About 中文

I’m now an Assistant Professor and PhD supervisor in Department of Computer Science and Technology, School of Informatics, Xiamen University. I received my PhD degree on computer science from Hong Kong Baptist University in 2019 supervised by Prof. Yiu-ming Cheung and received my bachelor and master degree on software engineering from University of Macau in 2012 and 2014, supervised by Prof. Yuan Yan Tang. I was a research intern at Tencent in 2018. I have published over 40 top-tier papers, many of which have been featured in top-tier machine learning journals (JCR Q1) such as IEEE TNNLS and IEEE TCYB, as well as premier AI and computer vision conferences (CCF-A) like CVPR, ICCV, AAAI, and IJCAI. I have led several projects, including the General Project and Youth Project of the National Natural Science Foundation of China, the Major Project of the Fujian New Think Tank, the General Project of the Fujian Natural Science Foundation, and open projects at the Zhejiang Lab. I serve as a member of the Machine Vision Committee and the Secretary-General of the Xiamen Member Activity Center of the China Society of Image and Graphics. I also serve as the Associate Editor of the international journal IEEE Transactions on Emerging Topics in Computational Intelligence. I was honored as the “Xiaomi Young Scholar” by Xiamen University in 2023. My research interest includes artificial intelligence, machine learning. My research direction is trustworthy deep learning, including long-tail learning, federated learning, label-noise learning and continual learning.

For undergraduate students, please check 本科招生简章. For postgraduate students, please check 硕士招生简章.

There is still a few postgraduate quota in Fall 2025. Please contact me ASAP. 

Recent News

  • Apr 17 2024: One paper about long-tailed continual learning is accepted by IJCAI . Congrats to Chenxing!
  • Dec 27 2023: Receive Xiaomi Youth Scholar Award.
  • Dec 9 2023: Three AAAI papers are accepted!
  • Aug 24 2023: One NSFC general project is granted.
  • July 14 2023: One paper about long-tailed label-noise learning is accepted by ICCV . Congrats to Yiliang!
  • Mar 28 2023: Congrats to Xinyi for receiving the PhD offer from UCL supported by UCL-CSC scholarship!
  • Feb 28 2023: One paper about long-tailed learning is accepted by CVPR. Congrats to Yan!
  • Apr 21 2022: One paper about long-tailed federated learning is accepted by IJCAI. Congrats to Xinyi!
  • Dec 24 2020: One Zhejiang Lab open project is granted.
  • Sep 18 2020: One NSFC youth project is granted.
  • Sep 11 2020: One provincial general project is granted.
  • Mar 12 2020: Teach for one semester at Xiamen University Malaysia.
  • Dec 10 2019: Join the Center for PAMI at Xiamen University.

Research Interests

Deep Learning with Incomplete Labels

Machine learning with incomplete labels addresses effective model training in the presence of significant label deficiencies, such as imbalanced label distribution or noisy labels. The goal is to achieve accurate predictions despite low data quality. We enhance model performance on incomplete labeled data by introducing more effective label processing and integration strategies.

Trustworthy Federated Learning

Trustworthy federated learning aims to perform model training in a distributed data environment, ensuring data privacy and security while maintaining high model performance. It also explores personalization and fairness within the federated learning framework. Our work focuses on data heterogeneity, long-tailed distribution, missing labels, and noise issues, improving the robustness and performance of models in heterogeneous data and noisy environments.

Continual Learning and Prompt Learning

In real-world applications, the data environment is dynamically changing, and models need to continuously learn new knowledge while retaining existing knowledge without forgetting. Our research involves task-imbalanced continual learning, concept drift handling in incremental learning, and domain generalization, aiming to enhance model stability and adaptability in dynamic and imbalanced data environments.

Supervised Students

  • Pinxin Qian (2020-2023, Employed in Alibaba): Federated learning
  • Xinyi Shang (2020-2023, Pursuing PhD at UCL): Federated learning
  • Yiliang Zhang (2021-2024, Employed in Alibaba): Label-noise learning
  • Yan Jin (2021-2024, Pursuing PhD at BUAA): Long-tail learning
  • Xiaolin Huang (2021-2024, Employed in JD): Semi-supervised learning
  • Fengling Lv (2021-2024, Employed in Xiaomi): Federated learning
  • Chengbin Zheng (2021-2024, Employed in CATL): Graph neural networks
  • Chenxing Hong (2022-): Continual learning
  • Lin Chen (2022-): Federated learning
  • Liyuan Wang (2022-): Long-tail learning
  • Yijie Liu (2023-): Federated learning
  • Shanshan Yan (2023-): Federated learning
  • Yizhou Chen (2023-): Semi-supervised learning
  • Hezhao Liu (2023-): Continual learning
  • Shu Chen (2023-): Label-noise learning
  • Zenghui Huang (2024-): Long-tail learning
  • Ruichi Zhang (2024-): Long-tail learning
  • Junpeng Chen (2024-): Long-tail learning
  • Shihao Hou (2024-): Federated learning