• MEng: 1) Electronic Information     

• MS：1)Intelligent Science and Technology     

• Ph.D: 1) Electronic Information; 2) Intelligent Science and Technology;

Guo Kailing is a pre-appointed assistant professor and doctoral supervisor at South China University of Technology. He is a joint doctoral student of South China University of Technology and the University of Technology Sydney. His main research areas include low-rank and sparse learning, deep learning optimization and model compression, and multimodal human body data processing. In the past five years, he has presided over one project each funded by the National Natural Science Foundation of China for Young Scientists, the Natural Science Foundation of Guangdong Province, and the China Postdoctoral Science Foundation. He/She has undertaken research and development projects in key fields of Guangzhou City, published over 20 papers in top journals and conferences in the field, and obtained 4 authorized national invention patents.

• Low-rank and sparse learning, deep learning optimization and model compression, multimodal human body data processing

• Kailing Guo, Liu Liu, Xiangmin Xu, Dong Xu, and Dacheng Tao, “GoDec+: Fast and Robust Low-rank Matrix Decomposition Based on Maximum Correntropy”, IEEE Transactions on Neural Network and Learning Systems, 2018 (TNNLS，JCR Zone 1, impact factor 11.683)

• Kaiing Guo, Xiangmin Xu, and Dacheng Tao, “Discriminative GoDec+ for Classification”, IEEE Transactions on Signal Processing , 2017(TSP，JCR Zone 1, influence factor 4.203)

• Zhenquan Lin, Kailing Guo, Xiaofen Xing, Xiangmin Xu, “Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values”, ACM International Conference on Multimedia, 2021 (Top Meeting of the Field)

• Bolun cai, Xiangmin Xu, Kailing Guo, Kui Jia, Bin Hu, Dacheng Tao, A Joint Intrinsic-Extrinsic Prior Model for Retinex, IEEE International Conference on Computer Vision, 2017 (Top Meeting of the Field)

• Yucheng Cai, Zhuowen Yin, Kailing Guo, Xiangmin Xu, “Pruning the Unimportant or Redundant Filters? Synergy Makes Better”, IEEE International Joint Conference on Neural Networks, 2021 (important conferences in the field)