fan Meng, Zhiquan Qi, Yingjie Tian, Lingfeng Niu. Pedestrian detection based on the privileged information. Neural Comput. Appl. 29(12): 1485-1494 (2018)

• Zhiquan Qi, Fan Meng, Yingjie Tian, Lingfeng Niu, Yong Shi, Peng Zhang. Adaboost-LLP: A Boosting Method for Learning With Label Proportions. IEEE Trans. Neural Networks Learn. Syst. 29(8): 3548-3559 (2018)

• Yong Shi, Jianyu Miao, Zhengyu Wang, Peng Zhang, Lingfeng Niu. Feature Selection With ℓ2, 1-2 Regularization. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4967-4982 (2018)

• Xin Shen, Lingfeng Niu, Zhiquan Qi, Yingjie Tian. Support vector machine classifier with truncated pinball loss. Pattern Recognit. 68: 199-210 (2017)

• Lingfeng Niu, Ruizhi Zhou, Yingjie Tian, Zhiquan Qi, Peng Zhang. Nonsmooth Penalized Clustering via ℓp Regularized Sparse Regression. IEEE Trans. Cybern. 47(6): 1423-1433 (2017)

• Zhiquan Qi, Bo Wang, Fan Meng, Lingfeng Niu. Learning With Label Proportions via NPSVM. IEEE Trans. Cybern. 47(10): 3293-3305 (2017)

• Huadong Wang, Yong Shi, Lingfeng Niu, Yingjie Tian. Nonparallel Support Vector Ordinal Regression. IEEE Trans. Cybern. 47(10): 3306-3317 (2017)

• Xi Zhao, Yong Shi, Lingfeng Niu. Kernel based simple regularized multiple criteria linear program for binary classification and regression. Intell. Data Anal. 19(3): 505-527 (2015)

• Zhiquan Qi, Yingjie Tian, Lingfeng Niu, Bo Wang. Semi-supervised classification with privileged information. Int. J. Mach. Learn. Cybern. 6(4): 667-676 (2015)

• Xiaojun Chen, Lingfeng Niu, Yaxiang Yuan. Optimality Conditions and a Smoothing Trust Region Newton Method for NonLipschitz Optimization. SIAM J. Optim. 23(3): 1528-1552 (2013)

• Lingfeng Niu, Jianmin Wu, Yong Shi. Training the max-margin sequence model with the relaxed slack variables. Neural Networks 33: 228-235 (2012)

• Lingfeng Niu. Parallel algorithm for training multiclass proximal Support Vector Machines. Appl. Math. Comput. 217(12): 5328-5337 (2011)

• Lingfeng Niu, Yaxiang Yuan. A parallel decomposition algorithm for training multiclass kernel-based vector machines. Optim. Methods Softw. 26(3): 431-454 (2011)

承担项目：

国家自然科学基金面上基金项目《基于最优化的深度学习效能与效率研究》 (12271503，2023.01-2026.12）课题负责人

国家自然科学基金重大项目课题《混合整数规划的人工智能方法》 (11993102011, 2020.01-2024.12) 国科大子课题负责人

国科大优秀教师科研能力提升项目《基于最优化的深度神经网络压缩研究》(E02A150101，2021-2022.12）课题负责人

国家自然科学基金面上基金项目《非Lipschitz优化的高效光滑化信赖域方法及应用》(11671379，2017.01-2020.12）课题负责人

国家自然科学基金重点项目子课题《面向信息技术的优化理论和方法》(11331012，2014.01-2018.12）国科大子课题负责人

国家自然科学基金青年基金《求解非光滑、非凸正则极小化问题的光滑化信赖域方法》(11201472，2013.01-2015.12）课题负责人

科技部国家重点研发专项《信息驱动的系统认知与博弈演化学习方法研究》(2021YFA1000403，2021.12-2026.11）主要参与人

国家自然科学基金国际(地区)合作与交流项目《最优化数据挖掘的商业智能方法以及在金融与银行管理中的应用》(71110107026，2012.01-2016.12）主要参与人