Georgia Institute of Technology College of Sciences

Front propagation in fluid flows arise in power generation of automobile engines, forest fire spreading, and material interfaces of solidification to name a few. In this talk, we introduce the level set formulation and the resulting Hamilton-Jacobi equation, known as G-equation in turbulent combustion. When the fluid flow has enough intensity, G-equation becomes non-coercive and non-linearity no longer dominates. When front curvature and flow stretching effects are included, the extended G-equation is also non-convex. We discuss recent progress in analysis and computation of homogenization and large time front speeds in cellular flows (two dimensional Hamiltonian flows) from both Lagrangian and Eulerian perspectives, and the recovery of experimental observations from the G-equations.

The classical Weyl equidistribution theorem says that if v is a non-resonant vector then the sequence v, 2v, 3v... is uniformly distributed on a torus. In this talk we discuss the rate of convergence to the uniform distribution. This is a joint work with Bassam Fayad.

Random matrix theory is a fast developing topic with connections to so many areas of mathematics: probability, number theory, combinatorics, data analysis, mathematical physics, to mention a few. The determinant is one of the most studied matrix functionals. In our talk, we are going to give a brief survey on the studies of this functional, dated back to Turan in the 1940s. The main focus will be on recent developments that establish the limiting law in various models.

Populations, whether they be viral particles, bio-chemicals, plants or animals, are subject to intrinsic and extrinsic sources of stochasticity. This stochasticity in conjunction with nonlinear interactions between individuals determines to what extinct populations are able to persist in the long-term. Understanding the precise nature of these interactive effects is a central issue in population biology from theoretical, empirical, and applied perspectives. For the first part of this talk, I will discuss, briefly, the relationship between attractors of deterministic models and quasi-stationary distributions of their stochastic, finite population counterpoints i.e. models accounting for demographic stochasticity. These results shed some insight into when persistence should be observed over long time frames despite extinction being inevitable. For the second part of the talk, I will discuss results on stochastic persistence and boundedness for stochastic models accounting for environmental (but not demographic) noise. Stochastic boundedness asserts that asymptotically the population process tends to remain in compact sets. In contrast, stochastic persistence requires that the population process tends to be "repelled" by some "extinction set." Using these results, I will illustrate how environmental noise can facilitate coexistence of competing species and how dispersal in stochastic environments can rescue locally extinction prone populations. Empirical work on Kansas prairies, acorn woodpecker populations, and microcosm experiments demonstrating these phenomena will be discussed.