Dacheng Tao

Prof. Dr. Dacheng Tao

Beautiful is the first test: there is no permanent place in the world for ugly mathematics.

– G.H. Hardy

News:

Best paper award: S. Si, D. Tao, and K.P. Chan, “Cross-domain Web Image Annotation,” IEEE ICDM Workshop on Internet Multimedia Mining, pp. 1-6, 2009. (Best paper award)

Dr. Tao has become an associate editor of IEEE Transactions on Knowledge and Data Engineering.

Dr. Dacheng Tao has become as a guest professor in Wuhan University (State key lab of information engineering surveying, mapping and remote sensing).

Dr. Dacheng Tao has become as a visiting professor in Xi’Dian University.

Prof. Dr. Dacheng Tao

School of Computer Science and Information Systems

Birkbeck, University of London

Dacheng Tao received the B.Eng. degree from the University of Science and Technology of China (USTC), the M.Phil degree from the Chinese University of Hong Kong (CUHK), and the Ph.D degree from the University of London (Lon). Currently, he is a NANYANG Assistant Professor with the School of Computer Engineering in the Nanyang Technological University, a Research Associate Fellow in the University of London, a Visiting Professor in the Xidian University, and a Guest Professor in the Wuhan University.

His research is computational neuroscience. In particular, he mainly applies statistics and mathematics for data analysis problems in neuroscience and related areas, e.g., neurocomputing, cognitive vision, large scale data processing, machine intelligence and compressed sensing. He has authored and co-authored more than 100 scientific articles at top venues including IEEE T-PAMI, T-KDE, T-IP, NIPS, ICDM, CVPR, ECCV; ACM T-KDD, KDD and Cognitive Computation, with best paper awards. One of his T-PAMI papers was selected as a “New Hot Paper” in ScienceWatch.com (Thomson Scientific) for the contribution of tensor machines for computational neuroscience. His publications have been cited in GoogleScholar more than 1,000 times, his H-Index in GoogleScholar is 17+ and his Erdös number is 3. Previously he gained several Meritorious Awards from the International Interdisciplinary Contest in Modeling, which is the highest level mathematical modeling contest in the world, organized by COMAP. He holds the K. C. WONG Education Foundation Award of Chinese Academy of Sciences.

He is an associate editor of IEEE Transactions on Knowledge and Data Engineering (T-KDE), Neurocomputing (Elsevier), Neural Processing Letters (Springer), Information Sciences (Elsevier), the Official Journal of the International Association for Statistical Computing -- Computational Statistics & Data Analysis (Elsevier), Signal Processing (Elsevier), Pattern Analysis and Applications (Springer), etc. He has copyedited five books and eight journal special issues on several topics of computational neuroscience. He has (co-)chaired for special sessions, invited sessions, workshops, panels and conferences. He has served with more than 100 major international conferences including ICDM, KDD, CVPR, ICCV, ECCV, and Multimedia, and more than 40 prestigious international journals including T-PAMI, T-KDE, T-OIS, and T-IP. He is a member of IEEE, IEEE Computer Society, IEEE Signal Processing Society, IEEE SMC Society, and IEEE SMC Technical Committee on Cognitive Computing.

Contact Information

Office:	Blk N4, 02b-46 School of Computer Engineering Nanyang Technological University Singapore 639798.
Phone:	+65 6790 6250
Fax:	+65 6792 6559
Email:	dctao at ntu.edu.sg, dacheng.tao at gmail.com

Postal Address

Blk N4, 02b-46
School of Computer Engineering
Nanyang Technological University
Singapore 639798.

Research Interests

My research is computational neuroscience. In particular, I mainly apply statistics and mathematics for data analysis problems in neuroscience and related areas, e.g., neurocomputing, cognitive vision, large scale data processing, machine intelligence and compressed sensing.

Recent Representative Publications

1. M. Song, D. Tao et al., “Colour to Grey: Visual Cue Preservation,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), to appear.

2. Y. Mu, D. Tao et al., “Biologically Inspired Tensor Features,” Cognitive Computation (Springer), to appear.

3. Y. Mu and D. Tao, “Biologically Inspired Feature Manifold for Gait Recognition,” Neurocomputing (Elsevier), to appear.

4. S. Si, D. Tao, and B. Geng, “Bregman Divergence based Regularization for Transfer Subspace Learning,” IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear.

5. S. Si, D. Tao, and K.P. Chan, “Evolutionary Cross-domain Discriminative Hessian Eigenmaps,” IEEE Transactions on Image Processing (TIP), to appear.

6. X. Tian, D. Tao et al., “Active Reranking for Web Image Search,” IEEE Transactions on Image Processing (TIP), to appear.

7. T. Zhou, D. Tao, and X. Wu, “Manifold Elastic Net,” Data Mining and Knowledge Discovery (DMKD: Springer), to appear.

8. T. Xia, D. Tao, T. Mei, and Y. Zhang, “MultiView Spectral Embedding,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (TSMC-B), to appear.

9. D. Song and D. Tao, “Biologically Inspired Feature Manifold for Scene Classification,” IEEE Transactions on Image Processing (TIP), vol. 19, no. 1, pp. 174-184, January, 2010.

10. D. Tao et al., “Geometric Mean for Subspace Selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.31, no.2, pp. 260-274, February 2009. (The conference version has been accepted as a full paper in ICDM 2007 and won the best theory/algorithms paper runner up award.)

11. W. Bian and D. Tao, “Manifold Regularization for SIR with Rate Root-n Convergence,” Advances in Neural Information Processing Systems (NIPS), pp. 1-8, 2009.

12. T. Zhou and D. Tao, “Fast Gradient Clustering,” NIPS 2009 Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (NIPS: DISCML), pp. 1-6, 2009.

13. S. Si, D. Tao, and K.P. Chan, “Cross-domain Web Image Annotation,” IEEE ICDM Workshop on Internet Multimedia Mining, pp. 1-6, 2009. (Best paper award)

14. T. Zhang, D. Tao et al., “Patch Alignment for Dimension Reduction,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 20, no. 2, February 2009.

15. Y. Huang, K. Huang, T. Tan, and D. Tao, “A Novel Visual Organization Based on Topological Perception,” Asian Conference on Computer Vision (ACCV), pp. 1-11, 2009.

16. B. Geng, D. Tao et al., “Ensemble Manifold Regularization,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-7, 2009.

17. Y. Huang, K. Huang, L. Wang, D. Tao et al., “Enhanced Biologically Inspired Model,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.

18. D. Tao et al., “Bayesian Tensor Approach for 3-D Face Modelling,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 18, no. 10, pp. 1397-1410, October 2008.

19. J. Sun, D. Tao et al., “Incremental Tensor Analysis: theory and applications,” ACM Transactions on Knowledge Discovery in Data (ACM TKDD), vol. 2, no. 3, October 2008. (The conference version has been accepted as a full paper in ACM SIGKDD 2006.)

20. J. Shen, D. Tao, and X. Li, “Modality Mixture Projections for Semantic Video Event Detection,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 18, no. 11, pp. 1587-1596, November 2008.

21. D. Tao et al., “General Tensor Discriminant Analysis and Gabor Features for Gait Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 10, pp. 1700-1715, October 2007.

22. D. Tao et al., “Negative Samples Analysis in Relevance Feedback,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 19, no. 4, pp. 568-580, April 2007.

23. D. Tao et al., “Supervised Tensor Learning,” Knowledge and Information Systems (Springer: KAIS), vol. 13, no. 1, pp. 1-42, January 2007.

24. D. Tao et al., “Asymmetric Bagging and Random Subspace for Support Vector Machines-based Relevance Feedback in Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 28, no.7, pp. 1088-1099, July 2006.

25. D. Tao et al., “Kernel Direct Biased Discriminant Analysis: A New Content-based Image Retrieval Relevance Feedback Algorithm,” IEEE Transactions on Multimedia (TMM), vol. 8, no. 4, pp. 716-727, August 2006. (Best Transactions paper award finalist)

Call for Papers/Chapters

TBA