Ming Dong 


Professor, Co-Director

Department of Computer Science,
Data Science and Business Analytics Program, Big Data &Business Analytics Group

College of Engineering, Wayne State University
Office: 5057 Woodward Ave., Suite 14110.1, Detroit, MI, 48202
Phone:(313)577-0725,Email: mdong at wayne dot edu

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Short Bio

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. He is currently a professor in the Department Computer Science and the co-director of the Data Science and Business Analytics program and the Big Data & Business Analytics Group at Wayne State University. He is also the director of the Machine Vision and Pattern Recognition Lab.

Dr. Dong's areas of research include deep learning, data mining, and computer vision with applications in health informatics and automotive industry. His research is funded by National Science Foundation, National Institutes of Health, State of Michigan, Private Foundations (e.g., Michigan Health Endorsement Fund, Epilepsy Foundation) and Industries (e.g., APB Investment, Ford Motor Company). He has published over 100 technical articles in premium journals and conferences in related fields, e.g., TMM, TPAMI, TKDE, TNN, TVCG, TC, IEEE CVPR, IEEE ICCV, IEEE ICDM, ACM MM, AMIA and WWW. He is/was an associate editor of Statistical Analysis and Data Mining, the American Statistical Association (ASA) Data Science Journal (since 2018), Journal of Smart Health (Since 2016), IEEE Transactions on Neural Networks (2008-2011), and Pattern Analysis and Applications (2007-2010), and served in many conference program committees and US National Science Foundation panels. He also served as senior research consultant in Baidu Inc. in 2008.


Funding

External

Internal


Selected Publications (Full Publications)

  1. Hajar Emami, Ming Dong, Siamak P. Nejad-Davarani, and Carri Glide-Hurst, "Generating Synthetic CTs from Magnetic Resonance Images using Generative Adversarial Networks", Medical Physics Journal, Vol. 45, Issue 8, pp. 3627-3636, August 2018

  2. S. Chen, C. Zhang and M. Dong, Deep Age Estimation: From Classification to Ranking”, IEEE Trans. on Multimedia (TMM), Vol. 20, Issue 8, pp. 2209-2222, August 2018.

  3. S. Chen, C. Zhang and M. Dong, "Coupled End-to-end Transfer Learning with Generalized Fisher Information", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (IEEE CVPR), Salt Lake City, UT, June 2018.

  4. Wang, L, Zhu, D and Dong, M, "Clustering over-dispersed data with mixed feature types", Statistical Analysis and Data Mining, Vol 11, Issue 2, pp. 55-65, April 2018.

  5. F. Zhang, Q. Mao, X. Shen, Y. Zhan and M. Dong, "Spatially Coherent Feature Learning for Pose-invariant Facial Expression Recognition", ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) , Vol. 4, Issue 1s, No. 27, April 2018.

  6. Mehedi Hasan, Alexander Kotov, April Idalski Carcone, Ming Dong, Sylvie Naar, "Predicting the Outcome of Patient-Provider Communication Sequences using Recurrent Neural Networks and Probabilistic Models", AMIA Infomatics Summit (AMIA), San Francisco, CA, March 2018.

  7. Li, X, Zhu, D and Dong, M, "Multinomial classification with class-conditional overlapping sparse feature groups", Pattern Recognition Letters (PRL) , vol. 101, pp 37-43, Jan. 2018.

  8. Wang, L, Zhu, D, Li, Y and Dong, M, "Modeling Over-dispersion for Network Data Clustering", Proc. of 16th IEEE International Conference on Machine Learning and Application, Cancun, Mexico, December 2017. (Best Paper Award Top 3 Finalist)

  9. H. Xu, M. Dong and Z. Zhong, "Directionally Convolutional Networks for 3D Shape Segmentation", Proc. of IEEE International Conference on Computer Vision (IEEE ICCV), Venice, Italy, October 2017. Source code link in Github.

  10. S. Chen, C. Zhang, M. Dong, J. Le and M. Rao 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.

  11. Qirong Mao, Yongbin Yu, Feifei Zhang, and Ming Dong, Hierarchical Bayesian Theme Models for Multi-pose Facial Expression Recognition, IEEE Trans. on Multimedia (TMM), Vol. 19, Issue 2, pp. 861-873, April 2017

  12. 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.

  13. 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, Volume 28, Issue 5, pp 867-879, 2017.

  14. L. Wang, M. Dong and A. Kotov,Multi-level Approximate Spectral Clustering, Proc. of IEEE International Conference on Data Mining (IEEE ICDM), Atlantic City, NJ, 2015.

  15. L. Wang and M. Dong, Exemplar-based Low-rank Matrix Decomposition for Data Clustering, Journal of Data Mining and Knowledge Discovery (DMKD), Volume 29, Issue 2, pp 324-357, March 2015.

  16. Q. Mao, M. Dong, Z. Huang and Y. Zhan, "Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks", IEEE Trans. on Multimedia (IEEE TMM), 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).

  17. L. Wang and M. Dong, Multi-Level Low-rank Approximation-based Spectral Clustering for Image Segmentation, Pattern Recognition Letters (PRL), Vol. 33, pp. 2206 - 2215, 2012.

  18. 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 (IEEE TKDE), Vol. 24, No. 6, pp 1036-1050, 2012.

  19. 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 (IEEE TKDE), vol. 22, no. 10, pp. 1459-1474, October 2010.

  20. Y. Chen, L. Wang, M. Dong and J. Hua, "Exemplar-based Visualization of Large Document Corpus", IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), vol. 15, no. 6, pp. 1193-1200, 2009.

  21. 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 (IEEE TVCG), vol. 15, no.6, pp. 1161-1168, 2009.

  22. 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 (IEEE TPAMI), Vol. 31, No. 5, pp 953 - 960, 2009.

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

  24. 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. (conference version:Incorporating User provided Constraints into Document Clustering, was published in Proc. of IEEE International Conference on Data Mining (IEEE ICDM), Omaha, NE, 2007 as a Regular paper, acceptance rate 7.2%).

  25. 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 (IEEE ICDM ), Hong Kong, China, 2006 as a Regular paper, acceptance rate 9.5%).

  26. 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, (ACM WWW), April 2008, China, Regular paper, acceptance rate 11%.

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

  28. 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 (IEEE CVPR), pp. 2057-2063, New York, NY, 2006.

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

  30. Y. Li , M. Dong and R. Kothari, Classifiability Based Omnivariate Decision Trees;, IEEE Transactions on Neural Networks (IEEE TNN), Vol. 16, No. 6, pp. 1547 - 1560, 2005.

  31. 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.

  32. M. Dong and R. Kothari, "Look-Ahead Based Fuzzy Decision Tree Induction", IEEE Transactions on Fuzzy System (IEEE TFS), Vol. 9, No. 3, pp. 461--468, 2001.


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