Georgia Institute of Technology College of Sciences

In 1665, Huygens discovered that, when two pendulum clocks hanged from a same wooden beam supported by two chairs, they synchronize in anti-phase mode. On the other hand, metronomes synchronize in-phase when oscillating on top of the same movable surface. In this talk, I will describe and analyze a model to help understand the conditions that lead to anti-phase synchronization vs. the conditions that lead to in-phase synchronization.

There has been recent interest in sparse bounds for various operators that arise in harmonic analysis. Perhaps the most basic "sparse" result is a pointwise bound for the dyadic Hardy-Littlewood maximal function. It turns out that the direct analogue of this result does not hold if one adds an extra dilation parameter: the dyadic strong maximal function does not admit a pointwise sparse bound or a sparse bound involving L^1 forms (both of which hold in the one-parameter setting). The proof is based on the construction of a certain pair of extremal point sets. This is joint work with Jose Conde-Alonso, Yumeng Ou, and Guillermo Rey.

In this series of (3-5) lectures, I will talk about different aspects of a class of contact 3-manifolds for which geometry, dynamics and topology interact subtly and beautifully. The talks are intended to include short surveys on "compatibility", "Anosovity" and "Conley-Zehnder indices". The goal is to use the theory of Contact Dynamics to show that conformally Anosov contact 3-manifolds (in particular, contact 3-manifolds with negative $\alpha$-sectional curvature) are universally tight, irrducible and do not admit a Liouville cobordism to tight 3-sphere.

For each n, let M be an n by n random matrix with independent ±1 entries. We show that the probability that M is not invertable equals (1/2 + o(1/n))^n, which settles an old problem. Some generalizations are considered.

The fluctuations of ergodic sums by the means of global and local specifications on periodic points will be discussed. Results include a Lindeberg-type central limit theorems in both setups of specification. As an application, it is shown that averaging over randomly chosen periodic orbits converges to the integral with respect to the measure of maximal entropy as the period approaches infinity. The results also suggest to decompose the variances of ergodic sums according to global and local sources.

A wide variety of applied tasks, such as ranking, clustering, graph matching and network reconstruction, can be formulated as a matrix estimation problem where the rows and columns of the matrix are shuffled by a latent permutation. The combinatorial nature of the unknown permutation and the non-convexity of the parameter space result in both statistical and algorithmic challenges. I will present recent developments of average-case models and efficient algorithms, primarily for the problems of ranking from comparisons and statistical seriation. On the statistical side, imposing shape constraints on the underlying matrix extends traditional parametric approaches, allowing for more robust and adaptive estimation. On the algorithmic front, I discuss efficient local algorithms with provable guarantees, one of which tightens a conjectured statistical-computational gap for a stochastically transitive ranking model.

Since Erdős–Rényi introduced random graphs in 1959, two closely related models for random graphs have been extensively studied. In the G(n,m) model, a graph is chosen uniformly at random from the collection of all graphs that have n vertices and m edges. In the G(n,p) model, a graph is constructed by connecting each pair of two vertices randomly. Each edge is included in the graph G(n,p) with probability p independently of all other edges. Researchers have studied when the random graph G(n,m) (or G(n,p), resp.) satisfies certain properties in terms of n and m (or n and p, resp.). If G(n,m) (or G(n,p), resp.) satisfies a property with probability close to 1, then one may say that a `typical graph’ with m edges (or expected edge density p, resp.) on n vertices has the property. Random graphs and their variants are also widely used to prove the existence of graphs with certain properties. In this talk, two problems for these categories will be discussed. First, a new approach will be introduced for the problem of the emergence of a giant component of G(n,p), which was first considered by Erdős–Rényi in 1960. Second, a variant of the graph process G(n,1), G(n,2), …, G(n,m), … will be considered to find a tight lower bound for Ramsey number R(3,t) up to a constant factor. (No prior knowledge of graph theory is needed in this talk.)

Stein's method is a powerful technique to quantify proximity between probability measures, which has been mainly developed in the Gaussian and the Poisson settings. It is based on a covariance representation which completely characterizes the target probability measure. In this talk, I will present some recent unifying results regarding Stein's method for infinitely divisible laws with finite first moment. In particular, I will present new quantitative results regarding Compound Poisson approximation of infinitely divisible laws, approximation of self-decomposable distributions by sums of independent summands and stability results for self-decomposable laws which satisfy a second moment assumption together with an appropriate Poincaré inequality. This is based on joint works with Christian Houdré.

It is anticipated that chaotic regimes (characterized by, e.g., sensitivity with respect to initial conditions and loss of memory) arise in a wide variety of dynamical systems, including those arising from the study of ensembles of gas particles and fluid mechanics. However, in most cases the problem of rigorously verifying asymptotic chaotic regimes is notoriously difficult. For volume-preserving systems (e.g., incompressible fluid flow or Hamiltonian systems), these issues are exemplified by coexistence phenomena: even in quite simple models which should be chaotic, e.g. the Chirikov standard map, completely opposite dynamical regimes (elliptic islands vs. hyperbolic sets) can be tangled together in phase space in a convoluted way.

Recent developments have indicated, however, that verifying chaos is tractable for systems subjected to a small amount of noise— from the perspective of modeling, this is not so unnatural, as the real world is inherently noisy. In this talk, I will discuss two recent results: (1) a large positive Lyapunov exponent for (extremely small) random perturbations of the Chirikov standard map, and (2) a positive Lyapunov exponent for the Lagrangian flow corresponding to various incompressible stochastic fluids models, including stochastic 2D Navier-Stokes and 3D hyperviscous Navier-Stokes on the periodic box. The work in this talk is joint with Jacob Bedrossian, Samuel Punshon-Smith, Jinxin Xue and Lai-Sang Young.

We present a new algorithm for sampling contingency tables with fixed margins. This algorithm runs in polynomial time for certain broad classes of sparse tables. We compare the performance of our algorithm theoretically and experimentally to existing methods, including the Diaconis-Gangolli Markov chain and sequential importance sampling. Joint work with Igor Pak.