High Dimensional Inference and Random Matrices

Random matrix theory lies at the confluence of several areas of mathematics, especially number theory, combinatorics, dynamical systems, diffusion processes, probability and statistics. At the same time, random matrix theory may hold the key to solving critical problems for a broad range of complex systems from biophysics to quantum chaos to signals and communication theory to machine learning to finance to geophysical modeling. This Program was a unique opportunity to explore the interplay of stochastic and mathematical aspects of random matrix theory and its applications.

The aim of the program was to bring together researchers interested in the theory and applications of random matrices to share their results, discuss new research directions and develop collaborations. The program concentrated on large-dimensional random matrices and the problems that make use of them. In particular, emphasis was on how developments in random matrix theory might impact statistical inference in high dimensional systems.

Working Groups:

    1. Climate and Weather
    2. Wireless Communications
    3. Universality
    4. Regularization and Covariance
    5. Geometric Methods
    6. Multivariate Distributions
    7. Graphical Models/Bayesian Methods
    8. Estimating functionals of a high dimensional sparse vector of means
    9. Statistical Mechanics of Granular Flow

** To see more in depth information on this program, see the report HERE **