赵静
计算机科学与技术学院??????


导航
个人资料
研究方向
开授课程
科研项目
学术成果
荣誉及奖励







个人资料
部门： 计算机科学与技术学院
毕业院校：
学位：
学历：
邮编：
联系电话：
传真：
电子邮箱： jzhao@cs.ecnu.edu.cn
办公地址： 理科大楼B909
通讯地址：

教育经历

工作经历

个人简介

社会兼职
上海市计算机学会人工智能专委会秘书长
SCI期刊International Journal of Machine Learning and Cybernetics (JMLC) 副编辑
Information Fusion, IEEE Transactions on Intelligent Transportation System, Neurocomputing, Neural Processing Letter, Intelligent Data Analysis等国际期刊审稿人



研究方向
模式识别与机器学习：概率模型，近似推理，核方法，序列数据建模


开授课程


科研项目


学术成果
代表性论文如下：(#)表示第一作者，（*）表示通讯作者
[1]S. Sun^(#), Z. Cao, H. Zhu, and J. Zhao^(*). A Survey of Optimization Methods from a Machine Learning Perspective. IEEE Transactions on Cybernetics (T-CYB), 50:3668-3681, 2020.( SCI一区期刊, IF:10.387).
[2]J. Zhao^(#), S. Sun^(*), H. Wang and Z. Cao. Promoting Active Learning with Mixtures of Gaussian Processes. Knowledge-Based Systems (KBS), 188: 1-12, 2020.(SCI二区期刊，IF:5.101)
[3]J. Zhao^(#), X. Liu, S. He, S. Sun^(*). Probabilistic inference of Bayesian neural networks with generalized expectation propagation. Neurocomputing, 412: 392-398, 2020. (SCI二区期刊，IF:4.438)
[4]Y. Hu^(#),S. Sun^(*), X. Xu,J. Zhao. Attentive multi-view reinforcement learning. International Journal of Machine Learning and Cybernetics, 2020.(SCI三区期刊，IF：3.753)
[5]H. Zhu^(#), J. Zhao^(*), S. Sun. Multi-view Deep Gaussian Process with a Pre-training Acceleration Technique. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 299-311, 2020. (CCF C类会议)
[6]Z. Dong^(#),J. Zhao^(*), and S. Sun^(*). A Conditional Random Fields Based Framework for Multiview Sequential Data Modeling. Proceedings of the 26th International Conference on Neural Information Processing (ICONIP), pp. 1-12, 2019. (CCF C类会议)
[7]Y. Hu^(#), S. Sun^(*), X. Xu andJ. Zhao^(*). Multi-view Deep Attention Network for Reinforcement Learning. Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), pp.1-2, 2019.(Student Abstract, CCF A类会议)
[8]X. Liu^(#),J. Zhao^(*)and S. Sun^(*). Bayesian Adversarial Attack on Graph Neural Networks. Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), pp.1-2, 2019.(Student Abstract, CCF A类会议)
[9]J. Wang^(#),J. Zhao^(*), S. Sun and D. Shi. Intelligent Educational Data Analysis with Gaussian Processes. Proceedings of the 25th International Conference on Neural Information Processing (ICONIP), pp. 353-362, 2018. (CCF C类会议)
[10]J. Fei^(#),J. Zhao^(#), S. Sun^(*)and Yan Liu. Active Learning Methods with Deep Gaussian Processes. Proceedings of the 25th International Conference on Neural Information Processing (ICONIP), pp. 473-483, 2018. (CCF C类会议)
[11]J. Chen^(#), S. Sun^(*),J. Zhao^(*). Multi-label active learning with conditional Bernoulli mixtures. Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI), pp. 954-967, 2018. (CCF C类会议)
[12]J. Zhao^(#), X. Xie, X. Xu, S. Sun^(*). Multi-view learning overview: Recent progress and new challenges. Information Fusion (IF), pp. 43-54, 2017.(SCI一区期刊, IF：10.716，ESI高被引论文，Top 1%)
[13]H. Wang^(#)andJ. Zhao^(#)and Z. Tang and S. Sun^(*). Educational and Non-educational Text Classification Based on Deep Gaussian Processes. Proceedings of the 24th International Conference on Neural Information Processing (ICONIP), pp. 415-423, 2017. (CCF C类会议)
[14]C. Luo^(#), S. Sun,J. Zhao^(*). Variational hidden conditional random fields with beta processes. Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 1887-1893, 2017. (EI会议)
[15]J. Zhao^(#)and S. Sun^(*). Variational Dependent Multi-output Gaussian Process Dynamical Systems. Journal of Machine Learning Research (JMLR), 17: 1-36, 2016.(CCF A类期刊，IF:4.091)
[16]J. Zhao^(#)and S. Sun^(*). High-Order Gaussian Process Dynamical Models for Traffic Flow prediction. IEEE Transactions on Intelligent Transportation Systems (TITS), 17: 2014-2019, 2016.(SCI二区期刊, IF:5.744)
[17]M. Yin^(#),J. Zhao^(#), S. Sun^(*). Key course selection for academic early warning based on Gaussian processes. The 17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), pp. 240-247, 2016. (EI会议)
[18]J. Zhao^(#)and S. Sun^(*). Revisiting Gaussian Process Dynamical Models. In Proceedings of the 24th International Joint Conference Artificial Intelligence (IJCAI), pp. 1047-1053, 2015.(CCF A类会议)
[19]S. Sun^(#)(*)andJ. Zhaoand J. Zhu. A Review of Nystr?m Methods for Large-Scale Machine Learning. Information Fusion (IF), 26:36-48, 2015.(SCI一区期刊，IF：10.716)
[20]S. Sun^(#)(*)andJ. Zhaoand Q. Gao. Modeling and Recognizing Human Trajectories with Beta Process Hidden Markov Models. Pattern Recognition (PR), 48: 2407-2417, 2015.(SCI二区期刊，IF:5.898)
[21]J. Zhao^(#)and S. Sun^(*). Variational Dependent Multi-output Gaussian Process Dynamical Systems. In Proceedings of the 17th International Conference of Discovery Science (DS), 8777: 350-361, 2014. (EI会议)
授权专利与登记软件著作权如下：
[1]孙仕亮、戴海威、赵静.一种基于变分BP-HMM的人的行为轨迹识别方法：中 国，1.9.（发明专利，已授权）
[2]赵静孙仕亮 基于高斯过程动态系统的多视图机器人手臂控制软件V1.0 2019SR**.（软件著作权，已登记）
[3]赵静孙仕亮 基于高阶高斯过程动态系统的多视图交通流预测软件V1.0 2019SR**.（软件著作权，已登记）



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