Welcome International Students!

Ph.D, Applied Mathematics, July 2011

• Dissertation: “Multi-Manifold Learning Theory and Algorithms with Application in Machine

Learning”

• Advisor: Professor Bo Zhang, Co-Advisor: Professor Hong Qiao

Minzu University of China, Beijing, P. R. China

B.E., Department of Mathematics and Information Sciences, June, 2006

Professor at Wenzhou University, Dec., 2018 - present

• Interested in Deep learning, Self-driving vehicles

Associate Professor at Wenzhou University, Sep., 2013 - Dec.,2018

• Interested in Feature Learning, Sparse Representation, Low-Rank Representation and their

applications in large scale image classifcation and data mining.

Lecturer at Wenzhou University, July, 2011 - Sep.,2013

• Interested in Feature Learning, Sparse Representation, Low-Rank Representation and their

applications in large scale image classifcation and data mining.

Ph.D at Academy of Mathematics and Systems Science, Chinese Academy of Sciences Jul., 2006 -

Jul., 2011

• Working with Prof. Bo Zhang and Prof. Hong Qiao on manifold learning, sparse representation

and image data mining.

Research Internship at Samsung SAIT Research Center, Beijing, China, Nov. 2010 - March. 2011

• Develop an image classifcation system for PASCAL VOC 2007.

Machine learning, Artifcial intelligence, Big data mining, Sparse representation, Self-driving vehicle

Undergraduate: Calculus; Digital Image Processing; Mathematical Models

Graduate: Pattern Recognition

Projects and Supervisions

Project leader, National Natural Science Foundation of China, "Semi-supervised learning for high-dimensional big data", Grant No. 61772373, Jan. 2018 - Dec. 2021.

Publications

Mingyu Fan, Hong Qiao and Bo Zhang, Intrinsic dimension estimation of manifolds by incising balls, Pattern Recognition, 42 (2009), 780-787. 2.

Mingyu Fan, Nannan Gu, Hong Qiao, Bo Zhang, Sparse Regularization for semi-supervised classification, Pattern Recognition, 44 (8) (2011) 1777-1784. 3.

Nannan Gu, Mingyu Fan, Hong Qiao, Bo Zhang, Discriminative Sparsity Preserving Projections for Semi-Supervised Dimensionality Reduction. IEEE Signal Process. Lett. 19(7): 391-394 (2012).