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Zhengming (Allan) Ding 
Ph.D. Candidate, 
Electrical & Computer Engineering, 
Northeastern University, USA
allanding [at] ece [dot] neu [dot] edu
Who am I  [Full Resume] (updated on 04/10/2017)
I am now a Ph.D. candidate of Department of Electrical & Computer Engineering,  Northeastern University (NEU),  supervised by Prof.  Yun(Raymond) Fu. My  research interests include machine learning and computer vision. I received my M.S. degree and B.S. degree both in Computer Science from University of Electronic Science and Technology of China (UESTC), China, in 2013 and 2010. During Jun. to Aug. 2015, I was doing summer intern in Army Research Laboratory with Nasser Nasrabadi. During May to July, 2016, I worked as Data Scientist Intern as Adobe with William Yan. 

 What’s New [more]

  • 04/2017, I received the 2017 College of Engineering Outstanding Graduate Research Award.
  • 04/2017, I am selected to attend the CVPR-2017 Doctoral Consortium in Hawaii.
  • 04/2017, we have one paper accepted by IEEE TIP. Congratulations to Dr. Kong and Dr. Li.
  • 03/2017, we got one paper accepted by IEEE TNNLS.
  • 03/2017, I received the FG-17 travel grant.
  • 03/2017, I accepted the invitation to serve as Program Committee member of ACII 2017 and ICMLA 2017.
  • 03/2017, I am selected to attend the FG-2017 Doctoral Consortium in Washington, DC.
  • 02/2017, we have one paper accepted by IEEE CVPR 2017.
  • 02/2017, we have one paper accepted by IEEE TCSVT.  Congratulations to Handong.
  • 02/2017, I’m co-organizing a tutorial on “Multi-view Face Representation” [pdf] (with Handong  and Prof. Fu) at FG-2017, in Washington, DC.

Research Interests

  • Manifold learning, subspace learning, low-rank representation and sparse representation
  • Deep learning (deep auto-encoder, LSTM, CNN)
  • Transfer Learning, domain adaptation,
  • Multi-source/multi-view learning, and multi-task learning

Selected Publications [Google Scholar][DBLP][GitHub][Full List]

Journal Papers:
[J4]. Zhengming Ding, and Yun Fu. Robust Multi-view Data Analysis through Collective Low-Rank Subspace. IEEE Transactions on Neural Networks and Learning Systems, 2017 [pdf][bib][code]
[J3]. Zhengming Ding, and Yun Fu. Robust Transfer Metric Learning for Image Classification, IEEE Transactions on Image Processing, vol. 26, no.2, pp. 660-670, 2016 [pdf][bib][code]
[J2]. Zhengming Ding, Ming Shao, and Yun Fu. Incomplete Multisource Transfer Learning, IEEE Transactions on Neural Networks and Learning Systems, 2016 [pdf][bib][code]
[J1]. Zhengming Ding, Ming Shao, and Yun Fu. Missing Modality Transfer Learning via Latent Low-Rank Constraint, IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4322-4334, 2015 [pdf] [bib][code]
Conference Papers:
[C10]. Zhengming Ding, Ming Shao and Yun Fu. Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning. Computer Vision and Pattern Recognition (CVPR), 2017. [pdf][bib][code]
Zhengming Ding, Ming Shao and Yun Fu. 
Deep Robust Encoder through Locality Preserving Low-Rank Dictionary. European Conference on Computer Vision, (ECCV), 2016. [pdf][bib][poster][code]
[C8]. Zhengming DingNasser Nasrabadi  and Yun Fu. Deep Transfer Learning for Automatic Target Classification: MWIR to LWIR. SPIE Defense+ Security,  2016 (Best Paper Award) [pdf][bib] [slides][code]
[C7]. Zhengming Ding, Ming Shao and Yun Fu. Transfer Learning for Image Classification with Incomplete Multiple Sources. IJCNN, 2016 [pdf][bib][slides][code]
[C6]. Zhengming Ding, Nasser M Nasrabadi and Yun Fu. Task-driven Deep Transfer Learning for Image Classification.  ICASSP, 2016. [pdf][bib][poster][code]
[C5]. Zhengming Ding and Yun Fu. Robust Multi-view Subspace Learning through Dual Low-rank Decompositions. AAAI, 2016. (Acceptance Rate: 26% = 549/2132) [pdf][bib][code][slides]
[C4]. Zhengming Ding, Ming Shao, and Yun Fu. Deep Low-rank Coding for Transfer Learning. IJCAI, 2015 (20-min talk). [pdf][slides][poster][bib][code]
[C3]. Zhengming Ding, Sungjoo Suh, Jae-Joon Han, Changkyu Choi, and Yun Fu, Discriminative Low-Rank Metric Learning for Face Recognition, FG, 2015. [pdf] [bib][code][poster]
[C2]. Zhengming Ding, Yun Fu. Low-Rank Common Subspace for Multi-View Learning. ICDM, 2014. (regular paper, 71 out of 727) [pdf][slides][bib][code]
[C1]. Zhengming Ding, Ming Shao, Yun Fu.  Latent Low-Rank Transfer Subspace Learning for Missing Modality Recognition. AAAI, 2014. [pdf][poster][bib][code]

Selected Awards
  • FG Travel Grant, 2017
  • National Institute of Justice Fellowship, 2016
  • IJCAI Travel Grant, 2016
  • SPIE Best Paper Award, 2o16
  • AAAI Student Travel Award, 2016
  • ICDM Student Travel Award, 2014 
  • ACM MM Student Travel Award, 2014
  • National Graduate Scholarship,  2012
  • National Inspirational Scholarship,  2007
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