李春光

姓名: 李春光(Chun-Guang Li)
所属专业: 信息与通信工程 / 信号与信息处理    专业代码: 081000 / 081002
职称: 副教授
院系: 信息与通信工程学院
研究方向:机器学习与数据科学
电话:62283059-1007
邮箱:lichunguang[AT]bupt[dot]edu[dot]cn
Homepage in English: 点击这里

个人简介:  男,1979年11月出生,籍贯辽宁凌源。2002年7月毕业于吉林大学通信工程学院,获工学学士学位;2007年12月毕业于北京邮电大学信息工程学院,获工学博士学位,同年留校执教,现为北京邮电大学信息与通信工程学院副教授。2011年7月至2012年4月访问于微软亚洲研究院(MSRA)视觉计算组。2012年12月至2013年11月底访问于美国约翰霍普金斯大学(The Johns Hopkins University)生物医学工程系成像科学研究中心视觉-动态-学习实验室。研究方向为机器学习与数据科学,研究兴趣为高维数据建模、分析与学习及其在信号处理、模式识别、生物信息学以及中医信息学中的应用,包括稀疏/低秩模型(子空间聚类与矩阵填充)、流形学习(流形聚类与半监督学习)、图像局部特征抽取、基因表达谱与基因调控网络分析等。近年来,主持完成留学回国人员科研启动项目1项,主持完成国家自然科学基金青年科学基金1项,主持完成国际合作项目1项, 主持完成北京邮电大学科研创新专项3项,参加完成国家自然科学基金面上项目4项,参与完成国家自然科学基金委与英国皇家学会合作交流项目1项。累计在本领域国际学术会议和学术期刊上发表研究论文30余篇。现为IEEE/ACM/CCF会员和CCFCV专委会委员。

最新动态(Latest News)

  • Chong YouRene Vidal教授合作的研究论文被IEEE Transactions on Image Processing 接收![2017-03-20]
  • 张洪刚郭军教授合作指导的博士研究生四建楼的研究论文被IEEE Transactions on Systems, Man and Cybernetics: Systems 接收![pdf] [2016-12-25]
  • Rene Vidal教授合作的研究论文被IEEE Transactions on Signal Processing 接收![2016-08-30]
  • Chong You, Daniel Robinson Rene Vidal教授合作的研究论文被IEEE CVPR2016 接收!(oral) [2016-03-04]
  • 申请晋升信息与通信工程学院信息与通信工程学科副教授喜获通过![2015-12-04]

教育与工作\研究经历

研究方向: 统计机器学习与数据科学,研究兴趣为高维数据建模、分析与学习及其在信号处理、模式识别、生物信息学与中医信息学中的应用,包括稀疏表示、矩阵填充、子空间聚类、流形学习与半监督学习、图像特征抽取与分类、基因表达谱与基因调控网络分析等。

  • 近年来,主持完成国家自然科学基金青年科学基金1项,主持完成留学回国人员科研启动项目1项,参加完成国家自然科学基金面上项目4项,参与完成国家自然科学基金委与英国皇家学会合作交流项目1项,主持完成北京邮电大学科研创新专项3项,主持完成国际合作项目1项。
  • 在IEEE TPAMI/TIP/TSP/JBHI/TITS/TSMC, ICCV, CVPR, Neurocom., IJPRAI, BMVC, ACCV, ICDAR, ACM Multimedia, ICPR, ICASSP, ICIP, VCIP等国际期刊和国际会议上发表论文30余篇。现为国际电子电气工程师协会(IEEE)会员,国际计算机协会(ACM)会员,中国计算机协会(CCF)会员和计算机视觉专委会(CCFCV)委员,亚太信号与信息处理协会(APSIPA)技术委员会成员。

 研究项目

  • [7] 基于结构化低秩准则的缺值填充问题研究, 主持, 教育部留学回国人员科研启动项目, 项目批准号: 留48, 2014.09-2016.12. [已结题]
  • [6] 基于激活力的复杂网络建模及其应用, 参与(排名第3位), 国家自然科学基金面上项目, 项目批准号: 61273217, 2013.01-2016.12. [已结题]
  • [5] 国家自然科学基金委与英国皇家学会合作交流项目, 参与, 项目批准号: 61511130081, 2015.04-2017.03. [已结题]
  • [4] 高维模式分析与学习, 主持, 北京邮电大学青年科研创新计划专项,课题编号:2012R0108, 2012.01-2013.12. [已结题]
  • [3] 基于视觉认知的图像不变特征提取, 参与(排名第2位), 国家自然科学基金面上项目, 项目批准号: 61175011, 2012.01-2015.12. [已结题]
  • [2] 丛流形学习及其在物体识别中的应用, 主持, 国家自然科学基金委青年科学基金, 项目批准号:61005004, 2011.01-2011.12. [已结题]
  • [1] 基于多种物体识别的标签生成技术项目(MORE), 主持, 企业合作项目(编号I068-2008) 2008.11 至 2009.01. [已结题]

代表性论文

  • [10] Chun-Guang Li, Chong You, and René Vidal, “Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework“, IEEE Transactions on Image Processing, Vol. 26, No. 6, pp.2988-3001, June 2017. DOI:10.1109/TIP.2017.2691557 [pdf][code]
  • [9] Chun-Guang Li and Rene Vidal. “A Structured Sparse plus Structured Low-Rank Framework for Subspace Clustering and Completion”, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp.6557-6570, Dec.15, 2016. [pdf] DOI: 10.1109/TSP.2016.2613070
  • [8] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Lag Vegas, Nevada, US. [pdf][code][spotlight][poster]
  • [7] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo, “Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning“, In Proc. of IEEE International Conference on Computer Vision (ICCV), Dec. 11-18, 2015, pp.2767-2775, Santiago, Chile. [pdf][spotlight][poster][code]
  • [6] Chun-Guang Li and René Vidal, “Structured Sparse Subspace Clustering: A Unified Optimization Framework“, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.277-286, June 7-12, 2015, Boston, Massachusetts, US.[pdf][code][poster]
  • [5] Xianbiao Qi, Rong Xiao, Chun-Guang Li, Yu Qiao, Jun Guo, and Xiaoou Tang, “Pairwise Rotation Invariant Co-occurrence Local Binary Pattern“, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 11, Nov. 2014, pp.2199-2213. DOI: 10.1109/TPAMI.2014.2316826 [pdf][code]
  • [4] Chun-Guang Li, Zhouchen Lin, and Jun Guo, “Bases Sorting: Generalizing the Concept of Frequency for Over-complete Dictionaries“, Neurocomputing, Vol.115, pp.192-200, 2013. [pdf][code]
  • [3] Chun-Guang Li, Jun Guo and Hong-gang Zhang, “Local Sparse Representation based Classification“, In Proc. of the 20th International Conference on Pattern Recognition (ICPR), August 23-26, 2010, Istanbul, Turkey. [pdf][code]
  • [2] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Learning Bundle Manifold by Double Neighborhood Graphs“, The 9th Asian Conference on Computer Vision (ACCV), 2009, LNCS 5996, Part III, pp. 321-330. [code][pdf]
  • [1] Chun-Guang Li and Jun Guo, “Supervised Isomap with explicit mapping“, In Proc. of the First International Conference on Innovative Computing, Information and Control, Vol.3, 2006, pp.345-348. [pdf][code]

完整论文列表 (*鼠标停留在链接上,可提示密码)

I. 国际期刊: 

  • 2017
  • [10] Chun-Guang Li, Chong You, and René Vidal, “Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework“, IEEE Transactions on Image Processing, Vol. 26, No. 6, pp.2988-3001, June 2017. DOI:10.1109/TIP.2017.2691557 [pdf][code]
    [9] Shuai Di, Honggang Zhang, Chun-Guang Li, Xue Mei, Danil Prokhorov, and Haibing Ling, “Cross-domain Traffic Scene Understanding: A Dense Correspondence based Transfer Learning Approach”, IEEE Transactions on Intelligent Transportation System, May 25, 2017. DOI: 10.1109/TITS.2017.2702012 [pdf]
    [8] Jianlou Si, Honggang Zhang, Chun-Guang Li, and Jun Guo, “Spatial Pyramid-Based Statistical Features for Person Re-Identification: A Comprehensive Evaluation”, IEEE Transactions on Systems, Man and Cybernetics: Systems, May 23, 2017. DOI: 10.1109/TSMC.2016.2645660 [pdf][code]
    [7] Xianbiao Qi, Guoying Zhao, Chun-Guang Li, Jun Guo, Matti Pietikainen, “HEp-2 Cell Classification via Combining Multi-resolution Co-occurrence Texture and Large Regional Shape Information”, IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol.21, No.2, pp.429-440, 2017. DOI: 10.1109/JBHI.2015.2508938 [pdf ][code]

  • 2016
  • [6] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, Available on arXiv: 1605.02633, 2016. [pdf][code]
    [5] Chun-Guang Li and Rene Vidal. “A Structured Sparse plus Structured Low-Rank Framework for Subspace Clustering and Completion”, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp.6557-6570, Dec. 15, 2016. [pdf] DOI: 10.1109/TSP.2016.2613070
    [4] Xianbiao Qi, Chun-Guang Li, Guoying Zhao, Xiaopeng Hong, Matti Pietikainen, “Dynamic texture and scene classification by transferring deep image features”, Neurocomputing, Vol.171, 2016, pp:1230-1241.[pdf]

  • 2014
  • [3] Xianbiao Qi, Rong Xiao, Chun-Guang Li, Yu Qiao, Jun Guo, and Xiaoou Tang, “Pairwise Rotation Invariant Co-occurrence Local Binary Pattern“, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 11, Nov. 2014, pp.2199-2213. DOI: 10.1109/TPAMI.2014.2316826 [pdf][code]

  • 2013
  • [2] Chun-Guang Li, Zhouchen Lin, and Jun Guo, “Bases Sorting: Generalizing the Concept of Frequency for Over-complete Dictionaries”, Neurocomputing, Vol.115, Sept. 4, 2013, pp.192–200. [pdf][code][data]

  • 2009
  • [1] Chun-Guang Li, Jun Guo, and Bo Xiao, “Intrinsic Dimensionality Estimation within Neighborhood Convex Hull“, International Journal of Pattern Recognition and Artificial Intelligence, Vol.23, No.1, Feb 2009, pp.31-44. [pdf]

II. 国际会议:

  • 2017
  • [27] Chun-Guang Li, who, and who, “bla bla bla”, Submitted to 31st Conference on Neural Information Processing Systems (NIPS 2017).

  • 2016
  • [26] Junjian Zhang, Chun-Guang Li, Honggang Zhang, and Jun Guo, “Low Rank and Structured Sparse Subspace Clustering”, In Proc. of IEEE International Conference on Visual Communication and Image Processing (VCIP), Nov. 27-30, 2016, Chengdu, China.[pdf]
    [25] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Lag Vegas, Nevada, US, pp.3928-3937. [pdf][code] (Oral, rate: <3.9%)

  • 2015
  • [24] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo, “Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning“, In Proc. of IEEE International Conference on Computer Vision (ICCV), Dec. 11-18, 2015, pp.2767-2775, Santiago, Chile. [pdf][spotlight][poster][code] (An extended version with theoretical investigation and extensive experimental evaluations will be available soon)
    [23] Zhen Qin, Chun-Guang Li, Honggang Zhang, and Jun Guo, “Improving Tag Matrix Completion for Image Annotation and Retrieval”, In Proc. of IEEE International Conference on Visual Communication and Image Processing (VCIP), Dec. 13-16, 2015, Singapore.[pdf]
    [22] Chun-Guang Li, Chong You, and René Vidal, “On Sufficient Conditions for Affine Sparse Subspace Clustering“, In Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, July 6-9, 2015, Cambridge, UK.[pdf]
    [21] Jianlou Si, Honggang Zhang, and Chun-Guang Li, “Regularization in Metric Learning for Person Re-Identification“, In Proc. of IEEE Conference on Image Processing (ICIP), Sept. 27-30, 2015, Quebec, Canada.[pdf]
    [20] Chun-Guang Li and René Vidal, “Structured Sparse Subspace Clustering: A Unified Optimization Framework“, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.277-286, June 7-12, 2015, Boston, Massachusetts, US.[pdf][code]

  • 2014
  • [19] Jianlou Si, Honggang Zhang, and Chun-Guang Li, “Person Re-Identification via Region-of-Interest based Features“, In Proc. of IEEE Conference on Visual Communications and Image Processing (VCIP), Dec.7-10, 2014, Valletta, Malta.[pdf]

  • 2013
  • [18] Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo, “Exploring Cross-Channel Texture Correlation for Color Texture Classification“, British Machine Vision Conference (BMVC), Sept 9-13, 2013, Bristol, UK. [pdf]
    [17] Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo, “Multi-scale Joint Encoding of Local Binary Patterns for Texture and Material Classification“, British Machine Vision Conference (BMVC), Sept 9-13, 2013, Bristol, UK. [pdf]
    [16] Xianbiao Qi, Yi Lu, Shifeng Chen, Chun-Guang Li, and Jun Guo, “Spatial Co-Occurrence of Local Intensity Order for Face Recognition“, ICME Workshop on Management Information Systems (MIS) in Multimedia Art, Education, Entertainment, and Culture (MIS-MEDIA), July 15-19, 2013, San Jose, USA. [pdf]
    [15] Qiang Wang, Zhiyuan Guo, Gang Liu, Chun-Guang Li, Jun Guo, “Local alignment for query by humming“, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, May 2013.[pdf]

  • 2012
  • [14] Chun-Guang Li, Xianbiao Qi, and Jun Guo, “Dimensionality Reduction by Low-Rank Embedding“, The 2012 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (ISciDE2012), LNCS 7751, pp.181-188, 2013. [pdf]
    [13] Xianbiao Qi, Rong Xiao, Lei Zhang, Chun-Guang Li, and Jun Guo, “A Rapid Flower/Leaf Recognition System“, The 20th anniversary ACM Multimedia (ACM MM), 2012, Nara. [pdf]
    [12] Chun-Guang Li, Xianbiao Qi, Jun Guo and Bo Xiao, “An Evaluation on Different Graphs for Semi-supervised Learning”, The 2011 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (ISciDE2011), LNCS 7202, pp. 58-65, 2012. [pdf]

  • 2010
  • [11] Chun-Guang Li, Jun Guo and Hong-gang Zhang, “Local Sparse Representation based Classification”, The 20th International Conference on Pattern Recognition (ICPR), August 23-26, 2010, Istanbul, Turkey. [pdf]

  • 2009
  • [10] Qianfang Xu, Chun-Guang Li, Bo Xiao, Jun Guo, “A Visualization Algorithm for Alarm Association Mining”, International Conference on Network Infrastructure and Digital Content, pp. 326-330, 2009.[pdf]
    [9] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Learning Bundle Manifold by Double Neighborhood Graphs“, The 9th Asian Conference on Computer Vision (ACCV), 2009, LNCS 5996, Part III, pp. 321-330. [pdf][code]
    [8] Chuang Zhang, Ming Wu, Chun-Guang Li, Bo Xiao, Zhiqing Lin, “Resume Parser: Semi-structured Chinese Document Analysis“, CSIE (5), 2009, pp.12-16. [pdf]
    [7] Hong-gang Zhang, Jun Guo, Guang Chen, and Chun-Guang Li, “HCL2000 — A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition“, ICDAR 2009, pp.286-290. [pdf]

  • 2007
  • [6] Chun-Guang Li, Jun Guo, and Xiangfei Nie, “Intrinsic Dimensionality Estimation with Neighborhood Convex Hull”, Proceeding of the International Conference on Computational Intelligence and Security, 2007. [pdf]
    [5] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Pruning Neighborhood Graph for Geodesic Distance based Semi-Supervised Classification“, Proceeding of the International Conference on Computational Intelligence and Security 2007, pp.428-432. [pdf]

  • 2006
  • [4] Xiangfei Nie, Jun Guo, Zhen Yang, Chun-Guang Li, Jian Wang, Weihong Deng, “EMD Based Face Gender Discrimination“, The Sixth World Congress on Intelligent Control and Automation (WCICA), Vol.1, pp. 4078-4081, 2006.[pdf]
    [3] Chun-Guang Li, Jun Guo, and Xiangfei Nie, “Learning geodesic metric for out-of-sample extension of isometric embedding“, Proceeding of the International Conference on Computational Intelligence and Security 2006, Part I, 2006, pp.449-452. [pdf]
    [2] Chun-Guang Li and Jun Guo, “Supervised Isomap with explicit mapping“, Proceeding of the First International Conference on Innovative Computing, Information and Control, Vol.3, 2006, pp.345-348. [pdf][code]
    [1] Chun-Guang Li, Jun Guo, Guang Chen, Xiangfei Nie, and Zhen Yang, “A version of Isomap with explicit mapping“, Proceeding of the International Conference on Machine Learning and Cybernetics, Vol.6, 2006, pp.3201-3206. [pdf]

III. 国内期刊:

  • 2008
  • [3] Bo Xiao, Qian-Fang Xu, Zhiqing Lin, Jun Guo and Chun-Guang Li, “Credible Association Rule and Its Mining Algorithm Based on Maximum Clique“, Journal of Software, Vol.19,No.10,2008,pp.2597-2610.[In Chinese][pdf]

  • 2007
  • [2] Xiang-Fei Nie, Chun-Guang Li and Jun Guo, “Face recognition based on Gabor wavelet and locally linear embedding“, Computer Engineering and Applications, Vol.43, No. 18, 2007, pp.62-64.[In Chinese]
    [1] Xiang-Fei Nie, Chun-Guang Li and Jun Guo, “Face Detection Based on Empirical Mode Decomposition and Matching Pursuit“, Computer Engineering, Vol.33, No.14, July, 2007, pp.30-33.[In Chinese]

IV. 博士学位论文:

  • 李春光, “流形学习及其在模式识别中的应用 (Manifold Learning and its Applications in Pattern Recognition)”, 北京邮电大学博士学位论文, 2007年12月. [pdf]

V. 课程讲义:

  • 李春光, “机器学习与数据科学讲义 (Lecture Notes in Machine Learning and Data Science)”, 正在准备中, 2008年6月-现在. [pdf]

外语能力:

  • [2] 第2外语: 英语 (全国公共英语等级考试 第5级*)
  • [1] 第1外语: 日语 (日语国际水平测试 1级*)
  • *: 最高级 (top level).

讲授课程

  1. 离散数学(本科生) [2008 春季]
  2. 数字信号处理(本科生) [2009 秋季]
  3. 生物信息基础(本科生) [2012 春季] [2014 秋季] [2015 秋季][2016 秋季][2017 秋季 教三-546 每周四下午 15:30-17:20 ]
    In this course we will introduce how to use computer to handle bioinformatics data. The contents cover the following topics: (1) Brief history of bioinformatics; (2) Basics in model biology; (3) Frequently used bioinformatics database; (4) Sequence analysis: theory, algorithms, and applications; (5) Protein structures and functions prediction; (6) Gene expression data analysis and etc. Lecture Slides are listed below:
    [1] 课程介绍 绪论
    [2] 生物学基础回顾
    [3] 数据库介绍
    [4] 序列分析
    [5] 基因识别与基因组分析
    [6] 隐马尔科夫模型
    [7] 系统发育分析
    [8] 基因表达谱分析
  4. 模式识别引论(研究生) [2014 秋季]
    [1]引言 ( slides )
    [2]部分数学基础 ( slides )
    [3]线性回归 ( slides A , slides B)
    [4]线性模型for分类 ( slides )
    [5]神经网络 ( slides )
    [6]核方法 ( slides )
    [7]支持向量机 ( slides )
  5. [8]其它 (聚类\降维等) ( slides)
  6. 神经计算(研究生) [2008-2012 春季][2014-2015 春季][2016 春季][2017年改为“机器学习与数据科学”]:
    This course covers the most machine learning techniques. Topics can be divided into three categories: 1) the classical topics of neural networks (e.g., Perceptron, Multi-layer Perceptron, Regularized Networks, and Self-Organization Map, and Deep Networks); 2) a series of statistical learning theories (e.g., the classical error analysis via bias-variance decomposition, Vapnik’s statistical learning theory and VC-dimension, regularization theory); 3) the related learning machines/algorithms (e.g., bagging, boosting/AdaBoost, Mixture of Experts, Decision Tree, Support Vector Machine, Kernel Methods, and Kernel Machine, Regularization networks), and other active topics in recent machine learning community (e.g., Manifold Learning, Subspace Clustering, Compressed Sensing, Sparse Representation, Low-Rank model, Matrix Completion and Sensing).
  7. 机器学习与数据科学 (面向学术型研究生) [2017年春季开课,48学时,3学分]
  8. [开课基本信息: (3月1日起) 周三 上午8:00-10:50 教三-233]
    [课程变更说明]
    [课程教学大纲]
    [课程教学要求]:覆盖机器学习与数据科学领域的典型算法,从最基本的学习算法——如最近邻、线性回归和感知器等——到支持向量机、深度学习,同时涵盖流形学习、压缩感知等无监督学习方面的最新进展。除了介绍应用和算法之外,本课程强调与算法相关的理论部分的介绍。在理论方面,涵盖参数估计、偏倚方差分解、统计学习理论和正则化理论;在学习范式方面,涵盖有监督学习、无监督学习与半监督学习等;在课程内容设置上,力争衔接基本概念、经典理论和研究前沿。希望通过本课程的学习,为今后有志于在模式识别、机器学习、数据科学等领域内从事相关研究的研究生打下坚实基础。
    * 所有拟计入成绩的平时作业: 请于6月21日下午3点之前完成提交; 期末大作业: 请于6月28日下午3点之前完成提交; 作业提交地点: 教三楼8层 803 模式识别与智能系统实验室. (个别缺席6月14日课的同学请尽快找我取走期末大作业题目纸)
    专题-1: 课程内容介绍 [slides]
    专题 0: 学习问题的发展简史 [slides]
    专题 1: 基于实例的学习 [part-0][part-I][part-II][练习与作业]
    专题 2: 线性模型 [part-I][part-II][part-III][补充材料][练习与作业]
    专题 3: 线性模型的扩展 [part-I][part-II][练习与作业]
    专题 4: 学习过程的统计性质 [part-I][part-II][练习与作业]
    专题 5: 支持向量机与统计学习理论 [part-I][part-II][练习与作业]
    专题 6: 正则化理论 [part-I][part-II][练习与作业]
    专题 7: 无监督学习与半监督学习 [part-I][part-II][练习与作业][参考资料(第1-2章)]
    专题 8: 压缩感知与稀疏表示 [part-I][part-II][练习与作业]
    专题 9: 深度学习 [slides][练习与作业]
    专题 10: 处理大规模数据的策略 [slides]

    * 本课程讲义: 《机器学习与数据科学讲义》(2008-2013-Now)正在准备中…..

指导学生

    1. 协助指导的研究生

  • 2008 – 2014: [硕博] 齐宪标 (with 郭军 教授)
  • 2009 – 2010: [硕] 顾芳 (with 郭军 教授)
  • 2014 – present: [博] 秦臻,[博] 四建楼,[博] 张军建 (with 张洪刚, 郭军 教授)
  • 2015.11 – present: [博] 郭若沛
    2. 指导的本科生

  • 2008: 齐宪标, 李晖, 刘乐凯, 唐寿成
  • 2009: 陈亮, 卢厚祥
  • 2010: 成林, 杨志诚, 崔子腾, 李昂然, 葛晗, 马淑靖, 徐饶, 甘强科
  • 2011*: –
  • 2012*: –
  • 2013*: –
  • 2014: 王筱斐, 王梓, 李润泽, 李成, 冯嵩
  • 2015: 张立夫, 赵剑峰, 谭鑫睿
  • 2016: 周磊, 李青阳, 郝梓君, 鲁为
  • 2017: 罗毅超,武瑞,杨涛
  • *: MSRA/JHU进修.

其它学术服务与会员:

  • [1] As a reviewer for SPARS, Natl. Science Review, IEEE TPAMI / TSP / TIP / TNNLS / TBME / TCYB / TCSVT, PR, Neurocom, PRL, NCAA, IJPRAI, AAAI, 《电子学报》, 《自动化学报》, 《计算机辅助设计与图形学学报》, 《控制与决策》, 《北京理工大学学报》, CCCV, CCPR, IC-NIDC
  • [2] 中国计算机学会计算机视觉(CCFCV)专业委员会委员(2016.09-present)
  • [3] IEEE / ACM / CCF会员

其它社会活动:

  • [1] 巴尔的摩华人专家学者联合会执委/副主席 (2013.01-2013.12)
  • [2] 九三学社社员 (2009.12-present)

获奖/业余活动/爱好:

  • [1] 中长跑 校运会成绩 1996.09@凌源一中(5000m冠军)/1997.09@凌源一中(1000m亚军)/1999.06@长邮(5000m季军)/1999.09@长邮(5000m冠军)/2000.06@长邮(5000m冠军;1500m亚军)/2004.04@北邮(1500m第4)/2008.04@北邮(1500m教师组冠军)/2009.04@北邮(1500m教师组冠军)/…/2015.04@北邮(1500m教师组第4)/2016.04@北邮(1500m教师组第6)…(预计2018.04校运会之后退役:)
  • [2] 1998-2002: 吉林大学通信工程学院7次获一等奖学金
  • [3] 乒乓球/摄影

其它资源链接:

  1.  Writing & Revision [link][link]
  2.  Ten Simple Rules for Mathematical Writing – [link]
  3.  Mathematics – Stack Exchange – [link]
  4.  Academic Lecture Videos – [link]
  5.  List of some journal impact factors – [link]
  6.  Comments on SCI-Journals – [link]
  7.  List of Computer Science Conference – [link]
  8.  Microsoft Academic Search – [link]
  9.  The Psychology of Luck – [link]
  10.  Dodo’s Pattern Recognition Commune – [link]
  11.  Preprint Papers – [link]
  12.  Compressed Sensing Resource – [link] [link]
  13.  Resource on Sensing and Analysis of High-dimensional Data – [link] [link]
  14.  Machine Learning (Theory) – [link]
  15.  Research Links: Machine Learning/Compressed Sensing /Action /Activity /Feature /Optimization/Subspace – [link]
  16.  Manifold Learning Resource – [ISOMAP][LLE][LaplacianEigenmap][ManifoldCharting][HessianLLE][LTSA][SDE][Logmap][DiffusionMaps][spectralmethod][comparison][survey]
  17. Modeling with High Dimensional Data: [subspace clustering] [Scalable Sparse Subspace Clustering] [Sparse and Low-Rank Model for Visual Analytics] [link]
  18.  Baltimore Chinese A+ Scholar Association (BCASA) – [link]
  19.  如何做研究 – [link]