Sheng Li

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I am a 5th-year Ph.D. candidate at the Dept. of Electrical & Computer EngineeringNortheastern University. I work with Prof. Yun (Raymond) Fu in the SMILE Lab since 2012.

I have a broad interest in machine learning and data mining models, with applications to big data analytics, visual intelligence, user behavior modeling, and causal inference. In Summer 2014 and 2015, I was a Data Scientist Intern at Adobe Research, working with Dr. Jaya Kawale and Dr. Nikos Vlassis on user behavior prediction and causal inference models for digital marketing. From 2007 to 2012, I was a research assistant at the Pattern Recognition Lab at Nanjing University of Posts and Telecommunications, working with Prof. Xiaoyuan Jing on pattern recognition and biometrics. I received my M.S. degree in Information Security, and B.S. degree in Computer Science from Nanjing University of Posts and Telecommunications (NUPT), China in 2012 and 2010, respectively.

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Research Interests
  • Robust Data Mining and Machine Learning 
  • Visual Intelligence and Cybersecurity
  • Time Series Modeling
  • Deep Learning
  • Causal Inference

Awards and Honors

Selected Publications [Full List] [Google Scholar Page]
  • Book
  1. Sheng Li, Yun Fu: Robust Representations for Data Analytics.  Springer, 2017.
  • Journal Papers
  1. Sheng Li, Kang Li, Yun Fu: Self-Taught Low-Rank Coding for Visual Learning. IEEE Trans. Neural Networks and Learning Systems (T-NNLS), 2017.
  2. Kang Li, Sheng LiSangmin Oh, Yun Fu. Videography based Unconstrained Video Analysis, IEEE Trans. Image Processing (T-IP), 2017.
  3. Sheng Li, Yun Fu: Learning Robust and Discriminative Subspace with Low-Rank Constraints. IEEE Trans. Neural Networks and Learning Systems (T-NNLS), 27(11): 2160-2173, 2016.
  4. Sheng LiYun FuLearning Balanced and Unbalanced Graphs via Low-Rank Coding. IEEE Trans. Knowledge and Data Engineering (T-KDE), 27(5): 1274-1287, 2015.
  5. Sheng Li*Liangyue Li*, Yun Fu: Learning Low-Rank and Discriminative Dictionary for Image Classification. Image and Vision Computing (IVC), 32(10): 814-823, 2014. (* indicates equal contribution.)
  6. Ya Su, Sheng Li, Shengjin Wang, Yun Fu, Submanifold Decomposition, IEEE Trans. Circuits and Systems for Video Technology (T-CSVT), 24(11): 1885-1897, 2014.
  7. Xiao-Yuan Jing, Sheng Li, David Zhang, Jian Yang, Jing-Yu Yang: Supervised and Unsupervised Parallel Subspace Learning for Large-Scale Image Recognition. IEEE Trans. Circuits System for Video Technology (T-CSVT), 22(10): 1497-1511, 2012.
  8. Xiao-Yuan Jing, Sheng Li, David Zhang, Chao Lan, Jingyu Yang: Optimal Subset-division based Discrimination and Its Kernelization for Face and Palmprint Recognition. Pattern Recognition (PR), 45(10): 3590-3602, 2012.
  • Conference Papers
    1. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. From Ensemble Clustering to Multi-View ClusteringIJCAI, 2017.
    2. Sheng Li, Nikos Vlassis, Jaya Kawale and Yun FuMatching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns. IJCAI, 2016.
    3. Sheng Li, Yaliang Li and Yun Fu. Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach. CIKM, 2016.
    4. Zhiqiang Tao, Hongfu Liu, Sheng Li and Yun Fu. Robust Spectral Ensemble Clustering. CIKM, 2016.
    5. Hongfu Liu, Ming Shao, Sheng Li and Yun Fu. Infinite Ensemble for Image Clustering. KDD, 2016.
    6. Sheng Li, Yun Fu. Unsupervised Transfer Learning via Low-Rank Coding for Image Clustering, IJCNN, 2016.
    7. Sheng Li, Kang Li and Yun Fu. Temporal Subspace Clustering for Human Motion Segmentation. ICCV, 2015.
    8. Sheng Li, Ming Shao and Yun Fu. Cross-View Projective Dictionary Learning for Person Re-identification. IJCAI, 2015.
    9. Ming Shao, Sheng Li, Zhengming Ding and Yun Fu. Deep Linear Coding for Fast Graph Clustering. IJCAI, 2015.
    10. Sheng Li, Jaya Kawale and Yun Fu. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. CIKM, 2015.
    11. Sheng Li, Jaya Kawale and Yun Fu. Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization, SIGIR, 2015.
    12. Sheng Li, Ming Shao and Yun Fu. Multi-view Low-Rank Analysis for Outlier Detection. SDM, 2015.
    13. Sheng Li, Yun Fu, Robust Subspace Discovery through Supervised Low-Rank Constraints, SDM, 2014. (Best Paper Award, 1 out of 389 submissions)
    14. Kang Li, Sheng Li, and Yun Fu, Early Classification of Ongoing Observation, ICDM, 2014.
    15. Ming Shao, Sheng Li, Tongliang Liu, Dacheng Tao, Thomas S. Huang, and Yun Fu, Learning Relative Features Through Adaptive Pooling for Image Classification, ICME, 2014. (Best Paper Award Candidate, 4 out of 716 submissions)
    16. Sheng Li, Ming Shao, and Yun Fu, Locality Linear Fitting One-class SVM with Low-Rank Constraints for Outlier Detection, IJCNN, 2014.
    17. Sheng Li, Yun Fu. Low-Rank Coding with b-Matching Constraint for Semi-supervised Classification, IJCAI, 2013.
    18. Liangyue Li, Sheng Li, and Yun Fu. Discriminative Dictionary Learning with Low-Rank Regularization for Face Recognition. IEEE FG, 2013. (Best Student Paper Honorable Mention Award)

    427 Richards Hall
    Northeastern University
    360 Huntington Avenue
    Boston, MA 02115 

    Email: shengli [AT]