基本信息
尚凡华 教授 博导

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博士学科：计算机科学与技术/控制科学与工程
硕士学科：计算机科学与技术/控制科学与工程/电子与通信工程
工作单位：人工智能学院

联系方式
通信地址：陕西省西安市太白南路2号 西安电子科技大学224信箱
邮政编码：710071
电子邮箱：fhshang@xidian.edu.cn
办公地点：主楼II区419


个人简介
尚凡华，教授，硕士/博士研究生导师。现为西安电子科技大学 智能信息处理研究所、智能感知与图像理解教育部重点实验室成员。
2018年 -- 至今， 西安电子科技大学， 教授，博导
2016年 -- 2018年，香港中文大学，副研究员
2013年 -- 2015年，香港中文大学，博士后研究员
2012年 -- 2013年，美国 杜克大学，博士后
2007年 -- 2012年，西安电子科技大学 博士
已在TPAMI、TNNLS、TKDE等顶级期刊和ICML、NIPS、KDD、AAAI、IJCAI、VLDB、AISTATS等顶级国际会议上发表学术论文90余篇，并与国际上多个顶尖科研团队(包括美国康奈尔大学、University of Texas at Austin、新加坡国立大学、南洋理工大学、香港中文大学等)具有良好的长期合作关系。担任包括NeurIPS、ICML、ICLR、CVPR、AAAI、IJCAI、NIPS、KDD、VLDB、ICCV、SDM等在内的机器学习、人工智能、数据挖掘等领域顶级国际会议的程序委员会委员及审稿人，还担任20多个国际学术期刊(TPAMI、TNNLS、TKDE、TSP、TIP等)审稿人。2015年获得陕西省优秀博士学位论文奖，2018年入选华山菁英人才计划。
目前的研究领域包括：机器学习、深度学习、人工智能、大数据、计算机视觉等。

主要研究方向
1．大规模机器学习 2．并行/分布式计算
3．对抗学习/鲁棒网络 4．随机/确定性优化
5．半监督/弱监督学习 6．矩阵/张量大数据解析
News:
July 2021: Congratulations to 罗如意(研二)和牛满堂(研二). Our work, “Progressive Semantic Matching for Video-Text Retrieval”, will appear at Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), 2021. (CCF A)
June 2021: Congratulations to 耿嘉诚(研三)、安维鑫(博一)和朱琪(保研). Our work, “Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications”, is accepted by IEEE Internet of Things Journal, 2021. (SCI 1区, IF: 9.936).
June 2021: Congratulations to 张智慧(研三)和徐涛(研一). Our work, “Principal Component Analysis in the Stochastic Differential Privacy Model”, is accepted by Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (Full oral paper, CCF B)
June 2021: Congratulations to 孔琳(研二)和孙威(保研). Our work, “Learned Interpretable Residual Extragradient ISTA for Sparse Coding”, is accepted by the ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI.
May 2021: Congratulations to魏秉坤(研三). Our work, “Efficient Gradient Support Pursuit with Less Hard Thresholding for Cardinality-Constrained Learning”, is accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (SCI 1区, IF: 11.683)
May 2021: Congratulations to徐涛(研一). Our work, “Differentially Private ADMM Algorithms for Machine Learning”, is conditionally accepted by IEEE Transactions on Information Forensics and Security, 2021. (CCF A, SCI 1区, IF: 6.013)
May 2021: Congratulations to 张智慧(研三)和徐涛(研一). Our work, “Principal Component Analysis in the Stochastic Differential Privacy Model”, will appear at Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (CCF B)
April 2021: Congratulations to 黄华(研二). Our work, “Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning”, will appear at the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. (CCF A, 录用率为13.9%)
March 2021: Congratulations to 黄华(研二). Our work, “Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning”, is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. (CCF A).
February 2021: Congratulations to 耿嘉诚(研三). Our work, “Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications”, is conditionally accepted by IEEE Internet of Things Journal, 2021. (SCI 1区, IF: 9.936).
December 2020: Our work, “Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding” will appear at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (Clear accept, CCF A)Congratulations to Yangyang Li and Lin Kong! 他们是智能学院研二学生。
December 2020: Our work, “Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling” will appear at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A) Congratulations to Peng Zhao and Zhubo Ruan! 其中Peng Zhao是智能学院研一学生,Zhubo Ruan是智能学院研三学生
July 2020: Our work, “Global Convergence Guarantees of (A)GIST for a Family of Noncovex Sparse Learning Problems”, is accepted by IEEE Transactions on Cybernetics, 2020. (SCI1区,IF: 11.079)
June 2020: Our work, “Semantic Segmentation for SAR Image Based onTexture Complexity Analysis and Key Superpixels”, will appear at Journal of Remote Sensing, 2020. (SCI IF: 4.509)
May 2020: Our work,“Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning”, is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. (CCF A，SCI1区, IF: 17.861)
March 2020: Our work, “Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data”, will appear at Journal of Remote Sensing, 2020. (SCI IF: 4.509)
January 2020: Our work,“Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning”, is conditionally accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. (CCF A，SCI1区, IF: 17.861)
祝贺安玉颖同学收到了五个顶级名校学校的offer，有cmu，ucsd，cornell，duke和columbia!
祝贺杨谢丛尤同学，拿到新加坡国立大学、约翰霍普金斯大学、纽约大学等名校的offer！
祝贺王禹旸同学，拿到布朗大学等常青藤名校的offer!
祝贺方思远同学，拿到密歇根安娜堡分校等名校深造的offer！
December 2019：Our work, “A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks”, will appear at the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Portland, USA, 2019.(Best Student Paper Award)
November 2019：Our work, “Deep Residual-Dense Lattice Network for Speech Enhancement”, will appear at The Thirty-Second Innovative Applications of Artificial Intelligence Conference (AAAI), New York, USA, 2020. (CCF A)
October 2019：Our work, “A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images”, will appear at IEEE ICDM Workshop of the 19th IEEE International Conference on Data Mining (ICDM), Beijing, China, 2019. (Best Paper Award)
September 2019：Our work, “Semi-supervised Graph Regularized Deep Non-negative Matrix Factorization with Bi-orthogonal Constraints for Data Representation”, is accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
August 2019: Our work, “Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning”, is accepted for Full Paper at the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing (CCF B). Congratulations to Xiangyang Liu and Bingkun Wei! 其中Xiangyang Liu是智能学院大三本科生，Bingkun Wei是智能学院研一学生。
August 2019: Our work, “CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation”, will appear at the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen.
August 2019: Our work, “Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification”, is accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
July 2019: 我们的综述论文,“Research Advances on Stochastic Gradient Descent Algorithms”，被自动化学报(CCF A类中文期刊)接收出版。
July 2019: Our work, “signADAM: Learning Confidences for Deep Neural Networks”, is available on arXiv.
June 2019: Our work, “Semi-supervised Graph Regularized Deep Non-negative Matrix Factorization with Bi-orthogonal Constraints for Data Representation”, is conditionally accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
June 2019: Our work, “Efficient Semi-Stochastic Gradient Support Pursuit for Sparsity-Constrained Non-convex Optimization”, will appear at IJCAI workshop on Data Science Meets Optimization.(10-minutepresentation)
May 2019: Our work, “Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification”, is conditionally accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
May 2019: Our work, “Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor”, will appear at IJCAI 2019 (CCF A).(15-minutepresentation)
May 2019: Our work, “LRR for Subspace Segmentation via Tractable Schatten-p Norm Minimization and Factorization”, is published by IEEE Transactions on Cybernetics (SCI IF: 10.387).
April 2019: Our work, “Direct Acceleration of SAGA using Sampled Negative Momentum”, appeared at AISTATS 2019.
March 2019: Our work, “Local Discriminative Based Sparse Subspace Learning for Feature Selection”, is published by Pattern Recognition (SCI IF: 5.898).
February 2019: Our work, “Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}”, appeared at AAAI 2019 (CCF A).

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基本信息
尚凡华 教授 博导

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博士学科：计算机科学与技术/控制科学与工程
硕士学科：计算机科学与技术/控制科学与工程/电子与通信工程
工作单位：人工智能学院

联系方式
通信地址：陕西省西安市太白南路2号 西安电子科技大学224信箱
邮政编码：710071
电子邮箱：fhshang@xidian.edu.cn
办公地点：主楼II区419


个人简介
尚凡华，教授，硕士/博士研究生导师。现为西安电子科技大学 智能信息处理研究所、智能感知与图像理解教育部重点实验室成员。
2018年 -- 至今， 西安电子科技大学， 教授，博导
2016年 -- 2018年，香港中文大学，副研究员
2013年 -- 2015年，香港中文大学，博士后研究员
2012年 -- 2013年，美国 杜克大学，博士后
2007年 -- 2012年，西安电子科技大学 博士
已在TPAMI、TNNLS、TKDE等顶级期刊和ICML、NIPS、KDD、AAAI、IJCAI、VLDB、AISTATS等顶级国际会议上发表学术论文90余篇，并与国际上多个顶尖科研团队(包括美国康奈尔大学、University of Texas at Austin、新加坡国立大学、南洋理工大学、香港中文大学等)具有良好的长期合作关系。担任包括NeurIPS、ICML、ICLR、CVPR、AAAI、IJCAI、NIPS、KDD、VLDB、ICCV、SDM等在内的机器学习、人工智能、数据挖掘等领域顶级国际会议的程序委员会委员及审稿人，还担任20多个国际学术期刊(TPAMI、TNNLS、TKDE、TSP、TIP等)审稿人。2015年获得陕西省优秀博士学位论文奖，2018年入选华山菁英人才计划。
目前的研究领域包括：机器学习、深度学习、人工智能、大数据、计算机视觉等。

主要研究方向
1．大规模机器学习 2．并行/分布式计算
3．对抗学习/鲁棒网络 4．随机/确定性优化
5．半监督/弱监督学习 6．矩阵/张量大数据解析
News:
July 2021: Congratulations to 罗如意(研二)和牛满堂(研二). Our work, “Progressive Semantic Matching for Video-Text Retrieval”, will appear at Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), 2021. (CCF A)
June 2021: Congratulations to 耿嘉诚(研三)、安维鑫(博一)和朱琪(保研). Our work, “Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications”, is accepted by IEEE Internet of Things Journal, 2021. (SCI 1区, IF: 9.936).
June 2021: Congratulations to 张智慧(研三)和徐涛(研一). Our work, “Principal Component Analysis in the Stochastic Differential Privacy Model”, is accepted by Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (Full oral paper, CCF B)
June 2021: Congratulations to 孔琳(研二)和孙威(保研). Our work, “Learned Interpretable Residual Extragradient ISTA for Sparse Coding”, is accepted by the ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI.
May 2021: Congratulations to魏秉坤(研三). Our work, “Efficient Gradient Support Pursuit with Less Hard Thresholding for Cardinality-Constrained Learning”, is accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (SCI 1区, IF: 11.683)
May 2021: Congratulations to徐涛(研一). Our work, “Differentially Private ADMM Algorithms for Machine Learning”, is conditionally accepted by IEEE Transactions on Information Forensics and Security, 2021. (CCF A, SCI 1区, IF: 6.013)
May 2021: Congratulations to 张智慧(研三)和徐涛(研一). Our work, “Principal Component Analysis in the Stochastic Differential Privacy Model”, will appear at Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (CCF B)
April 2021: Congratulations to 黄华(研二). Our work, “Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning”, will appear at the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. (CCF A, 录用率为13.9%)
March 2021: Congratulations to 黄华(研二). Our work, “Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning”, is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. (CCF A).
February 2021: Congratulations to 耿嘉诚(研三). Our work, “Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications”, is conditionally accepted by IEEE Internet of Things Journal, 2021. (SCI 1区, IF: 9.936).
December 2020: Our work, “Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding” will appear at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (Clear accept, CCF A)Congratulations to Yangyang Li and Lin Kong! 他们是智能学院研二学生。
December 2020: Our work, “Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling” will appear at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A) Congratulations to Peng Zhao and Zhubo Ruan! 其中Peng Zhao是智能学院研一学生,Zhubo Ruan是智能学院研三学生
July 2020: Our work, “Global Convergence Guarantees of (A)GIST for a Family of Noncovex Sparse Learning Problems”, is accepted by IEEE Transactions on Cybernetics, 2020. (SCI1区,IF: 11.079)
June 2020: Our work, “Semantic Segmentation for SAR Image Based onTexture Complexity Analysis and Key Superpixels”, will appear at Journal of Remote Sensing, 2020. (SCI IF: 4.509)
May 2020: Our work,“Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning”, is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. (CCF A，SCI1区, IF: 17.861)
March 2020: Our work, “Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data”, will appear at Journal of Remote Sensing, 2020. (SCI IF: 4.509)
January 2020: Our work,“Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning”, is conditionally accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. (CCF A，SCI1区, IF: 17.861)
祝贺安玉颖同学收到了五个顶级名校学校的offer，有cmu，ucsd，cornell，duke和columbia!
祝贺杨谢丛尤同学，拿到新加坡国立大学、约翰霍普金斯大学、纽约大学等名校的offer！
祝贺王禹旸同学，拿到布朗大学等常青藤名校的offer!
祝贺方思远同学，拿到密歇根安娜堡分校等名校深造的offer！
December 2019：Our work, “A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks”, will appear at the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Portland, USA, 2019.(Best Student Paper Award)
November 2019：Our work, “Deep Residual-Dense Lattice Network for Speech Enhancement”, will appear at The Thirty-Second Innovative Applications of Artificial Intelligence Conference (AAAI), New York, USA, 2020. (CCF A)
October 2019：Our work, “A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images”, will appear at IEEE ICDM Workshop of the 19th IEEE International Conference on Data Mining (ICDM), Beijing, China, 2019. (Best Paper Award)
September 2019：Our work, “Semi-supervised Graph Regularized Deep Non-negative Matrix Factorization with Bi-orthogonal Constraints for Data Representation”, is accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
August 2019: Our work, “Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning”, is accepted for Full Paper at the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing (CCF B). Congratulations to Xiangyang Liu and Bingkun Wei! 其中Xiangyang Liu是智能学院大三本科生，Bingkun Wei是智能学院研一学生。
August 2019: Our work, “CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation”, will appear at the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen.
August 2019: Our work, “Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification”, is accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
July 2019: 我们的综述论文,“Research Advances on Stochastic Gradient Descent Algorithms”，被自动化学报(CCF A类中文期刊)接收出版。
July 2019: Our work, “signADAM: Learning Confidences for Deep Neural Networks”, is available on arXiv.
June 2019: Our work, “Semi-supervised Graph Regularized Deep Non-negative Matrix Factorization with Bi-orthogonal Constraints for Data Representation”, is conditionally accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
June 2019: Our work, “Efficient Semi-Stochastic Gradient Support Pursuit for Sparsity-Constrained Non-convex Optimization”, will appear at IJCAI workshop on Data Science Meets Optimization.(10-minutepresentation)
May 2019: Our work, “Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification”, is conditionally accepted by IEEE Transactions on Neural Networks and Learning Systems (SCI IF: 11.683).
May 2019: Our work, “Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor”, will appear at IJCAI 2019 (CCF A).(15-minutepresentation)
May 2019: Our work, “LRR for Subspace Segmentation via Tractable Schatten-p Norm Minimization and Factorization”, is published by IEEE Transactions on Cybernetics (SCI IF: 10.387).
April 2019: Our work, “Direct Acceleration of SAGA using Sampled Negative Momentum”, appeared at AISTATS 2019.
March 2019: Our work, “Local Discriminative Based Sparse Subspace Learning for Feature Selection”, is published by Pattern Recognition (SCI IF: 5.898).
February 2019: Our work, “Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}”, appeared at AAAI 2019 (CCF A).

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科学研究
目前研究团队承担的科研项目：
华山菁英人才计划及科研启动经费，2018-2021
国家自然科学基金面上项目，2019-2022
学术报告：
2019年1月，《稀疏和低秩学习研究》，清华大学，北京
2019年1月，《超拉普拉斯先验用于稀疏和低秩学习》，陕西省信号处理学会六届二次理事会暨2019年度学术交流会议，西安
2019年3月，《理论加速的随机方差减少优化研究》，国家天元数学西北中心随机优化及其应用前沿论坛，西安交通大学，西安
2019年4月，《Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications》，第九届视觉与学习青年****研讨会(Vision And Learning Seminar, VALSE), 合肥
2019年4月，《动量加速的随机方差减少优化研究》，中国运筹学会第十二届全国数学优化会议，南京
2019年5月，《应用于大规模机器学习的动量加速随机方差优化研究》，河北工业大学，天津

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学术论文
Recent Research Highlight:(* Corresponding author)
Yuanyuan Liu,Fanhua Shang*, Hongying Liu, Lin Kong, Licheng Jiao, and Zhouchen Lin. Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning.To appear inIEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 2020. (SCI 1区,IF: 16.389,CCF A)
Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". To appear inIEEE Transactions on Knowledge and Data Engineering(TKDE), 32(1):188-202,2020.(SCI 1区,IF: 6.977,CCF A)
Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, and Zhouchen Lin. Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications.IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI),40(9): 2066-2080,2018. (SCI 1区,IF: 16.389,CCF A)
Kaiwen Zhou,Fanhua Shang*,James Cheng.A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. In:Proceedings of the 35th International Conference on Machine Learning (ICML), pp. 5975-5984, 2018.CCF A
Yuanyuan Liu,Fanhua Shang*, James Cheng, Hong Cheng, Licheng Jiao. Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. In:Proceedings of the 31st Conference on Neural Information Processing Systems(NIPS), pp.4875–4884,2017.CCF A
Fanhua Shang, Yuanyuan Liu, and James Cheng. Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. In:Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS,Proceedings of Machine Learning Research),51: 620-629,2016.
Selected Papers:(* Corresponding author, 2020IF)
Hongying Liu, Ruyi Luo, Fanhua Shang*, Mantang Niu, Yuanyuan Liu. “Progressive Semantic Matching for Video-Text Retrieval”. To appear in: Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), 2021. (CCF A)
Hua Huang, Fanhua Shang*, Yuanyuan Liu, Hongying Liu. “Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning”. To appear in: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. (CCF A)
Fanhua Shang, Bingkun Wei, Hongying Liu, Yuanyuan Liu, Pan Zhou and Maoguo Gong. “Efficient Gradient Support Pursuit with Less Hard Thresholding for Cardinality-Constrained Learning”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),2021. (SCI 1区, IF: 10.451)
Fanhua Shang, Hua Huang, Jun Fan, Hongying Liu, Yuanyuan Liu, Jianhui Liu. “Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning”. Accepted byIEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. (SCI 1区,IF: 6.977,CCF A)
Fanhua Shang, Tao Xu, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong. “Differentially Private ADMM Algorithms for Machine Learning”, IEEE Transactions on Information Forensics and Security (TIFS), conditionally accepted, 2021. (SCI 1区, IF: 7.178, CCF A)
Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu. “Principal Component Analysis in the Stochastic Differential Privacy Model”. To appear in:Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (CCF B)
Yuanyuan Liu, Jiacheng Geng, Fanhua Shang*, Hongying Liu, Qi Zhu. “Loopless Variance Reduced Stochastic ADMM forEquality Constrained Problems in IoT Applications”. IEEE Internet of Things Journal, accepted, 2021. (SCI 1区, IF: 9.471)
Yangyang Li, Lin Kong, Fanhua Shang*, Yuanyuan Liu, Hongying Liu, Zhouchen Lin. Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding. To appear in: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (Clear Accept, CCF A)
Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang*, Yuanyuan Liu.Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling. To appear in:Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A)
Jianrui Chen, Yanqing Lu, Fanhua Shang, Yuyang Wang. “A fuzzy matrix factor recommendation method with forgetting function and user features”. Applied Soft Computing, 100: 106910, 2021. (SCI 1区, IF: 6.725)
Ronghua Shang, Lujuan Wang, Fanhua Shang, Licheng Jiao, Yangyang Li. “Dual space latent representation learning for unsupervised feature selection”. Pattern Recognition (PR), 114: 107873, 2021. (SCI 1区, IF: 7.740)
Yuanyuan Liu,Fanhua Shang*, Hongying Liu, Lin Kong, Licheng Jiao, and Zhouchen Lin. Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning.Accepted byIEEE Transactions on Pattern Ansalysis and Machine Intelligence(TPAMI), 2020. (SCI 1区,IF: 16.389,CCF A)
Hengmin Zhang, Feng Qian, Fanhua Shang, Wenli Du, Jianjun Qian, Jian Yang.Global Convergence Guarantees of (A) GIST for a Family of Nonconvex Sparse Learning Problems.IEEE Transactions on Cybernetics, 2020.(SCI 1区,IF: 11.448)
Yang Meng, Ronghua Shang, Fanhua Shang, Licheng Jiao, Shuyuan Yang, Rustam Stolkin.Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation. IEEE Transactions on Neural Networks and Learning Systems,2020. (SCI 1区, IF: 10.451)
Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao, Jun Zhou, Kuldip K. Paliwal, Fanhua Shang*. Deep Residual-Dense Lattice Network for Speech Enhancement. In: Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI), 2020. (CCF A)
Hongying Liu, Fanhua Shang*, Shuyuan Yang, Maoguo Gong, Tianwen Zhu, Licheng Jiao. Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification. Accepted by IEEE Transactions on Neural Networks and Learning Systems,2019. (SCI 1区, IF: 10.451)
Yuanyuan Liu, Fanhua Shang*and Licheng Jiao. Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor. In:Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (CCF A)
Fanhua Shang, BingkunWei, Hongying Liu, Yuanyuan Liu, Jiacheng Zhuo. Efficient Semi-Stochastic Gradient Support Pursuit for Sparsity-Constrained Non-convex Optimization. In:Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI,Workshop of Data Science Meets Optimization), 2019. (CCF A)
Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao. Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}. In:Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019. (CCF A)
Kaiwen Zhou, Qinghua Ding,Fanhua Shang, James Cheng, Danli Li, Zhiquan Luo.Direct Acceleration of SAGA using Sampled Negative Momentum.In:Proceedingsof the 22nd International Conference on Artificial Intelligence and Statistics(AISTATS),2019.
Hengmin Zhang, Jian Yang,Fanhua Shang, Chen Gong, Zhenyu Zhang. LRR for Subspace Segmentation via Tractable Schatten-p Norm Minimization and Factorization.IEEE Transactions on Cybernetics, 2019. (SCI 1区,IF: 11.448)PDF
Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018.(SCI 1区,IF: 6.977,CCF A) PDF
Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin.ASVRG: Accelerated Proximal SVRG.In:Proceedings ofMachine Learning Research (PMLR), 2018.PDF
Kaiwen Zhou,Fanhua Shang*,James Cheng.A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. In:Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.(CCF A)PDF
Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, and Zhouchen Lin. Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence,2018.(SCI 1区,IF: 16.389,CCF A)PDF
Fanhua Shang, Yuanyuan Liu, James Cheng, and Da Yan. Fuzzy Double Trace Norm Minimization for Recommendation Systems.IEEE Transactions on Fuzzy Systems,2018. (SCI 1区, IF:12.029)PDF
Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin K.W. Ng, Yuichi Yoshida.Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. In:Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS, Journal of Machine Learning Research(CCF A)), 2018. PDF
Yuanyuan Liu,Fanhua Shang*, James Cheng, Hong Cheng, Licheng Jiao. Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS),2017.(CCF A) PDF
Fan Yang,Fanhua Shang, Yuzhen Huang, James Cheng, Jinfeng Li, Yunjian Zhao, Ruihao Zhao. LFTF: A Framework for Efficient Tensor Analytics at Scale. In:Proceedings of the 43rd International Conference on Very Large Data Bases (VLDB),2017. (CCF A)PDF
Yuanyuan Liu,Fanhua Shang*, and James Cheng.Accelerated Variance Reduced Stochastic ADMM. In:Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI),2017. (CCF A)PDF
Fanhua Shang, Yuanyuan Liu, and James Cheng. Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. In:Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS,Journal of Machine Learning Research,(CCF A)),2016.PDF
Fanhua Shang, Yuanyuan Liu, and James Cheng. Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. In:Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI),2016.(CCFA) PDF
Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, and Hong Cheng.Robust Bilinear Factorization with Missing and Grossly Corrupted Observations.Information Sciences,53-72,2016. (SCI 1区,IF:6.795)PDF
Yuanyuan Liu,Fanhua Shang*, Wei Fan, James Cheng, and Hong Cheng.Generalized Higher-Order Orthogonal Iteration for Tensor Learning and Decomposition.IEEE Transactions on Neural Networks and Learning Systems,27, 2551-2563,2016.(SCI 1区, IF: 10.451)PDF
Yuanyuan Liu,Fanhua Shang*, Licheng Jiao, James Cheng, and Hong Cheng.Trace Norm Regularized CANDECOMP/PARAFAC Decomposition with Missing Data.IEEE Transactions on Cybernetics,45, 2437-2448,2015.(SCI 1区,IF: 11.448)PDF
Fanhua Shang, Yuanyuan Liu, and James Cheng. Generalized Higher-Order Tensor Decomposition via Parallel ADMM. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI),2014.(CCFA) PDF
Yuanyuan Liu,Fanhua Shang*, Wei Fan, James Cheng, and Hong Cheng.Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion. In: Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS),1763-1771,2014. (CCF A)PDF
Yuanyuan Liu,Fanhua Shang*, Hong Cheng, and James Cheng. Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds. In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI),2014. (CCF B)PDF
Fei Yin, Licheng Jiao,Fanhua Shang, Lin Xiong, Xiaodong Wang. Sparse regularization discriminant analysis for face recognition.Neurocomputing,128, 341-362,2014.(SCI,IF:5.719)
Yuanyuan Liu,Fanhua Shang*, Hong Cheng, James Cheng, and Hanghang Tong. Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM),2014. (CCF B)PDF
Fanhua Shang, Yuanyuan Liu, and James Cheng, Hong Cheng.Robust Principal Component Analysis with Missing Data. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM),2014. (CCF B)PDF
Fanhua Shang, Yuanyuan Liu, James Cheng, and Hong Cheng. Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization. In: Proceedings of the 14th IEEE International Conference on Data Mining (ICDM),2014. (CCF B)PDF
Fei Yin, Licheng Jiao,Fanhua Shang, Lin Xiong, Shasha Mao. Double linear regressions for single labeled image per person face recognition.Pattern Recognition,47(4),1547-1558,2014.(SCI 1 区, IF: 7.740)
Jing Chai, Hongtao Chen, Lixia Huang,Fanhua Shang.Maximum margin multiple-instance feature weighting.Pattern Recognition,47(6), 2091-2103,2014.(SCI 1 区, IF: 7.740)
Yuanyuan Liu, Licheng Jiao, andFanhua Shang. An Efficient Matrix Factorization Based Low-Rank Representation for Subspace Clustering.Pattern Recognition,2013.(SCI 1 区, IF: 7.740)PDF
Fei Yin, Licheng Jiao,Fanhua Shang, Shuang Wang, Biao Hou.Fast Fisher Sparsity Preserving Projections.Neural Computing and Applications,23(3-4) ,691-705,2013.(SCI,IF:5.605)
Yuanyuan Liu, Licheng Jiao, andFanhua Shang*. An Efficient Matrix Bi-Factorization Alternative Optimization Method for Trace Norm Minimization.Neural Networks,2013.(SCI1区, IF: 8.050)PDF
Fanhua Shang, Licheng Jiao, Yuanyuan Liu, and Hanghang Tong. Semi-Supervised Learning with Nuclear Norm Regularization.Pattern Recognition,2013.(SCI 1 区, IF: 7.740)PDF
Yuanyuan Liu, Licheng Jiao, andFanhua Shang. A Fast Tri-Factorization Method for Low-Rank Matrix Recovery and Completion.Pattern Recognition,2013.(SCI 1 区, IF: 7.740)PDF
Fanhua Shang, Licheng Jiao, and Fei Wang. Graph Dual Regularization Non-Negative Matrix Factorization for Co-Clustering.Pattern Recognition,2012.(SCI 1 区, IF: 7.740)PDF
Fanhua Shang, Licheng Jiao, and Fei Wang. Semi-Supervised Learning with Mixed Knowledge Information. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD),2012. (CCF A)PDF
Licheng Jiao,Fanhua Shang*, Fei Wang, and Yuanyuan Liu. Fast Semi-Supervised Clustering with Enhanced Spectral Embedding.Pattern Recognition,2012.(SCI 1 区, IF: 7.740)PDF
Fanhua Shang, Licheng Jiao, Jiarong Shi, and Fei Wang, Maoguo Gong. Fast Affinity Propagation Clustering: A Multilevel Approach.Pattern Recognition,2012.(SCI 1 区, IF: 7.740)PDF
Fanhua Shang, Licheng Jiao, Yuanyuan Liu, and Fei Wang. Learning Spectral Embedding via Iterative Eigenvalue Thresholding. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM),2012. (CCF B)
Fanhua Shang, Licheng Jiao, Jiarong Shi, Maoguo Gong, and R. H. Shang. Fast Density-Weighted Low-Rank Approximation Spectral Clustering.Data Mining and Knowledge Discovery,23,345-378,2011.(SCI,IF:3.670)PDF
Fanhua Shang, Yuanyuan Liu, Fei Wang. Learning Spectral Embedding for Semi-Supervised Clustering. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM),2011. (CCF B)PDF

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荣誉获奖
2015年陕西省优秀博士学位论文奖；
2018年入选华山菁英人才计划
2019年国际数据挖掘会议(IEEE ICDM, CCF B类会议)Workshop最佳论文奖
2019年国际人工智能工具会议(IEEE ICTAI, CCF C类会议)最佳论文奖

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科研团队
机器学习与大数据(MiG)研究中心
尚凡华 教授
刘园园 教授
指导/协助指导博士生数名：
安维鑫 (2020年--至今) 杨琳琳 (2020年--至今)
指导/协助指导硕士生：
魏秉坤 (2018年--至今）张智慧 (2018年--至今） 张超龙(2018年--至今） 耿嘉诚(2018年--至今）
孔 琳(2019年--至今） 任 岩 (2019年--至今） 唐文我(2019年--至今） 施启睿(2019年--至今）
黄 华(2019年--至今） 李杨杨(2019年--至今） 张世豪(2019年--至今） 牛满堂(2019年--至今）
朱天文(2019年--至今） 罗如意(2019年--至今）
指导学生:
Qinghua Ding (本科就读于清华大学; CUHK MPhil, 2018-present): Stochastic Optimization for Machine Learning
Kaiwen Zhou (本科毕业于复旦大学; CUHK MPhil, 2017-present): Large-Scale Machine Learning
Fan Yang (本科毕业于中山大学; CUHK Phd, 2014-2018; Google DeepMind): Distributed Machine Learning
Jiacheng Zhuo: FYP 2016-2017 [after CUHK: PhD study at the University of Texas at Austin]
Junhui Cai: FYP 2016-2017 [after CUHK: PhD study at University of Pennsylvania]

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课程教学
目前承担的教学任务：
《模式识别》（硕士/博士研究生）2018春(250人左右)/2019春(350人左右)
《机器学习与深度学习理论》 （硕士/博士研究生） 2019春
《深度学习导论》（本科生）2018年秋(150人左右)/2019秋(200多人）
《科技论文写作》（本科生）2018年秋

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招生要求
博士研究生招生专业：
081200 计算机科学与技术
081100 控制科学与工程
硕士研究生招生专业：
081200 计算机科学与技术
081100 控制科学与工程
085208 电子与通讯工程
085211 计算机技术
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研究生招生的信息：
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团队每年计划招1～2名博士、7～8名学术型硕士（含推免保送生）和2～3名专业型硕士；接收外校保送生名额不限。
若考生有下列情况之一，录取时会优先考虑：
（1）具备良好的数学功底（包括数学专业的学生）；
（2）具备良好的英文读写能力；
（3）具有较强的编程能力；
（4）参加各类国家级竞赛并取得了较好的成绩；
希望考生有进取心和较强的团队合作精神。咨询的最佳联系方式是Email联系，见信后会尽快回复。

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硕士研究生

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Admission
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