Summary
The main focus of this project is to identify predictable behavior in large random structures using tools from probability theory, combinatorics, analysis, and theoretical computer science. Specific examples of models considered consist of those used to model noisy data sets, gases, and other types of spatial data. Mathematically, the three classes of models that are the primary focus of the project are random matrices, random polynomials, and Gibbs point processes. The project includes collaboration with graduate students on core problems concerning these three classes of models and mentor
