Zhangsong Li
Hello! I am a third-year Ph.D student in School of Mathematical Sciences at Peking University, where I am fortunate to be advised by Jian Ding. Previously, I obtained my B.S. degree in mathematics from Peking University.
My research interest lies in the intersection of probability, high-dimensional statistics and theoretical computer science.
Links: CV (last update: September 2025), Google Scholar.
Email: ramblerlzs at pku dot edu dot cn.
Research
- A smooth computational transition in tensor PCA
Zhangsong Li
[arXiv]
- Detection and reconstruction of a random hypergraph from noisy graph projection
Shuyang Gong, Zhangsong Li, Qiheng Xu
[arXiv]
- Asymptotic diameter of preferential attachment model
Hang Du, Shuyang Gong, Zhangsong Li, Haodong Zhu
[arXiv] [Slides]
- A computational transition for detecting multivariate shuffled linear regression by low-degree polynomials
Zhangsong Li
[arXiv]
- Detecting correlation efficiently in stochastic block models: breaking Otter's threshold by counting decorated trees
Guanyi Chen, Jian Ding, Shuyang Gong, Zhangsong Li
[arXiv]
- Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs
Zhangsong Li
Conference version in Proceedings of 29th APPROX/RANDOM, no. 30, pp. 1--18, 2025.
[arXiv] [Conference]
- Robust random graph matching in Gaussian models via vector approximate message passing
Zhangsong Li
Conference version in Proceedings of 38th COLT, pp. 3580--3581, 2025.
[arXiv] [Conference] [Slides]
- A computational transition for detecting correlated stochastic block models by low-degree polynomials
Guanyi Chen, Jian Ding, Shuyang Gong, Zhangsong Li
Annals of Statistics (to appear).
[arXiv] [Slides by Jian Ding]
- The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regime
Shuyang Gong, Zhangsong Li
[arXiv] [Slides by Shuyang Gong]
- Low-degree hardness of detection for correlated Erdős-Rényi graphs
Jian Ding, Hang Du, Zhangsong Li
Annals of Statistics (to appear).
[arXiv] [Slides]
- A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation
Jian Ding, Zhangsong Li
[arXiv]
- A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation
Jian Ding, Zhangsong Li
Foundations of Computational Mathematics, vol. 25, no. 4, pp. 1287--1344, 2025.
[arXiv] [Journal] [Slides]
Talks
- Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs (remote talk). The 29th International Conference on Randomization and Computation, August 2025, Berkeley, United States.
- Robust random graph matching in Gaussian models via vector approximate message passing. The 38th Annual Conference on Learning Theory, July 2025, Lyon, France.
- Robust random graph matching in Gaussian models via vector approximate message passing. International Conference on Applied Probability, June 2025, Beijing, China.
- Asymptotic diameter of preferential attachment model (joint with Shuyang Gong). YMSC Probability Seminar, May 2025, Beijing, China.
- Recent progress on random graph matching problems. Tsinghua University, March 2025, Beijing, China.
- Low-degree hardness of detection for correlated Erdős-Rényi graphs. Tsinghua Sanya International Mathematics Forum, January 2024, Sanya, China.
- A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation. Peking University, June 2023, Beijing, China.
Teaching Assistant
Probability Theory, Spring 2025.
Advanced Probability Theory, Fall 2024.
Applied Stochastic Process (Honor), Fall 2023.