Jingguo Lan
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Jingguo Lan (兰敬国) |
About Me
I am a Ph.D. student in Statistics at the School of Management, University of Science and Technology of China (USTC), advised by Prof. Xueqin Wang.
My research focuses on optimization algorithms for large-scale machine learning, including stochastic optimization, distributed algorithms (federated learning), and distributionally robust optimization. I am particularly interested in developing communication-efficient and statistically optimal methods for high-dimensional sparse learning in distributed settings.
Research Interests
Optimization for Machine Learning: Scalable algorithms for large-scale statistical learning
Distributed Optimization: Communication-efficient algorithms, federated learning, decentralized optimization
Best Subset Selection: Sparse estimation under \(\ell_0\) constraints in distributed environments
Robust Optimization: Distributionally robust optimization methods
Stochastic Optimization: Stochastic gradient methods, variance reduction techniques
News
[2025.11] New preprint: “Communication-Efficient Decentralized Optimization via Double-Communication Symmetric ADMM” on arXiv.
[2024.08] New preprint: “Minimax and Communication-Efficient Distributed Best Subset Selection with Oracle Property” on arXiv.
[2024.09] Started Ph.D. program in Statistics at USTC.
Education
Ph.D. in Statistics, University of Science and Technology of China, 2024 - present
M.S. in Data Science, University of Science and Technology of China, 2021 - 2024
B.S. in Mathematics and Applied Mathematics, University of Science and Technology of China, 2017 - 2021
More details on the Biography page.