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Rong Jin's Photo Nov. 2003    Rong Jin
Associate Professor
Department of Computer Science and Engineering, Michigan State University
Office Address: EB 1124    Email: rongjin_at_cse.msu.edu    Office Phone: +1 (517) 353-7284

Ph.D., Carnegie Mellon University (2003)


Research        
Statistical learning and its application to large-scale information management, including web document retrieval, content-based image retrieval, gene regulatory network reconstruction, neuron data analysis, and visual object recognition. Here is my latest Curriculum vitae.
Research Projects       
Research Group        
Current PhD students: Tianbao Yang, Jinfeng Yi, Mehrdad Mahdavi, and Fengjie Li

Former students

  • Wei Tong (PhD, 2010), Dimensionality Reduction for Non-Vector Data Representation, Postdoc at Michigan State University
  • Hamed Valizadegan (PhD, 2010), Boosting and Online Learning for Classification and Ranking, Postdoc at University of Pittsburgh
  • Yang Zhou (PhD, 2010), Learning with Structures, Yahoo! Labs
  • Yi Liu (PhD,2008), Semi-Supervised Learning wit Side Information: Graph-based Approaches, Google Research
  • Feng Kang (PhD,2007), Automatic Image Annotation, Yahoo!
  • Wu Ming (PhD, 2007), Label Propagation for Classification and Ranking, Microsoft
Selected Services        
Area Chair: SIGIR 2009, ACML 2009
Program committee member: WWW (2010, 2008), IJCAI 2011, NIPS (2010, 2009), SDM 2009, ICML (2011, 2010, 2008, 2007), SIGIR (2008, 2007, 2006), CIKM (2008, 2007, 2005, 2004), KDD (2011, 2010, 2008, 2006), AAAI (2008, 2005), PAKDD (2007, 2006, 2005, 2004)
Recent Publications (More)
--- Journal Papers ---
  • Learning Bregman Distance Functions for Semi-Supervised Clustering
    L. Wu, S. C. H. Hoi, R. Jin, Jianke Zu, and Nenghai Yu, IEEE Transactions on Knowledge and Data Engineering (TKDE), (in press)
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  • A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval
    L. Yang, R. Jin, L. Mummert, R. Sukthankar, A. Goode, B. Zheng,, S. Hoi, and M. Satya-narayanan, IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI) 32(1):30-44, 2010
  • Semi-supervised Feature Selection based on Manifold Regularization
    Z. Xu, I. King, M. Lyu, and R. Jin, IEEE Transaction on Neural Networks,1033-1047, 2010
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  • Efficient Algorithm for Localized Support Vector Machine
    H. Cheng, P. N. Tan and R. Jin, IEEE Transaction on Knowledge and Data Engineering (TKDE), 22(4): 537-549, 2010
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  • Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
    S. C. H. Hoi, R. Jin and M. R. Lyu, IEEE Transaction on Knowledge and Data Engineering (TKDE), 21(9): 1233-1248, 2009
  • Reconstruct Modular Phenotype-specific Gene Networks by Knowledge-Driven Matrix Factorization
    X. Yang, Y. Zhou, R. Jin and C. Chan, Bioinformatics 25(17): 2236-2243, 2009
  • Semi-Supervised SVM Batch Mode Active Learning with Applications to Image Retrieval
    S. Hoi, R. Jin, J. Zhu, and M. R. Lyu, ACM Transaction on Information System (TOIS) 27(3),July, 2009
  • SemiBoost: Boosting for Semi-supervised Learning
    P. K. Mallapragada, R. Jin, A. K. Jain, and Y. Liu, IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI), 31(11):2000-2014, 2009
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  • Identifying Functional Connectivity in Large Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach
    S. Eldawlatly, R. Jin, and K. Oweiss, Neural Computation 21(2): 450-477 (2009)
    • --- Conference Papers ---
  • Multi-label Learning with Incomplete Class Assignments
    S. S. Bucak, R. Jin, and A. K. Jain,  Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), 2011
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  • Exclusive Lasso for Multi-task Feature Selection
    Y. Zhou, R. Jin, and S. C. H. Hoi,  Proceeding of the 14th International Conference on Artificial Intelligence and Statistics (AISTAT), 2010
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  • Online Multiple Kernel Learning: Algorithms and Mistake Bounds
    R. Jin, S. C. H. Hoi, and T. Yang,  Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT2010, 2010
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  • Learning from Noisy Side Information by Generalized Maximum Entropy Model
    T. Yang, R. Jin, and A. K. Jain, Proceedings of the 27th International Conference on Machine Learning (ICML), 2010
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  • Simple and Efficient Multiple Kernel Learning By Group Lasso
    Z. Xu, R. Jin, H. Yang, I. King, and M. Lyu, Proceedings of the 27th International Conference on Machine Learning (ICML), 2010
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  • Online Visual Vocabulary Pruning Using Pairwise Constraints
    P. K. Mallapragada, R. Jin, A. K. Jain,, Proceedings of the 23nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
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  • Unsupervised Transfer Learning: Application to Text Categorization
    T. Yang, R. Jin, and A. K. Jain, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010
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  • Exploitation and Exploration in a Performance based Contextual Advertising System
    W. Li, X. Wang, R. Zhang, Y. Cui, R. Jin, and J.C. Mao, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010
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  • Smooth Optimization for Effective Multiple Kernel Learning
    Z. Xu,  R. Jin, S. Zhu, M. Lyu, and I. King, Proceedings of the 24th Conference on Artificial Intelligence (AAAI), 2010
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  • Robust Metric Learning with Smooth Optimization
    K. Huang, R. Jin, Z. Xu, and C. Liu, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), 2010
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  • Directed Network Community Detection: A Popularity and Productivity Link Model
    T. Yang, Y. Chi, S. Zhu, Y. Gong, and R. Jin, Proceedings of the SIAM International Conference on Data Mining (SDM), 2010
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  • Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition
    S. Bucak, R. Jin, and A. K. Jain, Advance in Neural Information Processing Systems (NIPS 24), 2010
  • Active Learning by Querying Informative and Representative Examples
    S. Huang, R. Jin, and Z. H. Zhou, Advance in Neural Information Processing Systems (NIPS 24), 2010
  • Regularized Distance Metric Learning:Theory and Algorithm
    R. Jin and S. Wang, Advance in Neural Information Processing Systems (NIPS 23), 2009
  • DUOL: A Double Updating Approach for Online Learning
    P. Zhao, S. C. H. Hoi and R. Jin, Advance in Neural Information Processing Systems (NIPS 23),2009
  • Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
    L. Wu, R. Jin, S. C. H. Hoi, J. Zhu and N. Yu, Advance in Neural Information Processing Systems (NIPS 23),2009
  • Learning to Rank by Optimizing NDCG Measure
    H. Valizadegan,R. Jin R. Zhang and J. Mao, Advance in Neural Information Processing Systems (NIPS 23), 2009 (Note that the comments made  about LambdaRank in the paper was incorrect given the recent work on evaluating LambdaRank (Microsoft Technical Report MSR-TR-2008-179). The seemingly poor performance of LambdaRank reported in our study is mainly due to the use of neural nets, not due to the deficiency of LambdaRank. More information of LambdaRank can be found in the website of Chris Burges)
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  • Regularized Distance Metric Learning:Theory and Algorithm
    R. Jin and S. Wang, Advance in Neural Information Processing Systems (NIPS 23), 2009
  • Adaptive Regularization for Transductive Support Vector Machine
    Z. Xu, R. Jin J. Zhu, I. King, M. R. Lyu, and Z. Yang, Advanced in Neural Information Processing Systems (NIPS 23),2009
  • Distance Metric Learning from Uncertain Side Information with Application to Automated Photo Tagging
    L. Wu, S. C.H. Hoi, R. Jin, J. Zhu and N. Yu, Proceedings of the 2009 ACM International Conference on Multimedia (MM2009), 2009
  • Efficient Multi-label Ranking for Multi-class Learning: Application to Object Recognition
    S. S. Bucak, P. K. Mallapragada, R. Jin and A. K. Jain, Proceedings of the 12th IEEE International Conference on Computer Vision (ICCV 2009), 2009
  • Combining Link and Content for Community Detection: A Discriminative Approach
    T. Yang, R. Jin, Y. Chi and S. Zhu, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 927-936, 2009
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  • Learning a Distance Metric from Multi-instance Multi-label Data
    R. Jin, S. Wang, and Z.-H. Zhou, Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009),2009
  • A Bayesian Framework for Community Detection Integrating Content and Link
    T. Yang, R. Jin, Y. Chi and S. Zhu, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), 2009
  • Online Learning by Ellipsoid Method
    L. Yang, R. Jin and J. Ye, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), 2009
  • Non-monotonic Feature Selection
    Z. Xu, R. Jin, J. Ye, M. R. Lyu and I. King, Proceedings of the 26th International Conference on Machine Learning (ICML 2009),2009
  • A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks
    T. Yang, Y. Chi, S. Zhu, Y. Gong and R. Jin, Proceedings of 2009 SIAM International Conference on Data Mining (SDM 2009), pp. 990-1001, 2009
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  • An Information Geometry Approach for Distance Metric Learning
    S. Wang and R. Jin, Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009), pp. 591-598, 2009
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    Teaching (Complete list)
    Spring 2011: CSE847 Machine Learning

    Fall 2010: CSE484 Information Retrieval


    Contact Info
  • Email:     rongjin_at_cse.msu.edu
  • URL:       http://www.cse.msu.edu/~rongjin
  • Office Phone:       +1 (517) 353-7284
  • Fax:       +1 (517) 432-1061
  • Office Address:       EB 1124      (MSU Campus Map)
  • Mailing Address:
             Rong Jin
             Department of Computer Science and Engineering
             3115 Engineering Building
             Michigan State University
             East Lansing, MI 48824
             U.S.A

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    Copyright By Rong Jin CSE,MSU