Sheng Li

I am a Data Scientist at Adobe Research, San Jose, CA.

Research Interests: I have a broad interest in machine learning and data mining models, with applications to big data analytics, online advertising, deep learning, visual intelligence, and user behavior modeling.

Education: Ph.D. in CE, Northeastern, 2017; M.S. in CS, NUPT, 2012; B.S. in CS, NUPT, 2010.

Best Paper Awards/Nominations: SIAM SDM 2014, IEEE ICME 2014, IEEE FG 2013

News

  • 12/27/2017, I accepted the invitation to serve as Program Committee member of KDD 2018.
  • 12/25/2017, I accepted the invitation to serve as an Associate Editor of Journal of Electronic Imaging (JEI).
  • 12/15/2017, I accepted the invitation to serve as an Editorial Board member of Neurocomputing.
  • 11/25/2017, I accepted the invitation to serve as Program Committee member of IJCAI 2018.
  • 11/23/2017, One paper on outlier detection is accepted by ACM Trans. Knowledge Discovery from Data (TKDD).
  • 11/22/2017, One paper on action recognition is accepted by IEEE Trans. Image Processing (T-IP).
  • 11/08/2017, Two papers (on multi-view outlier detection and person re-identification) and One poster (on student response prediction) are accepted at AAAI 2018.
  • 10/12/2017, Two papers on multi-label classification and multi-view graph learning are accepted at IEEE BigData 2017.
  • 10/04/2017, I accepted the invitation to serve as an Associate Editor of IET Image Processing.
  • 09/28/2017, One paper on person re-identification is accepted by IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI).
  • 09/20/2017, I accepted the invitation to serve as an Editorial Board member of Neural Computing and Applications.
  • 09/04/2017, One paper on causal inference is accepted at NIPS 2017.
  • 08/09/2017, One paper on 3D action prediction is accepted by ACM Trans. Multimedia Computing Communications and Applications (TOMM).
  • 08/07/2017, One paper on ensemble clustering is accepted by Data Mining and Knowledge Discovery (DMKD).
  • 06/27/2017, One paper on metric learning is accepted by IEEE Trans. Circuits and Systems for Video Technology (T-CSVT).
  • 06/05/2017, I joined the BigData Experience Lab at Adobe Research as a Data Scientist.
  • 05/01/2017, I will co-chair the VCIP'17 special session: "Regularization Techniques for High-Dimensional Visual Data Processing and Analysis", jointly with Prof. Zhangyang Wang and Dr. Xi Peng.
  • 04/24/2017, One paper on multi-view clustering is accepted at IJCAI 2017.
  • 02/18/2017, One paper on videis accepted by IEEE Trans. Image Processing (T-IP).
  • 01/01/2017, One book proposal is accepted by Springer.
  • 12/22/2016, I received the Baidu Research Fellowship.
  • 11/03/2016, One paper is accepted by IEEE Trans. Neural Networks and Learning Systems (T-NNLS).

Research Interests

  • Robust Data Mining and Machine Learning
  • Visual Intelligence and Cybersecurity
  • Time Series Classification and Prediction
  • Deep Learning
  • Behavior Modeling and Causal Inference
  • Online Advertising

Tutorials

  1. Sheng Li and Yun Fu: “Low-Rank and Sparse Modeling for Data Analytics”, International Joint Conference on Artificial Intelligence (IJCAI), 2016.
  2. Rene Vidal, Ehsan Elhamifar, Zhouchen Lin, Jiashi Feng, Sheng Li, Yun Fu: “Low-Rank and Sparse Modeling for Visual Analytics”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

Selected Publications [Google Scholar Page] [Full List]

  • Book
  1. Sheng Li, Yun Fu: Robust Representations for Data Analytics. Springer, 2017.


  • Journal Papers (1 IEEE TPAMI, 2 IEEE TNNLS, 1 IEEE TKDE, 2 IEEE TIP, 3 IEEE TCSVT, 1 ACM TKDD, 1 ACM TOMM, 1 DMKD, etc.)
  1. Sheng Li, Ming Shao, Yun Fu: Person Re-identification by Cross-View Multi-Level Dictionary Learning. IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI), 2017. [PDF] [Code]
  2. Sheng Li, Kang Li, Yun Fu: Early Recognition of 3D Human Actions. ACM Trans. Multimedia Computing Communications and Applications (TOMM), 2017.
  3. Sheng Li, Kang Li, Yun Fu: Self-Taught Low-Rank Coding for Visual Learning. IEEE Trans. Neural Networks and Learning Systems (T-NNLS), 2017. [PDF]
  4. Sheng Li, Ming Shao, Yun Fu: Multi-View Low-Rank Analysis with Applications to Outlier Detection. ACM Trans. Knowledge Discovery from Data (TKDD), 2017.
  5. Kang Li, Sheng Li, Sangmin Oh, Yun Fu. Videography based Unconstrained Video Analysis, IEEE Trans. Image Processing (T-IP), 2017. [PDF]
  6. Chengcheng Jia, Ming Shao, Sheng Li, Handong Zhao, Yun Fu. Stacked Denoising Tensor Auto-Encoder for Action Recognition with Spatiotemporal Corruptions, IEEE Trans. Image Processing (T-IP), 2017. [PDF]
  7. Hongfu Liu, Ming Shao, Sheng Li, Yun Fu: Infinite Ensemble Clustering. Data Mining and Knowledge Discovery (DMKD), 2017. [PDF]
  8. Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: SLMOML: Online Metric Learning with Global Convergence, IEEE Trans. Circuits and Systems for Video Technology (T-CSVT), 2017. [PDF]
  9. 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. [PDF] [Code]
  10. Sheng Li, Yun Fu: Learning Balanced and Unbalanced Graphs via Low-Rank Coding. IEEE Trans. Knowledge and Data Engineering (T-KDE), 27(5): 1274-1287, 2015. [PDF] [Code]
  11. 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.) [PDF] [Code]
  12. Ya Su, Sheng Li, Shengjin Wang, and Yun Fu. Submanifold Decomposition, IEEE Trans. Circuits and Systems for Video Technology (T-CSVT), 24(11): 1885-1897, 2014. [PDF]
  13. 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. [PDF]
  14. 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. [PDF]
  15. Xiao-Yuan Jing, Sheng Li, Wen-Qian Li, etc. Palmprint and Face Multi-Modal Biometric Recognition based on SDA-GSVD and Its Kernelization. Sensors, 12(5), pp. 5551-5571, 2012. [PDF]
  16. Xiao-Yuan Jing, Sheng Li, Chao Lan, David Zhang, Jingyu Yang, Qian Liu: Color Image Canonical Correlation Analysis for Face Feature Extraction and Recognition. Signal Processing (SP), 91(8): 2132-2140, 2011. [PDF]


  • Conference Papers (1 NIPS, 5 IJCAI, 3 AAAI, 3 CIKM, 2 SDM, 2 IEEE BigData, 1 SIGKDD, 1 SIGIR, 1 ICCV, 1 ICDM, etc.)
  1. Kai Li, Sheng Li, Zhengming Ding, Weidong Zhang, and Yun Fu. Latent Discriminant Subspace Representations for Multi-view Outlier Detection. AAAI, 2018.
  2. Kai Li, Zhengming Ding, Sheng Li, and Yun Fu. Discriminative Semi-coupled Projective Dictionary Learning for Low-Resolution Person Re-Identification. AAAI, 2018.
  3. Shumin Jing, Sheng Li. Contextual Collaborative Filtering for Student Response Prediction in Mixed-Format Tests. AAAI, 2018. (Poster)
  4. Sheng Li, Yun Fu. Matching on Balanced Nonlinear Representations for Treatment Effects Estimation. NIPS, 2017. [PDF]
  5. Sheng Li, Yun Fu. Robust Multi-Label Semi-Supervised Classification. IEEE BigData, 2017. [PDF]
  6. Sheng Li, Hongfu Liu, Zhiqiang Tao, and Yun Fu. Multi-View Graph Learning with Adaptive Label Propagation. IEEE BigData, 2017.
  7. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. From Ensemble Clustering to Multi-View Clustering. IJCAI, 2017. [PDF]
  8. Sheng Li, Nikos Vlassis, Jaya Kawale and Yun Fu. Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns. IJCAI, 2016. [PDF]
  9. Sheng Li. Learning Robust Representations for Data Analytics. IJCAI, 2016. (Poster) [PDF]
  10. Sheng Li, Yaliang Li and Yun Fu. Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach. CIKM, 2016. [PDF]
  11. Zhiqiang Tao, Hongfu Liu, Sheng Li and Yun Fu. Robust Spectral Ensemble Clustering. CIKM, 2016. [PDF]
  12. Hongfu Liu, Ming Shao, Sheng Li and Yun Fu. Infinite Ensemble for Image Clustering. KDD, 2016. [PDF]
  13. Sheng Li, Yun Fu. Unsupervised Transfer Learning via Low-Rank Coding for Image Clustering, IJCNN, 2016. [PDF]
  14. Guoqiang Zhong, Yan Zheng, Sheng Li and Yun Fu. Scalable Large Margin Online Metric Learning, IJCNN, 2016. [PDF]
  15. Sheng Li, Kang Li and Yun Fu. Temporal Subspace Clustering for Human Motion Segmentation. ICCV, 2015. [PDF] [Code]
  16. Sheng Li, Ming Shao and Yun Fu. Cross-View Projective Dictionary Learning for Person Re-identification. IJCAI, 2015. [PDF] [Code]
  17. Ming Shao, Sheng Li, Zhengming Ding and Yun Fu. Deep Linear Coding for Fast Graph Clustering. IJCAI, 2015. [PDF]
  18. Sheng Li, Jaya Kawale and Yun Fu. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. CIKM, 2015. [PDF]
  19. Sheng Li, Jaya Kawale and Yun Fu. Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization, SIGIR, 2015. [PDF]
  20. Sheng Li, Ming Shao and Yun Fu. Multi-view Low-Rank Analysis for Outlier Detection. SDM, 2015. [PDF] [Code]
  21. Sheng Li, Yun Fu, Robust Subspace Discovery through Supervised Low-Rank Constraints, SDM, 2014. (Best Paper Award, 1 out of 389 submissions) [PDF] [Code]
  22. Kang Li, Sheng Li, and Yun Fu, Early Classification of Ongoing Observation, ICDM, 2014. [PDF]
  23. 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) [PDF]
  24. Sheng Li, Ming Shao, and Yun Fu, Locality Linear Fitting One-class SVM with Low-Rank Constraints for Outlier Detection, IJCNN, 2014. [PDF]
  25. Sheng Li, Yun Fu. Low-Rank Coding with b-Matching Constraint for Semi-supervised Classification, IJCAI, 2013. [PDF] [Code]
  26. 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) [PDF] [Code]

Awards and Honors

Services

  • Associate Editor: Neurocomputing (2017 - ), IET Image Processing (2017 - ), Neural Computing and Applications (2017 - ), Journal of Electronic Imaging (2018 - )
  • Publicity Chair: ICMLA (2016), AMFG (2015-2017)
  • Special Session Co-Chair: VCIP (2017)
  • PC Member: AAAI (2017, 2018), IJCAI (2015-2018), KDD (2018), MIPR (2018), PAKDD (2017-2018), AFFCON (2018), DSAA (2017), FG (2017), ACII (2017), NLPCC (2017)
  • Reviewer: ACM CSUR, IEEE TPAMI / TKDE / TIP / TNNLS / TMM / TBD / TC / TCSVT / TETCI, ACM TKDD, Pattern Recognition, Neurocomputing, PLoS ONE, IJPRAI, JVCI, JEI, OE, etc.

Contact

SHENG LI

345 Park Avenue, San Jose, CA 95110

Email: shengli [AT] ece.neu.edu