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Texas A&M University
Mathematics

Probability Seminar

Fall 2019

 

Date:September 6, 2019
Time:11:30am
Location:BLOC 628
Speaker:Boris Hanin, TAMU
Title:Products of Many Large Random Matrices
Abstract:I will present some recent joint work with Mihai Nica (Toronto) and ongoing work with Grigoris Paouris (Texas A&M) about products of N independent matrices of size n x n in the regime where both the size n and the number of terms N tends to infinity. I will discuss in particular the work in progress with Paouris that gives finite size corrections to the multiplicative ergodic theorem regime in which the N is much larger than n.

Date:September 20, 2019
Time:11:30am
Location:BLOC 628
Speaker:Jiayan Ye (Prelim exam), TAMU
Title:On Stationary DLA.
Abstract:Diffusion limited aggregation (DLA) in $\mathbb{Z}^2$ is a stochastic process first defined by Witten and Sander in order to study aggregation systems governed by diffusive laws. In this talk, I will construct an infinite stationary DLA on the upper half planar lattice. The stationary DLA is ergodic with respect to integer left-right translations. This is a joint work with Eviatar Procaccia and Yuan Zhang.

Date:September 27, 2019
Time:11:30am
Location:BLOC 628
Speaker:Patricia Alonso Ruiz, TAMU
Title:Order statistics in stochastic geometry
Abstract:This talk will discuss and give an overview of questions concerning the asymptotic distribution of order statistics for functionals of spatial Poisson point processes. We will highlight some techniques from stochastic geometry applied in this setting.

Date:October 4, 2019
Time:11:30am
Location:BLOC 628
Speaker:Axel Sáenz, Tulane
Title:Stationary dynamics in finite time for the totally asymmetric simple exclusion process
Abstract:The totally asymmetric simple exclusion process (TASEP) is a Markov process that is the prototypical model for transport phenomena in non-equilibrium statistical mechanics. It was first introduced by Spitzer in 1970, and in the last 20 years, it has gained a strong resurgence in the emerging field of "Integrable Probability" due to asymptotic results from Johansson in 2000 and Tracy and Widom in 2007 (among other related results). In particular, these formulas led to great insights regarding fluctuations related to the Tracy-Widom distribution and scalings to the Kardar-Parisi-Zhang (KPZ) stochastic differential equation. In this joint work with Leonid Petrov (University of Virginia), we introduce a new and simple Markov process that maps the distribution of the TASEP at time t>0 , given step initial time data, to the distribution of the TASEP at some earlier time t−s>0. This process "back in time" is closely related to the Hammersley process introduced by Hammersley in 1972, which later found a resurgence in the longest increasing subsequence problem in the work of Aldous and Diaconis in 1995. Hence, we call our process the backwards Hammersley-type process (BHP). As a fun application of our results, we have a new proof of the limit shape for the TASEP. The central objects in our constructions and proofs are the Schur point processes and the Yang-Baxter equation for the sl_2 quantum affine Lie algebra. In this talk, we will discuss the background in more detail and will explain the main ideas behind the constructions and proof.

Date:October 18, 2019
Time:11:30am
Location:BLOC 628
Speaker: Louis-Pierre Arguin, CUNY
Title:Large Values of the Riemann Zeta Function in Short Intervals
Abstract:In a seminal paper in 2012, Fyodorov & Keating proposed a series of conjectures describing the statistics of large values of zeta in short intervals of the critical line. In particular, they relate these statistics to the ones of log-correlated Gaussian fields. In this lecture, I will present recent results that answer many aspects of these conjectures. Connections to problems in number theory will also be discussed.

Date:October 25, 2019
Time:11:30am
Location:BLOC 628
Speaker: Hanbaek Lyu, UCLA
Title:TBA
Abstract:TBA

Date:November 1, 2019
Time:11:30am
Location:BLOC 628
Speaker:Quan Zhou, TAMU (Statistics)
Title:Optimal detection of a drifting Brownian coordinate
Abstract:Given a stochastic process X_t, how to find a stopping time \tau that maximizes the expectation of some reward function, say G(X_\tau), is known as an optimal stopping problem. Two famous examples are the secretary problem and the pricing of American options. Interestingly, continuous-time optimal stopping problems can often be converted to free-boundary PDE problems. The primary goal of this talk is to introduce the theory of optimal stopping using a class of problems which we refer to as “optimal detection of a drifting Brownian coordinate”: Imagine N independent Brownian motions. One of them has a nonzero drift while all the others are just standard Brownian motions. The question is how to find out which one is drifting as soon as possible. This problem can be formulated in many ways. In this talk we will focus on one particular formulation as an optimal stopping problem and solve the corresponding free boundary problem. Some other formulations will be briefly discussed as well. We will also mention applications of the optimal stopping theory (and more generally stochastic optimization) in statistics.