Past Probability Seminars Spring 2020

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Revision as of 14:56, 23 February 2011 by Valko (talk | contribs) (Wednesday, March 2, 3:30PM, VV B115 Alan Hammond (Oxford))
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Spring 2011

Thursdays in 901 Van Vleck Hall at 2:25 PM, unless otherwise noted. If you would like to receive announcements about upcoming seminars, please visit this page to sign up for the email list.

Past Seminars

Monday, January 24, 2:25PM, B129 Sunil Chhita (Brown University)


Title: Particle Systems arising from an Anti-ferromagnetic Ising Model
Abstract: We present a low temperature anisotropic anti-ferromagnetic 2D Ising model through the guise of a certain dimer model. This model also has a bijection with a one-dimensional particle system equipped with creation and annihilation. We can find the exact phase diagram, which determines two significant values (the independent and critical value). We also highlight some of the behavior of the model in the scaling window at criticality and at independence.

Thursday, February 10, Alex Bloemendal (Toronto)

Title: Finite rank perturbations of large random matrices

Abstract: Finite (or fixed) rank perturbations of large random matrices arise in a number of applications. The main phenomenon is a phase transition in the largest eigenvalues as a function of the strength of the perturbation. I will describe joint work with Bálint Virág in which we introduce a new way to study these models. The starting point is a reduction to a natural band form; under the soft edge scaling, it converges to a souped-up version of the known continuum random Schrödinger operator on the half-line. We describe the near-critical fluctuations in several ways, solving a known open problem in the real case. One characterization also yields a new route to the Painlevé structure in the celebrated Tracy-Widom laws.

Thursday, February 24, Márton Balázs (Technical University Budapest)

Title: Modelling flocks and prices: jumping particles with an attractive interaction
Abstract: I will introduce a model of a finite number of competing particles on R. Real-life phenomena that could be modeled this way includes the evolution of stocks in a market, or herding behavior of animals. Given a particle configuration, the center of mass of the particles is computed by simply averaging the particle locations. The evolution is a continuous time Markov jump process: given a configuration and thus the center of mass, each particle jumps with a rate that depends on the particle's relative position compared to the center of mass. Those left behind have a higher jump rate than those in front of the center of mass. When a jump of a particle occurs, the jump length is chosen independently of everything from a positive distribution. Hence we see that the dynamics tries to keep the particles together.
The main point of interest is the behavior of the model as the number of particles goes to infinity. We first heuristically wrote up a differential equation on the evolution of particle density. I will explain the heuristics, and show traveling wave solutions in a few cases. I will also present a surprising connection to extreme value statistics. Then I will briefly sketch a hydrodynamic argument which proves that the evolution of the system indeed converges to that governed by the differential equation.
(Joint work with Miklós Rácz and Bálint Tóth)

Wednesday, March 2, 3:30PM, VV B115 Alan Hammond (Oxford)


Title: The sharpness of the phase transition for speed for biased walk in

supercritical percolation

Abstract: I will discuss a joint work with Alex Fribergh in which we study the biased random walk on the infinite cluster of supercritical percolation. Fixing any $d \geq 2$ and supercritical parameter $p >p_c$, the model has a parameter $\lambda > 0$ for the degree of bias of the walker in a certain preferred direction (which is another parameter, in $S^{d-1}$). We prove that the model has a sharp phase transition, that is, that there exists a critical value $\lambda_c > 0$ of the bias such that the walk moves at positive speed if $lambda < \lambda_c$ and at zero speed if $\lambda > \lambda_c$. This means that a stronger preference for the walker to move in a given direction actually causes the walk to slow down. The reason for this effect is a trapping phenomenon, and, as I will explain, our result is intimately tied to understanding the random geometry of the local environment that is trapping the particle at late time in the case when motion is sub-ballistic.

Thursday, March 31, Stefan Grosskinsky (Warwick)

Title: TBA
Abstract: TBA

Wednesday, April 6, Richard Sowers (University of Illinois at Urbana-Champaign)


Title: TBA
Abstract: TBA

Thursday, April 14, Janos Englander (University of Colorado - Boulder)

Title: TBA
Abstract: TBA

Thursday, April 28, John Fricks (Penn State)

Title: TBA
Abstract: TBA

Thursday, May 5, Soumik Pal (University of Washington)

Title: TBA
Abstract: TBA