尚凡华 教授  博导

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

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

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。

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 on Texture 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-minute presentation)

• 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-minute presentation)

• 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).