Topics: Particular areas of focus and interest for the meeting include: - Application of generic chaining techniques to study the regularity of stochastic processes and lower and upper bounds on norms of random vectors and matrices - Relation between various isoperimetric and concentration inequalities as well as their applications to convex geometry, statistics and computer science - Applications of modern empirical process and strong approximation methods to problems of machine learning and inference in high- and infinite-dimensional statistical models - Interactions between information-theoretic inequalities, convex geometry and high-dimensional probability - Stein’s method and its use in high-dimensional probability - Nonasymptotic random matrix theory - Interactions between high dimensional probability and statistical physics - Super-concentration phenomena in high dimension: new tools, examples and open problems - Application of Itô calculus to convex geometry - Identification of major problems and areas of potentially high impact for applications and use in other areas of mathematics, statistics, and computer science
