贾育衡

发布者：贾育衡发布时间：2020-11-14浏览次数：28579



Yuheng Jia(贾育衡)

Ph.D., Associate Professor 

School of Computer Science and Engineering, Southeast University

I am a member of PAttern Learning and Mining(PALM) Lab. 


贾育衡, 博士，副教授，江苏省“双创博士”，东南大学“至善青年学者”、“紫金青年学者”。2019年获得香港城市大学(CityU)博士学位，2019-2020年任香港城市大学博士后研究员。 导师为Sam Kwong 讲席教授。2020年起在东南大学计算机科学与工程学院任职副教授。现为东南大学PALM实验室成员。曾任斯坦福大学（Stanford University）访问学者（2018年）。研究内容广泛涉及机器学习和数据表示的多个子领域，主要包括半监督学习、高维数据分析与建模、张量表示与建模、图机器学习，深度学习以及在计算机视觉、高光谱表示、社区检测等方向的一些应用。在相关研究领域的国际会议和期刊上发表学术论文40+篇，其中CCF-A/IEEE Trans 论文30+。担任多个国际著名期刊会议的程序委员会委员和审稿人。主持多项国家自然科学基金、江苏省自然科学基金等横纵向项目。


My research interests broadly include topics in machine learning and data representation, such as semi-supervised learning, high-dimensional data modeling and analysis, low-rank tensor/matrix approximation and factorization,  graph signal processing and machine learning on graphs, and deep learning as well as some related applications. I have published about 40 peer-reviewed papers in top-tier journals and conferences with respect to those topics.  I defended my thesis in Nov. 2018 and officially received the Ph.D. degree in Feb. 2019 from the Computer Science Department at the City University of Hong Kong, where I was advised by Professor Sam Kwong.


欢迎2024年入学的研究生（计算机学院、软件学院、东蒙苏州联合研究生院）报考，请邮件联系。

备注：2024年，可招收海南专项研究生一名，欢迎报名！


计划每年辅导2-3名本科生进行科研训练，欢迎有兴趣的同学报名！（请申请同学在申请时附上自己的简历）




研究生招生说明：

作为导师，我有以下承诺：

1，我深知学生的成功就是老师的成功，我会全力帮助和指导学生进步。

2，我不会安排学生做无关的事务性工作，为学生营造良好的科研环境。

3，尽全力给予学生足够的指导时间。平均每周每生一对一的指导时间（组会、面谈、网络讨论等）不少于一小时。

4，坚持师生平等，尊重学生权利。

  

作为我的学生，希望您可以做到:

1，科研不容瑕疵。不允许任何形式的造假，剽窃等学术不端等行为。

2，科研是一个充满痛苦和惊喜的过程，兴趣和努力是克服痛苦的良药。请务必选择自己感兴趣的方向进行科研。

3，阅读是科研的不二法门，请大量阅读并按时参加组会。

4，尽管我会尽全力帮助您，但也希望您明白：达到毕业要求以取得学位是您自己的任务。所以希望您能够努力科研，有问题及时和老师沟通。

5，导师是研究生的第一负责人，任何非常规行为，请告知导师。



Contact Information:

Office: Room 230, School of Computer Science and Engineering, Southeast University Jiulonghu Campus, Nanjing, Jiangsu, China. (东南大学九龙湖校区，计算机楼，230房间)

Email: yhjia at seu dot edu dot cn


Selective Publications:(# Corresponding author, * Co-first author)

Journal Papers: 

  • [TIP] Z. Peng, H. Liu, Y. Jia #, et al. Adaptive Attribute and Structure Subspace Clustering Network, IEEE Transactions on Image Processing, 2022, in press, code.

  • [JSTAEORS] Shujun Yang, Yu Zhang, Yuheng Jia, Weijia Zhang. Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022.

  • [TCSVT] Y. Jia, H. Liu, et al. Self-Supervised Symmetric Nonnegative Matrix Factorization, IEEE Transactions on Circuits and Systems for Video Technology, 2021, in press, code.

  • [TIP] S. Yang, J. Hou, Y. Jia #, et al. Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification, IEEE Transactions on Image Processing, 2021, in press, code.

  • [Inf. Sci.] J. Cao, R. Wang, Y. Jia #, et al. No-Reference Image Quality Assessment for Contrast-Changed Images via a Semi-Supervised Robust PCA Model, Information Sciences, vol. 574, pp. 640-652, 2021.

  • [TCSVT] H. Liu *, Y. Jia *#, J. Hou, Q. Zhang, Global-Local Balanced Low-Rank Approximation of Hyperspectral Images for Classification, IEEE Transactions on Circuits and Systems for Video Technology, 2021, in press, code.

  • [TCSVT] Z. Peng, Y. Jia #, H. Liu. J. Hou, Q. Zhang, Maximum Entropy Subspace Clustering Network, IEEE Transactions on Circuits and Systems for Video Technology, 2021, in press, code.

  • [TCSVT] H. Liu *, Y. Jia *#, J. Hou, Q. Zhang, Learning Low-rank Graph with Enhanced Supervision, IEEE Transactions on Circuits and Systems for Video Technology, 2021, in press.

  • [TCSVT] Y. Jia, H. Liu. J. Hou, S. Kwong, Q. Zhang, Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation, IEEE Transactions on Circuits and Systems for Video Technology, 2021, in press, code.

  • [TCYB] Y. Jia, H. Liu. J. Hou, S. Kwong, Q. Zhang, Semi-supervised Affinity Matrix Learning via Dual-channel Information Recovery, IEEE Transactions on Cybernetics, 2020, in press, code.

  • [TII] H. Li, Y. Jia #, R. Cong, W. Wu, S. Kwong, C. Chen, Superpixel Segmentation Based on Spatially Constrained Subspace Clustering, IEEE Transactions on  Industrial Informatics, vol. 17, no. 11, pp. 7501-7512, 2021. 

  • [TCSVT] W. Wu, Y. Jia, S. Wang, R. Wang, H. Fan, S. Kwong, Positive and Negative Label-Driven Nonnegative Matrix Factorization, IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 7, pp. 2698-2710, 2021.

  • [SPL] Z. Peng, Y. Jia, J. Hou, Non-Negative Transfer Learning with Consistent Inter-domain Distribution, IEEE Signal Processing Letters, vol. 27, pp. 1720-1724, 2020.

  • [PR] R. Wang, S. Kwong, X. Wang, Y. Jia, Active k-Labelsets Ensemble for Multi-label Classification, Pattern Recognition, vol. 109, pp. 107583, 2021.

  • [TNNLS] Y. Jia, W. Wu, R. Wang, J. Hou, S. Kwong, Joint Optimization for Pairwise Constraint Propagation, IEEE Transactions on Neural Networks and Learning Systems, 2020, in press, code.

  • [TNNLS] Y. Jia, J. Hou, S. Kwong, Constrained Clustering with Dissimilarity Propagation Guided Graph-Laplacian PCA, IEEE Transactions on Neural Networks and Learning Systems, vol.32, no. 7, pp. 3167-3180, 2021, code.

  • [TSIPN] Y. Jia, H. Liu, J. Hou, S. Kwong, Clustering-aware Graph Construction: A Joint Learning Perspective, IEEE Transactions on Signal and Information Processing over Networks, vol. 6, pp. 357-370, 2020.

  • [TCYB] Y. Jia, H. Liu, J. Hou, S. Kwong, Semisupervised Adaptive Symmetric Nonnegative Matrix Factorization, IEEE Transactions on Cybernetics, vol. 51, no. 5, pp. 2550-2562, 2021, code.

  • [GRSL] S. Yang, J. Hou, Y. Jia, S. Mei, Q. Du, Hyperspectral Image Classification with Incremental Sparse Representation, IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 9, pp. 1598-1602, 2020.

  • [TNNLS] Y. Jia, H. Liu, J. Hou, S. Kwong, Pairwise Constraint Propagation with Dual Adversarial Manifold Regularization, IEEE Transactions on Neural Networks and Learning Systems, 2020, vol. 31, no. 12, pp. 5575-5587, 2020, code.

  • [TNNLS] Y. Jia, S. Kwong, J. Hou, W. Wu, Semi-Supervised Non-Negative Matrix Factorization with Dissimilarity and Similarity Regularization, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2510-2521, 2020, code.

  • [SPL] Y. Jia, S. Kwong, J. Hou, Semi-Supervised Spectral Clustering with Structured Sparsity Regularization, IEEE Signal Processing Letters, vol. 25, no. 3, pp. 403-407, 2018, code.

  • [TCYB] Y. Jia, S. Kwong, W. Wu, R. Wang, W. Gao, Sparse Bayesian Learning Based Kernel Poisson Regression, IEEE Transactions on Cybernetics, vol. 49, no. 1, pp. 56-68, 2019.

  • [TSMC] Y. Jia, S. Kwong, R. Wang, Applying Exponential Family Distribution to Generalized Extreme Learning Machine, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 5, pp. 1794-1804, 2020.

  • [TNNLS] W. Wu, Y. Jia, S. Kwong, J. Hou, Pairwise Constraint Propagation Induced Symmetric Nonnegative Matrix Factorization, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 12, pp. 6348-6361, 2018, code.

  • [TIP] W. Gao, S. Kwong, Y. Jia, Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding, IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 6074-6089, 2017.

  • [BioInfo] J. Zhang, S. Kwong, Y. Jia, K. Wong, NSSRF: global network similarity search with subgraph signatures and its applications, Bioinformatics,  vol. 33, no. 11, pp. 1696-1702, 2017.

  • [TMM] H. Li, S. Kwong, C. Chen, Y. Jia, R. Cong, Superpixel Segmentation Based on Square-wise Asymmetric Partition and Structural Approximation, IEEE Transactions on Multimedia, vol. 21, no. 10, pp. 2625-2637, 2019.

  • [Inf.Sci] W. Wu, S. Kwong, Y. Zhou, Y. Jia, W. Gao, Nonnegative Matrix Factorization with Mixed Hypergraph Regularization for Community Detection, Information Sciences, Vol. 435, pp. 263-281, 2018.

  • [TIP] W. Wu, S. Kwong, J. Hou, Y. Jia, H. Ip, Simultaneous Dimensionality Reduction and Classification via Dual Embedding Regularized Nonnegative Matrix Factorization, IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 3836-3847, 2019, code.

  • [JSTAEORS] S. Yang, J. Hou, Y. Jia, S.Mei, Q.Du, Pseudo-Label Guided Kernel Learning for Hyper-spectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,  vol. 12, no. 3, pp. 1000-1011, 2019, code on Google drive.

Conference Papers:

  • [ACM MM] Z. Peng, H. Liu, Y. Jia #, J. Hou, Attention-driven Graph Clustering Network, 2021.

  • [AAAI] Y. Jia, H. Liu, J. Hou, Qingfu Zhang, Clustering Ensemble Meets Low-rank Tensor Approximation, 2021, code.

  • [ACM MM] H. Liu *, Y. Jia *, J. Hou, Q. Zhang, Imbalance-aware Pairwise Constraint Propagation, 2019.

  • [ICME] Y. Jia, S. Kwong, J. Hou, W. Wu, Convex Constrained Clustering with Graph-Laplacian PCA, 2018.

  • [IntelliSys] Y. Jia, S. Kwong, W. Wu, W. Gao, R. Wang, Generalized Relevance Vector Machine, 2017.

  • [FUZZ-IEEE] R. Wang, S. Kwong, Y. Jia, Z. Huang, L. Wu, Mutual Information Based K-Labelsets Ensemble for Multi-Label Classification, 2018.

  • [GECCO] M. Wu, S. Kwong, Y. Jia, K. Li, Q. Zhang, Adaptive Weights Generation for Decomposition-based Multi-objective Optimization Using Gaussian Process Regression,  2017.

  • [ICMLC] W. Gao, S. Kwong, Y. Zhou, Y. Jia, et al., Multiscale Phase Congruency Analysis for Image Edge Visual Saliency Detection, 2016.


See the full publications on Google Scholar or my personal homepage.


Teaching:

Undergraduate course

Mathematical Optimization（最优化方法）， 2021 Autumn

Master student course

Artificial Intelligence（人工智能）， 2021 Spring

Ph.D. student course

High-dimensional Data Analysis （高维数据分析与处理），2021 Autumn