刘诚，硕士生导师，2018年7月博士毕业于香港城市大学。2019入职汕头大学并入选汕头大学卓越人才计划，主要研究方向为机器学习算法：多视图学习，多任务学习，生存分析算法等，同时运用人工智能算法解决与临床癌症相关的生物信息学应用问题。 期间主持了5 个科研项目，包含国家自然科学基金青年基金项目，广东省自然科学基金面上项目，广东省教育厅青年创新人才项目，中国科学技术大学苏州高等研究院横向项目，汕头大学卓越人才科研启动基金(100万)。 在研究领域，作为第一作者（含五篇通讯作者）的身份在SCI期刊/CCF推荐会议上发表了16篇论文， 一作和通讯总影响因子约110，h-index 15，i-index 21。发表的论文中包括8篇中科院一区期刊/CCF A在内IEEE Transactions系列顶级期刊上，以及6篇中科院二区/CCF B论文。其中包含一篇中科院一区（影响因子10.04）高被引论文。

主持5个项目:

1.  面向多组学癌症数据的多视图生存分析算法的研究, 国家自然科学基金项目, 青年基金项目（2022-2024），30万， 主持

2. 自适应多任务学习算法研究及其在癌症数据分析中的应用,广东省自然科学基金项目面上项目（2022-2024）,  10万， 主持

3. 结构化稀疏模型及其生物数据的应用, 广东省教育厅青年创新人才项目，5万， 主持

4. 基于组学数据方面的智能模型的快速实现应用开发服务， 中国科学技术大学苏州高等研究院（横向），10万， 主持

5. 基于正则化结构化稀疏模型及其医疗数据应用，汕头大学卓越人才科研启动基金，100万， 主持

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   主要发表论文(第一作者/通讯作者)： 17 篇（ 8篇中科院一区期刊/CCF A； 6篇中科院二区/CCF B）:

1. [IEEE TKDE] Cheng Liu*#, Si Wu, Rui Li, Dazhi Jiang, Hau-San Wong: Self-Supervised Graph Completion for Incomplete Multi-View Clustering. IEEE Transactions on Knowledge and Data Engineering (2023)  [CCF A] [中科院2区] ,  top期刊 (中科院分区定义)，影响因子 8.9

2. [IEEE TNNLS] Cheng Liu*#, Rui Li, Hangjun Che, Si Wu, Dazhi Jiang, Zhiwen Yu and Hau-San Wong: Self-Guided Graph Partial Propagation for Incomplete Multi-View Clustering. IEEE Transactions on Neural Networks and Learning Systems (2023)  [CCF B] [中科院1区]  top顶刊 (中科院分区定义)，影响因子 10.4.

3. [IEEE TNNLS] Cheng Liu*#, Wenming Cao, Si Wu, Wenjun Shen, Dazhi Jiang, Zhiwen Yu, Hau-San Wong: Asymmetric Graph-Guided Multi-Task Survival Analysis with Self-Paced Learning. IEEE Transactions on Neural Networks and Learning Systems (2020)  [CCF B] [中科院1区] (ESI 高被引论文)  top顶刊 (中科院分区定义)，影响因子 10.4.

4. [IEEE TCYB] Cheng Liu*#, Chutao Zheng, Si Wu, Zhiwen Yu, Hau-San Wong: Multitask Feature Selection by Graph-Clustered Feature Sharing. IEEE Transactions on Cybernetics 50(1): 74-86 (2020) [CCF B] [中科院1区]  top顶刊 (中科院分区定义)，影响因子 11.8

5. [IEEE TBME] Cheng Liu*, Si Wu, Dazhi Jiang, Zhiwen Yu, Hau-San Wong: View-Aware Collaborative Learning for Survival Prediction and Subgroup Identification. IEEE Transactions on Biomedical and Engineering (2022) [中科院工程技术2区].

6. [IEEE TCBB] Cheng Liu*,, Hau-San Wong#: Structured Penalized Logistic Regression for Gene Selection in Gene Expression Data Analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017) [CCF B]

7. [IEEE TCBB] Cheng Liu*, Wenming Cao, Si Wu#, Wenjun Shen, Dazhi Jiang, Zhiwen Yu, Hau-San Wong: Supervised graph clustering for cancer subtyping based on survival analysis and integration of multi-omic tumor data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020. [CCF B]

8. [PR] Cheng Liu*, Chutao Zheng, Sheng Qian, Si Wu and Hau-San Wong: Encoding Sparse and Competitive Structures among Tasks in Multi-Task Learning. Pattern Recognition 88: 689-701 (2019).[CCF B][中科院1区],  top顶刊 (中科院分区定义)，影响因子 8.0

9. [KBS] Cheng Liu*, Si Wu, Wenming Cao, Wenjun Shen, Dazhi Jiang, Zhiwen Yu, Hau-San Wong: Joint Subspace and Discriminative Learning for Self-Paced Domain Adaptation. Knowledge Based System  [CCF C] [中科院1区],  top顶刊 (中科院分区定义)，影响因子 8.8

10. [ASOC] Cheng Liu*, Yong Liang, Xin-Ze Luan, Kwong-Sak Leung, Tak-Ming Chan, Zongben Xu, Hai Zhang: The L1/2 regularization method for variable selection in the Cox model. Applied Soft Computing. (2014)  [中科院2区], top顶刊 (中科院分区定义)，影响因子 8.7

11. [IEEE TCYB] Jian Zhong, Xiangping Zeng, Wenming Cao, Si Wu#, Cheng Liu#, Zhiwen Yu, Hau-San Wong: Semisupervised Multiple Choice Learning for Ensemble Classifification. IEEE Transactions on Cybernetics (2020)  [CCF B] [中科院1区] (共同通讯作者). top顶刊 (中科院分区定义)，影响因子 11.8

12. [ASOC] Sijin Zhou, Dongmin Huang, Cheng Liu#, Dazhi Jiang#: Objectivity meets subjectivity: A subjective and objective feature fused neural network for emotion recognition. Applied Soft Computing (2022)  [中科院2区]  (共同通讯作者).  top顶刊 (中科院分区定义)，影响因子 8.7

13. [INS] Jiaxin Li, Haohong Zhou, Si Wu#, Cheng Liu#, Hau-San Wong Collaborative Learning-based Unknown-class Instance Identification for Open-set Domain Adaptation  Information Science (2023).  [CCF B] [中科院1区]  (共同通讯作者).  top顶刊 (中科院分区定义)，影响因子 8.1

14. [ICONIP-2017] Cheng Liu, Wen-Ming Cao, Chutao Zheng, Hau-San Wong: Learning With Partially Shared Features in Multi-Task Learning. The 24th International Conference on Neural Information Processing ICONIP (5) 2017: 95-104 （CCF C会议）

15. [ICSI-2012] Cheng Liu, Yong Liang, Xin-Ze Luan, Kwong-Sak Leung, Tak-Ming Chan, Zongben Xu, Hai Zhang: Iterative L1/2 Regularization Algorithm for Variable Selection in the Cox Proportional Hazards Model. ICSI (2) 2012: 11-17

16. [ICME-2022] Junjie Liang (研究生), Hang Gao (研究生), Haojun Sun, Rui Li and Cheng Liu#. Reliable self-supervised information mining for deep subspace clustering. IEEE ICME 2022 （CCF B）(通讯作者)

17. [SMC-2022] Hang Gao (研究生), Yunshan Li（本科生）and Cheng Liu#. Progressive Deep Subspace Clustering based on Sample Reliability. IEEE SMC 2022 （CCF C）(通讯作者)

在投论文(*第一作者/#通讯作者)

[Neurocomputing] Cheng Liu*#, Sentao Chen, Lin Zheng, Dazhi Jiang: Adaptive Graph-Guided Co-Regularization for Clustered Multi-Task Learning. Neurocomputing (Revision)

[IEEE TCBB] Hang Gao (研究生), Wenjun Shen and Cheng Liu#. Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics (Revision)（CCF B）(通讯作者/指导老师)

[CIS] Zhen Zheng (研究生), Rui Li and Cheng Liu#. Learning Robust Class-level Alignment for Cross Domain Medical Image Analysis via Dual Consistency Regularizations. Complex & Intelligent System (Submitted) [中科院计算机科学2区] (通讯作者/指导老师)

部分合作（Co-author）论文：

[IEEE TIP] Jichang Li, Si Wu, Cheng Liu, Zhiwen Yu, Hau-San Wong: Semi-Supervised Deep Coupled Ensemble Learning With Classification Landmark Exploration. IEEE Transactions on Image Processing (2020)  [CCF A] [中科院1区]

[IEEE TIP] Si Wu, Shufeng Wang, Robert Laganiere, Cheng Liu, Hau-San Wong, Yong Xu: Exploiting Target Data to Learn Deep Convolutional Networks for Scene-Adapted Human Detection. IEEE Transactions on Image Processing (2018) [IEEE TIP] [CCF A] [中科院1区]

[IEEE TIP] Haohong Zhou, Mohamed Azzam, Jian Zhong, Cheng Liu, Si Wu, Hau-San Wong: Knowledge Exchange Between Domain-Adversarial and Private Networks Improves Open Set Image Classification. IEEE Transactions on Image Processing (2021) [IEEE TIP] [CCF A] [中科院1区]
[IEEE TKDE] Zhiwen Yu, Zhongfan Zhang, Wenming Cao, Cheng Liu, CL Philip Chen, Hau-San Wong: Gan-based enhanced deep subspace clustering networks [IEEE TKDE] [CCF A] [中科院2区]

[IEEE TAI] Geng Tu, Jintao Wen, Cheng Liu, Dazhi Jiang, Erik Cambria Context-and sentiment-aware networks for emotion recognition in conversation. IEEE Transactions on Artificial Intelligence.

[PR] Wenming Cao, Zhongfan Zhang, Cheng Liu, Rui Li, Qianfen Jiao, Zhiwen Yu, Hau-San Wong: Unsupervised discriminative feature learning via finding a clustering-friendly embedding space Pattern Recognition (2022) [PR][CCF B][中科院1区]

[Information Fusion] Jintao Wen, Dazhi Jiang, Geng Tu, Cheng Liu, Erik Cambria Dynamic interactive multiview memory network for emotion recognition in conversation Information Fusion [CCF B][中科院1区]

[INS] Chenglu Li, Hangjun Che, Man-Fai Leung, Cheng Liu, Zheng Yan. Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints. Information sciences. [CCF B][中科院1区]

[INS] Dazhi Jiang, Geng Tu, Donghui Jin, Kaichao Wu, Cheng Liu, Lin Zheng, Teng Zhou: A hybrid intelligent model for acute hypotensive episode prediction with large-scale data. Information Science. 546: 787-802 (2021) [CCF B] [中科院1区]

[INS] Qianfen Jiao, Jian Zhong, Cheng Liu, Si Wu, Hau-San Wong Perturbation-insensitive cross-domain image enhancement for low-quality face verification. Information Science [CCF B] [中科院1区]

[KBS] Sentao Chen, Hanrui Wu, Cheng Liu. Domain invariant and agnostic adaptation. Knowledge-Based Systems. [CCF C] [中科院1区]

[KBS] Rui Li, Cheng Liu, Dazhi Jiang: Efficient dynamic feature adaptation for cross language sentiment analysis with biased adversarial training Knowledge-Based Systems. [CCF C] [中科院1区]

[BMC Bioinformatics] Yong Liang, Cheng Liu, Xin-Ze Luan, Kwong-Sak Leung, Tak-Ming Chan, Zongben Xu, Hai Zhang: Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification. BMC Bioinformatics  (2013) [CCF C] [中科院3区]

[Neurocomputing] Chutao Zheng, Cheng Liu, Hau-San Wong: Corpus-based topic diffusion for short text clustering. Neurocomputing  (2018) [CCF C] [中科院2区]

[Neurocomputing] Sheng Qian, Hua Liu, Cheng Liu, Si Wu, Hau-San Wong: Adaptive activation functions in convolutional neural networks. Neurocomputing 272: 204-212 (2018) [CCF C] [中科院2区]

[Neurocomputing] Dongmin Huang, Sentao Chen, Cheng Liu, Lin Zheng, Zhihang Tian, Dazhi Jiang:  Differences first in asymmetric brain: A bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition. Neurocomputing 448: 140-151 (2021) [中科院2区]

[CIBM] Zhihui He, Yingqing Lin, Runguo Wei, Cheng Liu, Dazhi Jiang Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction. Computers in Biology and Medicine. [CCF C][中科院2区]

[Soft Computing] Xin-Ze Luan, Yong Liang, Cheng Liu, Kwong-Sak Leung, Tak-Ming Chan, Zongben Xu, Hai Zhang: A novel L1/2 regularization shooting method for Cox’s proportional hazards model. Soft Computing. (2014) [中科院3区]

[Applied Intelligence] Xuanhao Yang,  Hangjun Che,  Man-Fai Leung, Cheng Liu. Adaptive graph nonnegative matrix factorization with the self-paced regularization. Applied Intelligence. [中科院2区]

[MSSP] Guorong Xiao, Yunju Ma, Cheng Liu, Dazhi Jiang: A machine emotion transfer model for intelligent human-machine interaction based on group division Mechanical Systems and Signal Processing [中科院1区]

[Connection Science] Dazhi Jiang, Runguo Wei, Zhihui He, Senlin Lin, Cheng Liu, Yingqing Lin GASN: gamma distribution test for driver genes identification based on similarity networks. Connection Science [CCF C]

[IEEE SMC] Chutao Zheng, Cheng Liu, Hau-San Wong: Iterative Term Weighting for Short Text Data. IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2015:1687-1692. (Nominee of Best Paper Award) （CCF C）

[CVPR] Si Wu, Jichang Li, Cheng Liu, Zhiwen Yu, Hau-San Wong: Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification. CVPR 2019: 6500-6509 （CCF A）

[CVPR] Xiwen Wei, Zhen Xu, Cheng Liu, Si Wu, Zhiwen Yu, Hau San Wong: Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation CVPR 2013:（CCF A）

[IJCAI] Sheng Qian, Guanyue Li, Wen-Ming Cao, Cheng Liu, Si Wu, Hau-San Wong: Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations. IJCAI 2019: 3268-3274 （CCF A）

[COLING] Rui Li, Cheng Liu, Dazhi Jiang: Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (CCF B)

[ICME] Zhongfan Zhang, Wenming Cao, Cheng Liu, Rui Li, Qianfen Jiao, Zhiwen Yu, C. L. Philip Chen, Hau-San Wong: Unsupervised Ensemble Learning Via Network Generation. ICME 2021: 1-6 (CCF B)

[ISBI] Xin-Ze Luan, Yong Liang, Cheng Liu, Zongben Xu, Hai Zhang, Kwong-Sak Leung, Tak-Ming Chan: Regularization Path for Linear Model via Net Method. ICSI (2) 2012: 414-421