李俊

发布人:赵学龙审核人:张世刚 时间:2020-01-10浏览:4079设置

  更新日期:2020年1月10日



姓 名

李俊

性 别

出生年月

198312

四川仁寿

民 族

政治面z

中共党员

最后学历

博士研究生

最后学位

工学博士

技术职称

教授

行政职务


单位电话


Email

junli@njust.edu.cn

工作单位

计算机科学与工程学院

邮政编码

210094

通讯地址

南京市孝陵卫200

个人主页

https://sites.google.com/view/junlineu/


工作经历

2019.11----至今:南京理工大学     教授

2018.11-2019.10:美国麻省理工学院 博士后

2015.12-2018.10:美国东北大学     博士后

教育经历

2011.09-2015.11: 南京理工大学     博士

2012.10-2013.07:美国罗格斯大学   访问学生

获奖、荣誉称号

 2016 Microsoft-MSR Image Recognition Challenge@ACM Multimedia 竞赛冠军

 2011 四川省优秀硕士论文

社会、学会及学术兼职

SPC/PC member for AAAI/IJCAI/ACM MM/FG.

reviewer for IEEE TNNLS/TIP/TKDE/TIFS/TCSVT/TMM/TBIOM et. al.

发表论文

Selected Publications:

Jun Li, Gan Sun, Guoshuai Zhao and Li-wei Lehman, Robust Low-Rank Discovery of Data-Driven Partial Differential Equations, 34th AAAI Conference on Artificial Intelligence, AAAI-20, oral.

 Gan Sun, Yang Cong, Qianqian Wang, Jun Li and Yun Fu, Lifelong Spectral Clustering, 34th AAAI Conference on Artificial Intelligence, AAAI-20.

 Hongfu Liu#, Jun Li#, Yue Wu and Yun Fu Clustering with Outlier Removal, IEEE Transactions on Knowledge and Data Engineering, 2019, # co-first author.

 Shuo Chen, Lei Luo, Chen Gong, Jian Yang, Jun Li and Heng Huang, Curvilinear Distance Metric Learning, Thirty-third Annual Conference on Neural Information Processing Systems, NeurIPS-19.

 Wei Luo, Xitong Yang, Xianjie Mo, Yuheng Lu, Larry Davis, Jun Li, Jian Yang and Ser-Nam Lim, Cross-X Learning for Fine-Grained Visual Categorization, IEEE International Conference on Computer Vision, ICCV-19.

 Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, and Yun Fu, Adversarial Graph Embedding for Ensemble Clustering, 28th International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 3562-3568.

Jun Li, Hongfu Liu and Yun Fu, Predictive Coding Machine for Compressed Sensing and Image Deblurring, In: 32th AAAI Conference on Artificial Intelligence, AAAI-18, pp. 3506-3513.

Jun Li, Heyou Chang, Jian Yang, Wei Luo and Yun Fu, Visual Representation and Classification by Learning Group Sparse Deep Stacking Network, IEEE Transactions on Image Processing, Vol. 27, No. 1, Jan. 2018, pp. 464-476.

 Wei Luo#, Jun Li#, Jian Yang, Wei Xu and Zhang, Jian, Convolutional Sparse Auto-Encoder for Image Classification, IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 7, July 2018, pp. 3289-3294. # co-first author.

Jun Li, Tong Zhang, Wei Luo, Jian Yang, Xiaotong Yuan, and Jian Zhang, Sparseness Analysis in the Pertraining of Deep Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 6, Jun. 2017, pp. 1425-1438.

Jun Li, Hongfu Liu, Handong Zhao, and Yun Fu, Projective Low-rank Subspace Clustering via Learning Deep Encoder, 26th International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 2145-2151.

Jun Li, Handong Zhao, Zhiqiang Tao and Yun Fu, Large-scale Subspace Clustering by Fast Regression Coding, 26th International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 2138-2144.

Jun Li, Yu Kong and Yun Fu, Sparse Subspace Clustering by Learning Approximation $\ell_0$ Codes, 31th AAAI Conference on Artificial Intelligence, AAAI-17, pp. 2189-2195.

Jun Li, Yu Kong, Handong Zhao, Jian Yang and Yun Fu, Learning Fast Low-Rank Projection for Image Classification, IEEE Transactions on Image Processing, Vol. 25, No. 10, Oct. 2016, pp.4803-4814.

 Yue Wu, Jun Li, Yu Kong and Yun Fu, Deep Convolutional Neural Network with Independent Softmax for Large Scale Face Recognition, The first place in ACM Multimedia-Microsoft-MSR Image Recognition Challenge, ACM MM 2016, pp. 1063-1067.

Jun Li, Heyou Chang and Jian Yang, Sparse Deep Stacking Network for Image Classification, 29th AAAI Conference on Artificial Intelligence, AAAI-15, pp. 3804-3810.

Jun Li, Wei Luo, Jian Yang and Xiaotong Yuan, Unsupervised Pretraining Encourages Moderate-Sparseness, International Conference Machine Learning, ICML 2014---workshop: uLearnBio.

指导学生情况

博士生:许叶松(co-supervison with Prof. Jian Yang)

指导原则:理论技术应用。

研究方向:机器学习、计算机视觉,机器学习与基础科学(数学、物理和化学)的交叉结合。

欢迎有较好的数学、英语基础和较强编程能力,且对机器学习、深度学习和计算机视觉等领域感兴趣的同学与我联系,尤其鼓励有计算物理和计算化学背景的同学。

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