I work on statistical modeling, machine learning, and network science. My research explores the intersection of structure, data, and algorithms, with a focus on both theoretical understanding and real-world applications. My full list of publications can be found on 🔗 Google Scholar

📌 Current Research Directions

• Network Models
  Directed and undirected models with reciprocity, transitivity, and latent structure.

• High-Dimensional Data
  Theory and methodology for sparse estimation, regularization, and scalable computation in high-dimensional settings.

• Correlated Data
  Statistical inference for dependent and structured data, including longitudinal, spatial, and time series contexts.

• Human-Centred Foundation Models
  Statistical perspectives for safe, interpretable, and collaborative AI systems.

✨ Recent Highlights

• Feng, R. and Leng, C. (2025). Modelling Directed Networks with Reciprocity. Biometrika, to appear.

• Jiang, B., Leng, C., Yan, T., Yao, Q., and Yu, X. (2025). A Two-Way Heterogeneity Model for Dynamic Networks. Annals of Statistics, to appear.

• Fan, X., Li, B., Leng, C., and Wu, W. (2025). Learning Changes in Graphon Attachment Network Models. ICML.

• Fan, X., Ma, F., Leng, C., and Wu, W. (2025). Low-Rank Graphon Learning for Networks. NeurIPS.