Special Issue on Random Matrix: Theory and Applications

Statistical Learning and Data Science (SLADS) is a newly launched journal sponsored by the Chinese Academy of Sciences, dedicated to publishing high-quality research across statistics, machine learning, artificial intelligence, and data science. SLADS emphasizes both rapid publication and rigorous peer review, using the OpenReview system to ensure transparency and quality. The editorial goal is to reach an Accept/Reject decision within 3.5 months of submission while maintaining high scholarly standards.

Aims and Scope

Random Matrix Theory (RMT) has evolved from a specialized tool in nuclear physics into a cornerstone of modern mathematics and a ubiquitous framework for understanding complex systems. Today, RMT provides the essential language for analyzing high-dimensional data, chaotic quantum systems, wireless communications, and even deep learning.

This special volume aims to bring together leading researchers from mathematics, physics, statistics, and engineering to present state-of-the-art theoretical developments and their transformative applications. We invite submissions that bridge the gap between rigorous mathematical theory and practical implementation in diverse scientific fields.

Submission Deadline: December 31, 2026

• Early submissions will be reviewed and published online ahead of the final issue.

Topics of Interest

We welcome both original research articles and comprehensive review papers. Topics of interest include, but are not limited to:

1. Theoretical Foundations of RMT

• Universality classes, Whistler, Wishart, Circular ensembles and their limits.
• Large deviations, edge statistics, and the Tracy-Widom distribution.
• Free probability theory and non-commutative probability.
• Sparse random matrices, structured matrices (Toeplitz, Hankel), and heavy-tailed distributions.
• Eigenvector localization and delocalization transitions.

2. Applications in Data Science & Statistics

• High-dimensional inference: Covariance matrix estimation, PCA in high-dimensions, and hypothesis testing.
• Machine Learning: High-dimensional generalization (ERM, kernel methods, double descent), loss landscapes, and Hessian spectral analysis.
• Signal Processing: Detection and estimation in massive MIMO systems, array processing, and radar.
• Network Science: Spectral properties of random graphs, community detection, and centrality measures.

3. Applications in Physics & Mathematics

• Quantum Chaos & Many-Body Physics: Spectral statistics of chaotic systems, entanglement entropy, and thermalization.
• Disordered Systems: Anderson localization, spin glasses, and transport in random media.
• Number Theory: Connections between RMT and the zeros of the Riemann Zeta function.
• String Theory & 2D Gravity: Matrix models in high-energy physics.

4. Interdisciplinary Applications

• Quantitative Finance: Cleaning correlation matrices, portfolio optimization, and market microstructure.
• Ecology & Biology: Stability of complex ecosystems (May's stability criteria) and gene regulatory networks.
• Neuroscience: Modeling neural connectivity and brain dynamics.

Guest Editors

• Weiming Li, Shanghai University of Finance and Economics
• Cosme Louart, The Chinese University of Hong Kong, Shenzhen
• Yuanyuan Xu, Chinese Academy of Sciences
• Jianfeng Yao, The Chinese University of Hong Kong, Shenzhen
• Lun Zhang, Fudan University

Submission Information

Manuscripts should be submitted through the SLADS website at http://slads.scichina.com.

Contact: Ruiyan Zhang, zhangry@scichina.com