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: 5057 Woodward Ave., Suite 14110.1, Detroit, MI, 48202





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. He was on sabbatical leave in 2009 and served as a senior research consultant in portal search department in Baidu, Beijing, China..

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 TMM, IEEE TKDE, 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 has served as an associate editor for IEEE Transactions on Neural Networks and Journal of Pattern Analysis and Applications (Springer).




Selective Recent Publications

  1. S. Chen, C. Zhang, M. Dong, and J. Le “Ranking-CNN for Age Estimation”, Proc. of IEEE Conference on Computer Vision and Pattern Recognition (IEEE CVPR), Honolulu, Hawaii, July 2017. Source code link in Github.
  2. Xiaoce Feng, Yong Xu, Ming Dong and Philip Levy, “Non-contact Home Health Monitoring based on Low-cost High-performance Accelerometers”, Proc. of IEEE/ACM conference on connected health: Applications, Systems and Engineering Technologies, Philadelphia, PA, July 2017.
  3. X. Li, D. Zhu, M. Dong, P. Levy and M. Nezhad, “SDT: A Tree Method for Detecting Patient Subgroups with Personalized Risk Factors”, Proc. of AMIA Joint Summits on Translational Science, San Francisco, CA, 2017.
  4. Qirong Mao, Yongbin Yu, Feifei Zhang, and Ming Dong, “Hierarchical Bayesian Theme Models for Multi-pose Facial Expression Recognition”, IEEE Trans. on MultimediaVol. 19, Issue 2, pp. 861-873, April 2017
  5. Zhang, Q. Mao, M. Dong and Y. Zhan, “Multi-pose Facial Expression Recognition Using Transformed Dirichlet Process”, Proc. of ACM Conference on Multimedia, Amsterdam, Netherland, 2016.
  6. H. Xu, M. Dong, D. Zhu, A. Kotov, A. Carcone and S. Naar-King, “Text Classification with Topic-based Word Embedding and Convolutional Neural Networks”, Proc. of ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Seattle, WA, 2016.
  7. Lu Wang, Dongxiao Zhu, Yan Li and Ming Dong, “Poisson-Markov Mixture Model and Parallel Algorithm for Binning Massive and Heterogeneous DNA Sequencing Reads”, International Symposium on Bioinformatics Research and Applications, Minsk, Belarus, June 2016.
  8. R. Almomani, M. Dong, and D. Zhu, “Object Tracking via Dirichlet Process-based Appearance Models”, Neural Computing and Applications, Special issues on Computational Intelligence for Vision and Robotics, 2016, doi:10.1007/s00521-016-2280-1.
  9. Mehedi Hasan, Alexander Kotov, April Carcone, Ming Dong, Sylvie Naar-King, and Kathryn Brogan Hartlieb, “A study of the effectiveness of machine learning methods for classification of clinical interview fragments into large number of categories”, Journal of Biomedical Informatics, Volume 62, pp. 21-31, August 2016.
  10. L. Wang, M. Dong and A. Kotov, “Multi-level Approximate Spectral Clustering”, Proc. of IEEE International Conference on Data Mining, Atlantic City, NJ, 2015.
  11. A. Kotov, M. Hasan, A. Carcone, M. Dong, S. Naar-King and K. Brogan, “Interpretable Probabilistic Latent Variable Models for Automatic Annotation of Clinical Text”, Proc. of American Medical Informatics Association Annual Symposium, 2015.
  12. L. Wang and M. Dong, “Exemplar-based Low-rank Matrix Decomposition for Data Clustering”, Journal of Data Mining and Knowledge Discovery, Volume 29, Issue 2, pp 324-357, March 2015.
  13. Q. Mao, M. Dong, Z. Huang and Y. Zhan, "Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks", IEEE Trans. on Multimedia, Vol. 16, Issue 8, pp. 2203 – 2213, December 2014 (Conference version: “Speech Emotion Recognition Using CNN”, was published in Proc. of ACM International Conference on Multimedia, Orlando, Florida, 2014 as a short paper).
  14. L. Wang and M. Dong, “Multi-Level Low-rank Approximation-based Spectral Clustering for Image Segmentation”, Pattern Recognition Letters, Vol. 33, pp. 2206 - 2215, 2012.
  15. L. Wang, M. Rege, M. Dong and Y. Ding “Low-rank Kernel Matrix Factorization for Large Scale Evolutionary Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 6, pp 1036-1050, 2012.
  16. Manjeet Rege and M Dong, “A Graph Theoretic approach to Heterogeneous Data Clustering: New Research Directions and Some Results,” VDM Verlag, 2010, ISBN: 978-3-639-11658-8.
  17. Y. Chen, L. Wang and M. Dong, “Non-negative Matrix Factorization for Semi-supervised Heterogeneous Data Co-clustering”, IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1459-1474, October 2010.
  18. Mostafa Ghannad-Rezaie, Hamid Soltanian-Zadeh, Hao Ying and Ming Dong, "Selection-Fusion Approach for Classification of Datasets with Missing Values", Pattern Recognition, vol. 43, no. 6, 2340-2350, June 2010.
  19. Y. Chen, L. Wang, M. Dong and J. Hua, "Exemplar-based Visualization of Large Document Corpus", IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1193-1200, 2009.
  20. Guangyu Zou, Jing Hua, Zhaoqiang Lai, Xianfeng Gu, and Ming Dong, "Intrinsic Geometric Scale Space by Shape Diffusion," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no.6, pp. 1161-1168, 2009.
  21. 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.
  22. 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.
  23. 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%).
  24. 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%).
  25. 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%.
  26. Y. Li, M. Dong and J. Hua, "Localized Feature Selection for Clustering", Pattern Recognition Letters,  Vol. 29, pp. 10 – 18, 2008.
  27. 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.
  28. 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.
  29. C. Yang, M. Dong, and J. Hua, “Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning", Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2057 – 2063, New York, NY, 2006.
  30. 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.
  31. C. Yang, M. Dong, and F. Fotouhi, “Region Based Image Annotation Through Multiple-Instance Learning”, Proc. of ACM International Conference on Multimedia, pp 435 – 438, Singapore, Nov 6 - 11, 2005.
  32. 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.
  33. Xushen Zhou and Ming Dong, "Can Fuzzy Logic Make Technical Analysis 20/20?" Financial Analysts Journal, Vol. 60, No. 4, pp. 54-73, July/August 2004.

Professional Services

MVPRL   Publications    Research     Teaching