苗建雨，男，1989.11月生，博士，讲师，中国图学学会可视化与认知计算专委会委员。
电子邮箱：jymiao@haut.edu.cn
研究方向：机器学习、最优化与数据挖掘.
教育经历：
2011.09-2014.06,郑州大学,应用数学,硕士;
2015.09-2018.06,中国科学院大学,运筹学与控制论,博士;
代表性教科研学术成果：
论文：
1. Jianyu Miao, Tiejun Yang, Chao Fan, Zhensong Chen, Xuan Fei, Xuchan Ju, Ke Wang, Mingliang Xu, Self-Paced Non-Convex Regularized Analysis-Synthesis Dictionary Learning for Unsupervised Feature Selection, Knowledge-Based Systems, 2022, 241.
2. Jianyu Miao, Tiejun Yang, Lijun Sun, Xuan Fei, Lingfeng Niu, Yong Shi, Graph Regularized Locally Linear Embedding for Unsupervised Feature Selection, Pattern Recognition, 122, 2022.
3. Jianyu Miao, Yuan Ping, Zhensong Chen, Xiao-Bo Jin, Peijia Li, Lingfeng Niu, Unsupervised feature selection by non-convex regularized self-representation, Expert Systems with Applications, 163, 2021.
4. Jianyu Miao, Tiejun Yang, Junwei Jin, Lijun Sun, Lingfeng Niu, Yong Shi, Towards Compact Broad Learning System by Combining Convex Group and Non-convex Individual Sparsity, International Journal of Information Technology and Decision Making, 2022, 21(1): 169-194.
5. Jianyu Miao, Heling Cao, Xiao-Bo Jin, Rongrong Ma, Xuan Fei, Lingfeng Niu, Joint Sparse Regularization for Dictionary Learning, Cognitive Computation, 2019, 11(5): 697-710.
6. Jianyu Miao, Tiejun Yang, Junwei Jin, Lingfeng Niu, Graph-based Clustering via Group Sparsity and Manifold Regularization, IEEE Access, 2019, 7: 172123-172135.
7. Yong Shi, Jianyu Miao, Zhengyu Wang, Peng Zhang, Lingfeng Niu, Feature Selection with L_{2,1-2} Regularization, IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 4967-4982.
8. Yong Shi, Jianyu Miao, Lingfeng Niu, Feature Selection MCP^2 Regularization, Neural Computing & Applications, 2019, 31(10): 6699--6709.
9. Rongrong Ma, Jianyu Miao, Lingfeng Niu, Peng Zhang, Transformed \ell_1 Regularization for Learning Sparse Deep Neural Networks, Neural Networks, 2019, 119: 286-298.
10. Jiashuai Zhang, Jianyu Miao, Kun Zhao, Yingjie Tian, Multi-task feature selection with sparse regularization to extract common and task-specific features. Neurocomputing, 2019, 340: 76-89.
项目：
1.基于非凸正则化的多视角无监督特征选择方法研究，国家自然科学基金，2022.01-2024.12
2.稀疏理论及其在若干机器学习问题中的应用，河南省科技攻关项目，2020.01-2021.11
3.基于非凸稀疏正则化的深度神经网络压缩方法研究，河南省教育厅，2021.01-2022.12
4.面向大规模不完全多视角数据的稀疏子空间聚类算法研究，郑州市科技协同创新专项，2022.01-2023.12
