Resume
Academic Achievements
- Published multiple papers in international conferences such as ICML (International Conference on Machine Learning), IEEE Transactions on Information Theory, etc. Specific papers include but are not limited to: Exactly Tight Information-theoretic Generalization Bounds via Binary Jensen-Shannon Divergence, How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis, Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective, etc.
Research Experience
- Post Doctoral Scholar at the School of Biomedical Informatics, Ohio State University.
Education
- Completed Ph.D. degree at the School of Computer Science and Technology, Xi’an Jiaotong University in September 2024, advised by Prof. Chen Li and Prof. Tieliang Gong; obtained B.E. degree in Computer Science and Technology at Xi’an Jiaotong University.
Background
- Research interests: machine learning and statistical learning theory. Recently, focusing on information-theoretic generalization analysis and robust learning in supervised learning, contrastive learning, and domain generalization. Main research topics include: analyzing the generalization ability of randomized learning algorithms through the lens of information theory; designing effective and robust learning algorithms based on information-theoretic measurements and analysis; developing computationally efficient approximations for information-theoretic quantities and measurements.