معرفی
Xiaodong Li is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of high-dimensional statistics, statistical learning, and nonconvex optimization, with applications in matrix completion, phase retrieval, and network analysis.
His research interests focus on developing and analyzing statistical methodologies for modern data science challenges. He investigates high-dimensional inference, nonconvex optimization algorithms, and robust statistical methods for complex data structures. His work often bridges theoretical guarantees with practical algorithmic implementation, particularly in unsupervised learning and signal processing.
The recent publications highlight a strong trend toward theoretical analysis of nonconvex methods in matrix estimation, community detection in networks, and high-dimensional signal recovery. His work frequently appears in top-tier journals such as the Annals of Statistics, IEEE Transactions on Information Theory, and Journal of Machine Learning Research, reflecting consistent contributions to both statistical theory and machine learning.
Scientific Awards:
- No scientific awards mentioned in the provided text.
Xiaodong Li has actively advised multiple PhD students, including Ji Chen (2020), Xingmei Lou (2022), Yucheng Liu (2022), and Xiaohan Hu (2024), with Zhentao Li currently under supervision. His research is supported by publications rather than explicitly mentioned grants, indicating a strong publication-driven academic profile. He teaches a wide range of courses from undergraduate probability and regression to graduate-level mathematical statistics and longitudinal data analysis.
While no specific lab or research group name is mentioned, his collaborative work with researchers such as Emmanuel J. Candes, T. Tony Cai, and Yudong Chen suggests active participation in the broader statistical learning and high-dimensional data analysis community. His recent work on polytree models and community detection indicates ongoing exploration into structured high-dimensional models and network inference.
