Shuyang Gong(巩舒阳)

About

I will be an FDS postdoc at the Institute for Foundations of Data Science, Yale University, starting Fall 2026. Previouly, I obtained my Ph.D degree in probability and mathematical statistics at School of Mathematical Sciences, Peking University. I obtained my B.S. degree in statistics from Department of Mathematics, Shandong University.

My research interest is probability theory.
Links: Google Scholar. ORCID. Linkedin.
My Erdős Number is 3.
Email address: gongsyprob at gmail dot com.

Curriculum Vitae

Research paper

• Stable algorithms cannot reliably find isolated perceptron solutions.
  with B. Huang, S. Li and M. Sellke.
  arXiv.


• Fundamental limits of community detection in contextual multi-layer stochastic block models.
  with D. Huang and Z. Li.
  arXiv.


• Detecting correlation efficiently in stochastic block models: breaking Otter's threshold in the entire supercritical regime.
  with G. Chen, J. Ding and Z. Li.
  arXiv.


• Finding a dense submatrix of a random matrix. Sharp bounds for online algorithms.
  with S. Bhamidi and D. Gamarnik.
  Electronic Communications in Probability.
  arXiv, journal.


• Detection and reconstruction of a random hypergraph from noisy graph projection.
  with Z. Li and Q. Xu.
  Conference version to appear at ISIT 2026 .
  arXiv.


• Asymptotic diameter of preferential attachment model.
  with H. Du, Z. Li and H. Zhu.
  Electronic Communications in Probability.
  arXiv, journal.


• Detecting correlation efficiently in very supercritical stochastic block models: breaking the Otter's threshold barrier.
  with G. Chen, J. Ding and Z. Li.
  SODA 2026.
  conference, full version.


• A proof of the changepoint detection threshold conjecture in preferential attachment models.
  with H. Du and J. Xu.
  COLT 2025.
  Best Poster Award at The 2026 Joint Workshop of RMTA & SNAB.
  Journal version to appear at Annals of Applied Probability.
  arXiv, video by Jiaming Xu, conference, poster.


• A computational transition for detecting correlated stochastic block models by low-degree polynomials.
  with G. Chen, J. Ding and Z. Li.
  Annals of Statistics.
  arXiv, journal.


• The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regime.
  with Z. Li.
  arXiv.


• The algorithmic phase transition of random graph alignment problem.
  with H. Du and R. Huang.
  Probability Theory and Related Fields.
  arXiv, journal.


• A polynomial-time approximation scheme for the maximal overlap of two independent Erdős–Rényi graphs.
  with J. Ding and H. Du.
  Random Structures and Algorithms.
  arXiv, journal.

Talks

• Stable algorithms cannot reliably find isolated perceptron solutions. (May 15, 2026, Shanghai Center for Mathematical Sciences, Fudan University), Shanghai, China.
  
  
• Phase transitions in statistical models: several examples. (October 9, 2025, Shandong University), Jinan, China.
  
  
• A proof of the changepoint detection threshold conjecture in preferential attachment models. (July 3, 2025, COLT), Lyon, France.
  
  
• A proof of the changepoint detection threshold conjecture in preferential attachment models. (June 3, 2025, An International Conference on Applied Probability), Beijing, China.
  
  
• Asymptotic diameter of preferential attachment model. (joint with Zhangsong Li, May 29, 2025, YMSC probability seminar, Tsinghua University), Beijing, China.
  
  
• Combinatorial Statistics: recent progress on random graph alignment and changepoint detection. (March 26, 2025, Shandong University), Jinan, China.
  
  
• Matching Wishart matrices via Umeyama algorithm. (Sept.9 2024, Peking University), Beijing, China.
  
  
• Optimizing the overlap of two independent Erdös-Rényi graphs. (Jan.15 2024, probability seminar, Sichuan University), Chengdu, China.
  
  
• Algorithms and phase transitions in random graph alignment problem. (Sept.11 2023, Peking University), Beijing, China.
  
  
• On cluster expansion and its applications into Ising model--spontaneous magnetization and exponential decay of truncated two-point function in sufficiently Low temperature regime. (Apr. 22, 2023, Peking University), Beijing, China.
  
  
• A polynomial-time approximation scheme for the maximal overlap of two independent Erdös-Rényi graphs. (Zhongtai Securities Institute for Financial Studies, Shandong University. Nov. 7th, 2022), online.
  
  
• An introduction to first-passage percolation. (Zhongtai Securities Institute for Financial Studies, Shandong University. October, 2020), Jinan, China
  
  

Teaching

Calculus C(Fall 2021), Stochastic Processes(Spring and Fall 2022), Measure Theory(Spring 2023), Advanced Probability Theory(Fall 2023), Stochastic Processes and Statistical Physics(Spring 2024), Calculus(B)(Spring 2025), notes