Columbia Probability Seminar


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Spring Semester 2010


Tuesday, January 26, 10-11am, Math 507 [note special day, time, location
]

       Clément Hongler, U. Genève
       The energy density in the 2D Ising model


Friday, January 29

       Amir Yehudayoff, IAS
       Pseudorandomness for finite groups


Friday, February 5

       Olivier Bernardi, MIT
       From random trees to random surfaces


Friday, February 12

       Xuedong He, Columbia IEOR
       Hope, Fear and Aspiration


Friday, February 19

       Kay Kirkpatrick, Courant Institute
       Bose-Einstein condensation and the nonlinear Schrodinger equation


Friday, February 26: CANCELLED


Friday, March 5

       Stilian Stoev, U. Michigan Stats
       Max-stable processes: ergodicity, classification, and some new results on their path properties


March 22-25: Minerva Lectures

       Walter Schachermayer, Vienna
       The asymptotic theory of transaction costs

School of Social Work 903
Mon Mar 22: 4-5:30pm
Tue Mar 23: 3-4:30pm
Thu Mar 25: 3-4:30pm


March 25- April 2: Minerva Lectures

       Geoffrey Grimmett, Cambridge
       Lattice models in probability

Thu Mar 25: 9:20-10:50 am, Math 203 
Fri Mar 26: 9:20-10:50am, Math 203
Mon Mar 29: 3-4:30 pm, Math 520 (note time, location)  
Tue Mar 30: 9:20-10:50am, Math 203
Thu Apr 1: 9:20-10:50am, Math 203


Friday, April 9

       William Sudderth, U. Minnesota Stats
       De Finetti coherence and logical consistency


April 12- 22: Minerva Lectures

        Ofer Zeitouni, UMN & Weizmann Institute
       Random Walks in Random Environments

Mon Apr 12
Fri Apr 16
Mon Apr 19
Wed Apr 21
Thu Apr 22

9:20-10:50 am, Math 622



Friday, April 23


       Van Vu, Rutgers
       Random matrices: Universality of the Local eigenvalues statistics

Friday, April 30

       Steven Shreve, Carnegie Mellon U.
       Diffusion matching of statistics of an Ito process







Jan 26: Clément Hongler, U. Genève
    The energy density in the 2D Ising model

We consider the critical planar Ising model from a conformal invariance point of view, using discrete complex analysis techniques. We are interested in the scaling limit of the model in a simply connected domain with pure (+ or free) boundary conditions.
More precisely, we will be interested in the behaviour of the energy density field in the scaling limit, proving an improved version of conjectures coming from Conformal Field Theory, exhibiting a nice connection with hyperbolic geometry.
The derivation is made by using a fermionic observable, which is a discrete holomorphic deformation of a partition function, and then by passing to the limit, obtaining a continuous holomorphic function, which solves a certain boundary value problem.

Joint work with Stas Smirnov.


Jan 29: Amir Yehudayoff, IAS
        Pseudorandomness for finite groups

We will discuss pseudorandomness with respect to a natural family of tests defined by finite groups. For every n and a finite group G, consider the following family of tests T_n(G): a function t from {0,1}^n to G in T_n(G) is defined by choosing g_1,...,g_n from G, and setting t(x_1,...,x_n) = g_1^{x_1} \cdots g_n^{x_n}.
 
Our goal is to construct a distribution D on {0,1}^n with as small support as possible
that epsilon-fools groups of size g. Namely, so that for every group G of size at most g, and for every test t in T_n(G), the statistical distance between t(U_n) and t(D) is at most epsilon, where U_n in the uniform distribution on {0,1}^n. In other words, no test defined by a group of size at most g can distinguish D from the uniform distribution with probability higher than epsilon.

We construct a distribution D that epsilon-fools groups of size g so that log(support(D)) is order log(n)(loglog(n) + log(1/epsilon) + log(1/g)).

Joint work with Mark Braverman, Anup Rao and Ran Raz.


Feb 5: Olivier Bernardi, MIT
       From random trees to random surfaces

Imagine gluing some polygons together by identifying edges in pairs.
If the surface obtained is (homeomorphic to) the sphere, then your
gluing is called a ``planar map''. Equivalently, a planar map can be
defined as a connected planar graph embedded in the sphere (considered
up to homeomorphism).

Maps are of interest, in particular, for defining random geometries.
Indeed, by considering all possible ways of obtaining the sphere by
gluing n squares, one obtains a ``random lattice''. By considering the
continuous limit of random lattices one obtains a ``random surface''.
Many recent advances  in understanding these random geometries are
based on bijections between planar maps and certain decorated plane
trees. Of particular importance is a bijection by Schaeffer and its
generalization by Bouttier, Di Francesco and Guitter.

In this talk, I will present a bijection  between "tree-rooted maps"
(planar map with a marked spanning tree) and pairs of plane trees.
Then, I will present the bijection of Schaeffer et al., which can be
seen as a specialization of the previous one. Lastly, (in order boost
the probabilistic content of the talk) I will briefly review some of
the known and unknown properties of random surfaces.


Feb 12: Xuedong He, Columbia IEOR
      Hope, Fear and Aspiration

In this paper, we propose a new portfolio choice model in continuous time
which features three key human incentives in choice-making: hope, fear and
aspiration. By applying recently developed quantile formulation, we solve
this model completely. Three quantitative indices: fear index, hope index
and lottery-likeness index are proposed to study the impact of hope, fear
and aspiration respectively on the investment behavior. We find that the
extreme fear would prevent the agent from risking too much and consequently
induces a portfolio insurance policy endogenously. On the other side, the
hope will drive the agent aggressive, and the more hopeful he is, the more
aggressive he will be. Finally, a high aspiration will lead to a
lottery-like terminal payoff, indicating that the agent will risk much. The
higher the aspiration is, the more risk the agent would or have to take.

This is joint work with Xunyu Zhou.


Feb 19: Kay Kirkpatrick, Courant Institute
       Bose-Einstein condensation and the nonlinear Schrodinger equation

Near absolute zero, a gas of quantum particles can condense into an
unusual state of matter, called Bose-Einstein condensation, that
behaves like a giant quantum particle. It's only recently that we've
been able to make the rigorous probabilistic connection between the
physics of the microscopic dynamics and the mathematics of the
macroscopic model, the cubic nonlinear Schrodinger equation (NLS).
I'll discuss joint work with Benjamin Schlein and Gigliola Staffilani
on two-dimensional cases for Bose-Einstein condensation--and the
periodic case is especially interesting, because of techniques from
analytic number theory and applications to quantum computing. As time
permits, I'll also mention work in progress on a high-probability
phase transition for the invariant measures of the NLS.



Mar 5: Stilian Stoev, U. Michigan Stats
       Max-stable processes: ergodicity, classification, and some new results on their path properties

We start by reviewing the spectral representations of
max-stable processes. We then discuss results on their ergodicity, weak
mixing, mixing, and classification. We conclude with some work in progress
on limit theorems in Holder spaces, which can be applied to establish the
regularity of some Brown-Resnick processes.


Apr 23: Van Vu, Rutgers
       Random matrices: Universality of the Local eigenvalues statistics

One of the main goals of the theory of random matrices is to establish the limiting distributions of the eigenvalues. In the 1950s, Wigner proved his famous semi-cirle law (subsequently extended by Anord, Pastur and others), which established the global distribution of the eigenvalues of random Hermitian matrices. In the last fifty years or so, the focus of the theory has been on the local distributions, such as the distribution of the gaps between consecutive eigenvalues, the k-point correlations, the local fluctuation of a particular eigenvalue, or the distribution of the least singular value. Many of these problems have connections to other fields of mathematics, such as combinatorics, number theory, statistics and numerical linear algebra.
Most of the local statistics can be computed explicitly for random matrices with gaussian entries (GUE or GOE), thanks to Ginibre's formulae of the joint density of eigenvalues. It has been conjectured that these results can be extended to other models of random matrices. This is generally known as the Universality phenomenon, with several specific conjectures posed by Wigner, Dyson, Mehta etc.
In this talk, we would like to discuss recent progresses concerning the Universality phenomenon, focusing on a recent result (obtained jointly with T. Tao), which asserts that all local statistics of eigenvalues of a random matrix are determined by the first four moments of the entries. This (combining with results of Johansson, Erdos-Ramirez-Schlein-Yau and many others) provides the answer to several old problems.
The method also extends to other models of random matrices, such as sample covariance matrices.



Apr 30:
Steven Shreve, Carnegie Mellon U.
        Diffusion matching of statistics of an Ito process

Suppose we are given a multi-dimensional Ito process, which can be regarded as a model for an underlying asset price together with related stochastic processes, e.g., volatility.  The drift and diffusion terms for this Ito process are permitted to be arbitrary adapted processes.  We construct a weak solution to a diffusion-type equation that matches the distribution of the Ito process at each fixed time.  Moreover, we show how to also match the distribution at each fixed time of statistics of the Ito process, including the running maximum and running average of one of the components of the process.
A consequence of this result is that a wide variety of exotic derivative securities have the same prices when written on the original Ito process or on the mimicking process.  This is joint work with Gerard Brunick.