I received my B.Sc. degree from UESTC (University of Electronic Science and Technology of China) in June 2019. In the same year, I was admitted to study for an M.Sc. degree at Nanjing University without an entrance examination. I received my M.Sc. degree from the School of Artificial Intelligence, Nanjing University, in June 2022. In the same year, I was admitted to pursue a Ph.D. degree in the School of Artificial Intelligence at Nanjing University, where I belong to the LAMDA Group led by Prof. Zhi-Hua Zhou(周志华). I am currently supervised by Prof. De-Chuan Zhan(詹德川) and Prof. Han-Jia Ye(叶翰嘉).

🔬 Research Interests

My research mainly focuses on machine learning, especially:

Class-imbalanced Learning

Class-imbalanced learning addresses the challenge of datasets with imbalanced class distributions, where one or more classes have significantly fewer instances compared to others.

Semi-supervised Learning

Semi-supervised learning utilizes labeled data to ground predictions and unlabeled data to learn the larger data distribution, achieving strong results with fractions of labeled data.

Federated Learning

Federated learning is a decentralized approach that enables training machine learning models on distributed data sources while keeping the data local and preserving privacy.