一、导师基本信息

姓名： 江兵兵

邮箱：jiangbb@hznu.edu.cn

指导专业：计算机科学与技术(学硕)、软件工程(专硕)

二、主要研究领域

主要研究面向复杂数据的机器学习和数据挖掘方法。具体包括，半监督学习（同时利用少量标签数据和大量的无标签数据，增强机器学习的适应性）、多视图学习（包括数据表示、融合等）、特征选择（从原始数据中筛选相关特征子集，提高可解释性）、无监督聚类（包括谱聚类、密度聚类、层次聚类等）、算法选择/大语言模型及其在数据分析中的应用研究。

三、主讲课程

1. 离散数学，本科生，计算机科学与技术

2. 数据结构，本科生，计算机科学与技术

四、教育及工作经历

2014年6月在重庆邮电大学计算机学院获工学学士学位 （计算机专业统考，初试第一进入中国科大计算机学院）

2019年6月在中国科学技术大学计算机学院获工学博士学位（硕博连读，导师是IEEE Fellow陈欢欢教授)

2019年10月— 至今 杭州师范大学信息科学与技术学院 教师/硕士生导师

五、学术简介

在IEEE TPAMI、IEEE TKDE、IEEE TNNLS、IEEE TCYB、IEEE TETCI、《Information Fusion》、《Pattern Recognition》、IJCAI、AAAI、ACM MM、《电子学报》等国内外学术期刊和会议上累计发表论文50余篇，其中以第一作者/通讯作者在CCF推荐A类国际会议和期刊上发表论文6篇，在CCF推荐A类中文期刊上发表论文2篇，在SCI 1区期刊上发表论文9篇。目前担任人工智能领域SCI 1区期刊《Applied Soft Computing》的编委，以及IEEE TPAMI、IEEE TKDE、IEEE TNNLS、IEEE TFS、IEEE/CAA JAS、IEEE TCYB、IEEE TBD、《Pattern Recognition》、《Information Fusion》、《Applied Soft Computing》、《SCIENCE CHINA Information》、ICML、IJCAI、AAAI、ACM MM等30多个国际知名学术期刊和会议的特邀审稿人与PC member。主持国家自然科学基金青年项目1项，参与国家重点研发计划项目、国家自然科学基金培育项目等项目多项。曾获2023年度杭州师范大学优秀论文奖、2024年度杭州师范大学优秀论文二等奖、2019年中国科学院院长特别奖等荣誉。所指导的硕士研究生以第一/通信作者在CCF 推荐A/B类或SCI 1区期刊上累计发表论文10余篇，并获得杭州师范大学优秀硕士学位论文、国家奖学金、浙江省优秀毕业生等荣誉。其中2020级研究生获得杭州师范大学优秀硕士学位论文、浙江省优秀毕业生等荣誉；2022级研究生以第一/通信作者在IJCAI、AAAI、IEEE TETCI、Information Fusion、IJCNN等期刊和会议上发表论文多篇，获得国家奖学金、浙江省优秀毕业生等荣誉，并进入国内顶级院校读博；2023级研究生以第一作者在CCF 推荐A类会议AAAI上发表论文1篇、2024级研究生以学生第一作者在SCI 1区期刊发表论文1篇。

六、主持教学科研项目

国家自然科学基金青年项目 面向监督与半监督的贝叶斯学习方法研究， 2021—2023.

七、代表性学术论文(*表示通信作者)

 [1] Bingbing Jiang, Chenglong Zhang, Xinyan Liang, et al. Collaborative Similarity Fusion and Consistency Recovery for Incomplete Multi-view Clustering[C]. In Proceedings of the AAAI Conference on Artificial  Intelligence. 2025. (CCF A类会议)

[2] Bingbing Jiang, Jun Liu, Zidong Wang, et al. Semi-supervised Multi-view Feature Selection with Adaptive Similarity Fusion and Learning[J]. Pattern Recognition, 2025, 159:111159. (CCF B类/SCI 1区期刊)

[3] Chenglong Zhang, Xinjie Zhu, Zidong Wang, Yan Zhong, Weiguo Sheng, Weiping Ding, Bingbing Jiang*. Discriminative Multi-View Fusion via Adaptive Regression[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(6)：3821-3833. (SCI 1区期刊)

[4] Bingbing Jiang, Xingyu Wu, Xiren Zhou, et al. Semi-supervised multiview feature selection with adaptive graph learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3) 3615-3629. (CCF B类/SCI 1区期刊，获2024年度杭州师范大学优秀论文二等奖)

[5] Chenglong Zhang, Yang Fang, Xinyan Liang, Han Zhang, Peng Zhou, Xingyu Wu, Jie Yang, Bingbing Jiang*, Weiguo Sheng. Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference[C]. In Proceedings of the International Joint Conference on Artificial Intelligence. 2024: 5443-5452. (CCF A类会议)

[6] Chenglong Zhang, Xinyan Liang, Peng Zhou, Zhaolong Lin, Yingwei Zhang, Xinyu Wu, Weiguo Sheng, Bingbing Jiang*. Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion[C]. In Proceedings of the ACM International Conference on Multimedia. 2024: 5479-5488. (CCF A类会议)

[7] Xingyu Wu, Yan Zhong, Zhaolong Ling, Jie Yang, Li Li, Weiguo Sheng, Bingbing Jiang*. Nonlinear learning method for local causal structures[J]. Information Sciences, 2024, 654: 119789. (CCF B类/SCI 1区期刊)

[8] Zihao Xu, Chenglong Zhang, Zhaolong Ling, Peng Zhou, Yan Zhong, Li Li, Han Zhang, Weiguo Sheng, Bingbing Jiang*. Multi-View Semi-Supervised Feature Selection with Graph Convolutional Networks[C]. In Proceedings of the International Joint Conference on Neural Networks. 2024:1-8. (CCF C类会议)

[9] Chenglong Zhang, Bingbing Jiang*, Zidong Wang, et al. Efficient multi-view semi-supervised feature selection[J]. Information Sciences, 2023, 649:119675. (CCF B类/SCI 1区期刊)

[10] Bingbing Jiang, Chenglong Zhang, Yan Zhong, et al. Adaptive collaborative fusion for multi-view semi-supervised classification[J]. Information Fusion, 2023, 96: 37-50. (SCI 1区期刊, 获2023年度杭州师范大学优秀论文奖)

[11] Yangfeng Lu, Chenglong Zhang, Bingbing Jiang*. Accelerated Semi-supervised Feature Selection via Adaptive Bipartite Graph[C]. Proceedings of the International Conference on Artificial Intelligence and Pattern Recognition. 2023: 592-5498.

[12] Bingbing Jiang, Junhao Xiang, Xingyu Wu, et al. Robust multi-view learning via adaptive regression[J]. Information Sciences, 2022, 610: 916-937. (CCF B类/SCI 1区期刊)

[13] 江兵兵, 何文达, 吴兴宇等. 基于自适应图学习的半监督特征选择. 电子学报, 2022, 50(7): 1643-1652. (CCF A类中文期刊)

[14] Bingbing Jiang, Junhao Xiang, Xingyu Wu, et al. Robust adaptive-weighting multi-view classification[C]. In Proceedings of the ACM International Conference on Information & Knowledge Management. 2021: 3117-3121. (CCF B类会议)

[15] Bingbing Jiang, Chang Li, Maarten De Rijke, Huanhuan Chen, Xin Yao. Probabilistic feature selection and classification vector machine[J]. ACM Transactions on Knowledge Discovery from Data, 2019, 13(2): 1-27. (CCF B类期刊)

[16] Bingbing Jiang, Xingyu Wu, Kui Yu, Huanhuan Chen. Joint semi-supervised feature selection and classification through Bayesian approach[C]. In Proceedings of the AAAI Conference on Artificial Intelligence. 2019: 3983-3990. (CCF A类会议)

[17] 陈兵飞, 江兵兵*, 周熙人, 陈欢欢, 基于稀疏贝叶斯的流形学习. 电子学报, 2018, 46(1): 98-103. (CCF A类中文期刊)

[18] Bingbing Jiang, Zhengyu Li, Huanhuan Chen, et al. Latent Topic Text Representation Learning on Statistical Manifolds[J], IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11): 5643-5654. (CCF B类/SCI 1区期刊)

[19] Bingbing Jiang, Huanhuan Chen, Bo Yuan, Xin Yao. Scalable graph-based semi-supervised learning through sparse Bayesian model[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2758-2771. (CCF A类/SCI 1区期刊)

[20] Huanhuan Chen, Bingbing Jiang, Xin Yao. Semi-supervised negative correlation learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11): 5366-5379. (CCF B类/SCI 1区期刊)

[21] Xingyu Wu, Bingbing Jiang, Yan Zhong, Huanhuan Chen. Multi-target Markov boundary discovery: Theory, algorithm, and application[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 4964-4980. (CCF A类/SCI 1区期刊)

[22] Xingyu Wu, Bingbing Jiang, TianhaoWu, et al. Practical Markov Boundary Learning without Strong Assumptions[C]. In Proceedings of the AAAI Conference on Artificial Intelligence. 2023: 10388-10398. (CCF A类会议)

[23] 吴兴宇, 江兵兵, 陈欢欢等. 基于马尔科夫边界发现的因果特征选择算法综述. 模式识别与人工智能, 2022, 35(5): 422-438. (CCF B类中文期刊，入选 2022 年中国科协科技期刊双语传播工程)

[24] Xingyu Wu, Bingbing Jiang, Xiangyu Wang, et al. Feature selection in the data stream based on incremental Markov boundary learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 6740-6754. (CCF B类/SCI 1区期刊)

[25] Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen. Separation and Recovery Markov Boundary Discovery and Its Application in EEG-based Emotion Recognition[J]. Information Sciences, 2021, 571(9):262-278. (CCF B类/SCI 1区期刊)

[26] Yang Li, Bingbing Jiang, Huanhuan Chen, Xin Yao. Symbolic sequence classification in the fractal space[J]. IEEE Transactions on Emerging Topics in Computational Intelligence 2021,5(2):168-177. (SCI 1区期刊)

[27] Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, Chunyan Miao. Multi-label causal feature selection[C]. In Proceedings of the AAAI Conference on Artificial Intelligence. 2020: 6430-6437. (CCF A类会议)

[28] Xingyu Wu, Bingbing Jiang, Kui Yu, Chunyan Miao, Huanhuan Chen. Accurate Markov Boundary Discovery for Causal Feature Selection[J], IEEE Transactions on Cybernetics, 2020, 50(12): 4983-4996. (CCF B类/SCI 1区期刊)

[29] Xingyu Wu, Bingbing Jiang, Yan Zhong, Huanhuan Chen. Tolerant Markov Boundary Discovery for Feature Selection. In Proceedings of the ACM International Conference on Information & Knowledge Management. 2020: 2261-2264. (CCF B类会议)