MS-18 Random matrix theory for networks (Random Matrix Theory)

Dustin Mixon (Air Force Institute of Technology) and Rachel Ward (University of Texas at Austin)

Increasingly, methods of high-dimensional probability and heuristics from statistical physics are being used in harmony with techniques from manifold optimization to derive statistical guarantees for scalable algorithms in the analysis of big data with latent manifold structure. Applications include dimension reduction, network analysis, neural networks, phase retrieval, computer vision, and sparse signal recovery. This mini-symposium aims to bring together mathematicians and engineers from academia and industry at the forefront of this effort, albeit from various perspectives, in hopes that new collaborations and insights will result.


Sam Cole, A simple algorithm for spectral clustering of random graphs

Tingran Gao, Manifold Learning on Fibre Bundles

Thang Huynh, Phase Retrieval with Noise and Outliers

Hamidreza Hakim Javadi, Non-negative Matrix Factorization Revisited

Shuyang Ling, Fast joint blind deconvolution and demixing via nonconvex optimization

Amelia Perry, Message passing algorithms for synchronization problems

Soledad Villar, Clustering subgaussian mixtures by semidefinite programming