Meets Wednesdays, 2--3 PM in Parker 224


Regular speakers and their current areas of interest:

Olav Kallenberg: stochastic analysis and dynamical systems, with applications to statistical mechanics and biological evolution

Ming Liao: stochastic processes in Lie groups

Erkan Nane: fractional diffusions and iterated processes

Jurek Szulga


November 20, 2013

Speaker: Dr. Jerzy Szulga

Topic: I will present the key points of the paper Lévy Flights in Evolutionary Ecology, by W.A. Woyczyński et al.

In October 22 ceremony, held in Paris, my mentor and PhD supervisor, also a coauthor of our several joint papers, Wojbor Woyczyński of Case Western Reserve University, was awarded, together with a team of French mathematicians and biologists, the 2013 Prix La Recherche in the Field of Mathematics., for the aforementioned paper, published  in  Journal of Mathematical Biology (2012) 65: 677-707

(The jury, chaired this year by  Albert Fert, the 2007 Nobel Laureate in Physics,  awards  one prize  annually in each of the 12 areas of science and technology.)

The considered  evolution process  νt meanders in the metric space of measures on a closed Euclidean domain X.  Each vector x in X represents a phenotypic “trait”.  Individual are subject to a stochastic birth and death process but upon birth a random mutation x → x+Z may occur. This mechanism turns νt into a time homogeneous Markov process.

While at the beginning νt is just a counting measure, i.e., the straight sum of Dirac’s deltas, in time it becomes convoluted. For the long time behavior it requires a renormalization with deterministic or random parameters.

There are three main issues to present.

First is the rigorous construction of the evolution process to place it in the fruitful mathematical framework.

The second is the description of its characteristics, mainly with the help of SDE’s (stochastic differential equations). The SDE’s involve Lévy processes (a.k.a. “flights”) but of pure jump type, in contrast to the well known SDE’s based on the diffusion  Lévy process such as the Brownian Motion.  So, the classical Laplacian, driving these SDE’s, gives room to fractional Laplacians.

The third issue is the limit behavior, in spirit similar to approximation (hence simulation) of Brownian Motion by Random Walk.

Although the contents of the paper (and of supporting materials) is highly technical, even cumbersome, I will try to make it accessible to general mathematical public.


November 13, 2013

Speaker: Dr. Ming Liao

Title:  Levy processes and Fourier analysis on compact Lie groups

Abstract:  A process with independent and stationary increments is called a Levy process.  A Brownian motion is a continuous Levy process, but a general Levy process has jumps, thus provides a more general model in applications.  Levy processes may be defined in groups because increments may be defined in terms of the group structure.  For Levy processes in compact Lie groups, the Fourier analysis based on Peter-Weyl Theorem provides a convenient tool for study.  When the Levy process has an L^2 distribution density, it may be expanded into a Fourier series, and this allows  us to determine how fast the Levy process converges to the uniform distribution (that is, the normalized Haar measure).  We may also obtain useful conditions under which the Levy process has an L^2, or even smooth, density.


October 30, 2013

Speaker: Dr. Erkan Nane

Title: Stochastic partial differential equations

Abstract: Stochastic partial differential equations have  applications in various disciplines that range from applied mathematics, statistical mechanics, and theoretical physics, to theoretical neuroscience, theory of complex chemical reactions, including polymer science, fluid dynamics, and mathematical finance.

Additionally, I will give a short introduction to the study of SPDEs and state some recent surprising results for time fractional SPDEs.


October 23, 2013

Speaker: Dr. Olav Kallenberg

Title: Dobrushin's theorem and beyond

Abstract: Around 1956 the applied mathematician Dobrushin, famous for his work in statistical mechanics, proved that if you take a stationary set of points (particles) in space and scramble their positions at random (e.g. by letting them perform independent Brownian motions), you end up in the limit with a Poisson process, the most random of all point configurations. (Actually his proof was flawed, and it took more than a decade before a rigorous proof was given.) I will give a modern version of the theorem (that I proved last night), indicate how it leads to some interesting problems in real analysis, and discuss some extensions and connections to certain challenging problems in stochastic geometry.


October 2, 2013

Speaker: Dr. Olav Kallenberg

Title: History of Brownian motion + proof of 3rd arcsine law
Abstract: We often hear or read comments about the history of Brownian motion. However, the speakers or writers usually get things totally wrong. My main purpose in this talk is to set the record straight. The errors usually arise from a confusion between the physical phenomenon and the mathematical object of BM. For the former, the main contributors are van Leevenhoek, Brown, Einstein, Langevin, and Ornstein-Uhlenbeck. For the latter, the main contributors are Bachelier, Wiener, L'evy, Doeblin, It^o, Kakutani, Donsker, Skorohod, Strassen, ... Historical comments were made about all of those. --- A second purpose of my talk was to outline an elementary proof of L'evy's third arcsine law for BM, usually regarded as a deep result. 

August 28, 2013
Speaker: Dr. Olav Kallenberg
Title: Stationary and invariant densities and disintegration kernels

Abstract: Let $\xi$ and $\eta$ be jointly stationary random measures on a common space $S$, subject to the measurable action of a group $G$. If $\xi\ll\eta$ a.s., we would expect the existence of a stationary, product-measurable density process (RN-derivative) $X$ on $S$, so that $\xi=X\cdot\eta$ a.s. This holds when $S$ is Borel and $G$ is a locally compact, second countable topological group. The proof of this fact is surprisingly hard (took me the whole summer) and requires ideas from real analysis, differential geometry, topological groups, and probability theory. Key ingredients include the facts that 1) the centered balls in a Riemannian manifold $M$ form a differentiation basis for any locally finite measure, and 2) any topological group $G$ as stated contains an open subgroup $G_0$ (hence with discrete coset space $G/G_0$), which is isomorphic to the projective limit of a sequence of Lie groups $G_n$.

Last updated: 11/21/2013