Ming Dong 
 

Associate Professor ,                                Scientific Member                      

Department of Computer Science,        Developmental Therapeutics Program

College of Liberal Arts and Science,     Karmanos Cancer Institute

Wayne State University

Office: 436 State Hall, Detroit, MI, 48202

Phone:(313)577-0725

Email:

 
Personal Information

Ming Dong received his B. S. degrees in electrical engineering and industrial management engineering from Shanghai Jiao Tong University, Shanghai, China, in 1995. He received his Ph. D degree in electrical engineering from University of Cincinnati in 2001. From 1995 to 1997, He was a lecturer at University of Petroleum, Shan Dong, P. R. China. He was an Assistant Professor in the Department of Computer Science at Wayne State University from 2002 to 2008. He was prompted to Associate Professor of Computer Science with Tenure in 2008 and is the director of Machine Vision and Pattern Recognition Laboratory (MVPRL), part of the Center for Visual Informatics and Intelligence at Wayne State University.

Dr. Dong's areas of research include pattern recognition, data mining, and multimedia content analysis. His research is funded by National Science Foundation, State of Michigan, and Industries. He has published technical articles in journals such as IEEE TPAMI, IEEE TNN, IEEE TC, IEEE TFS, IEEE TVCG, Data Mining and Knowledge Discovery, and in leading conferences such as IEEE CVPR, IEEE ICDM, ACM MM and WWW. He is currently an associate editor of IEEE Transactions on Neural Networks and Journal of Pattern Analysis and Applications (Springer).

Current External Funding

Completed Grants

Selective Recent Publications (For Full Publication List, Click Here)

  1. Y. Li, M. Dong and J. Hua, “Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 5, pp 953 - 960, 2009.

  2. J. Hua, Z. Lai, M. Dong, X. Gu, and H. Qin. "Geodesic Distance-Weighted Shape Diffusion," IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1643 – 1650, 2008.

  3. Y. Chen, M. Rege, M. Dong and J. Hua, “Non-negative Matrix Factorization for Semi-supervised Data Clustering”, Journal of Knowledge and Information Systems (Springer), Vol. 17, No. 3, pp. 355 - 379, 2008. (invited as a best paper of ICDM 07, conference version: “Incorporating User provided Constraints into Document Clustering”, was published in Proc. of IEEE International Conference on Data Mining, Omaha, NE, 2007 as a Regular paper, acceptance rate 7.2%).

  4. M. Rege, M. Dong, and F. Fotouhi, “Bipartite Isoperimetric Graph Partitioning for Data Co-clustering”, Data Mining and Knowledge Discovery (Springer), Vol. 16, No. 3, pp. 276-312, 2008. (Conference version: “Co-clustering documents and words using Bipartite Isoperimetric Graph Partitioning”, was published in Proc. of IEEE International Conference on Data Mining, Hong Kong, China, 2006 as a Regular paper, acceptance rate 9.5%).

  5. M. Rege, M. Dong and J. Hua, “Graph Theoretical Framework for Simultaneously Integrating Visual and Textural Features for Efficient Web Image Clustering”, Proc. of 17th International World Wide Web Conference, April 2008, China, Regular paper, acceptance rate 11%.

  6. Y. Li, M. Dong and J. Hua, "Localized Feature Selection for Clustering", Pattern Recognition Letters,  Vol. 29, pp. 10 – 18, 2008.

  7. Y. Tan, J. Hua, and M. Dong “3D Reconstruction from 2D Images with Hierarchical Continuous Simplicies,” The Visual Computer, Vol. 23, No. 10, pp. 905 – 914, 2007.

  8. J. Jiang, M. Haacke, and M. Dong  "The Dependence of Vessel Area Accuracy and Precision as a Function of MR Imaging Parameters and Boundary Detection Algorithm", Journal of Magnetic Resonance Imaging (JMRI), Vol. 25, 1226- 1234, 2007.

  9. X. Luo, M. Dong and Y. Huang, “On Distributed Fault-Tolerant Detection in Wireless Sensor Networks”, IEEE Transactions on Computers, Vol. 55, No. 1, pp. 58-70, 2006.

  10. Y. Li , M. Dong and R. Kothari, “Classifiability Based Omnivariate Decision Trees”, IEEE Transactions on Neural Networks, Vol. 16, No. 6, pp. 1547 - 1560, 2005.

 

Professional Services

MVPRL   Publications    Research     Teaching     Personal Interests