Seminars and Colloquia by Series

Grid Ramsey problem and related questions

Series
Job Candidate Talk
Time
Thursday, December 4, 2014 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Choongbum Lee – MIT
The Hales--Jewett theorem is one of the pillars of Ramsey theory, from which many other results follow. A celebrated result of Shelah from 1988 gives a significantly improved bound for this theorem. A key tool used in his proof, now known as the cube lemma, has become famous in its own right. Hoping to further improve Shelah's result, more than twenty years ago, Graham, Rothschild and Spencer asked whether there exists a polynoimal bound for this lemma. In this talk, we present the answer to their question and discuss numerous connections of the cube lemma with other problems in Ramsey theory. Joint work with David Conlon (Oxford), Jacob Fox (MIT), and Benny Sudakov (ETH Zurich).

Geometric homogeneity in disordered spatial processes

Series
Job Candidate Talk
Time
Tuesday, December 2, 2014 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Eviatar Procaccia – University of California, Los Angeles
Experimentalists observed that microscopically disordered systems exhibit homogeneous geometry on a macroscopic scale. In the last decades elegant tools were created to mathematically assert such phenomenon. The classical geometric results, such as asymptotic graph distance and isoperimetry of large sets, are restricted to i.i.d. Bernoulli percolation. There are many interesting models in statistical physics and probability theory, that exhibit long range correlation. In this talk I will survey the theory, and discuss a new result proving, for a general class of correlated percolation models, that a random walk on almost every configuration, scales diffusively to Brownian motion with non-degenerate diffusion matrix. As a corollary we obtain new results for the Gaussian free field, Random Interlacements and the vacant set of Random Interlacements. In the heart of the proof is a new isoperimetry result for correlated models.

Quantum Entanglement Rates

Series
Job Candidate Talk
Time
Tuesday, November 25, 2014 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Dr Anna Vershynina – Institute for quantum information, RWTH University Aachen, Germany –

Please Note: Anna Vershynina is a job candidate. She is a Mathematical Physicist working on the rigorous mathematical theory of N-body problem and its relation with quantum information.

Entanglement is one of the crucial phenomena in quantum theory. The existence of entanglement between two parties allows for notorious protocols, like quantum teleportation and super dense coding. Finding a running time for many quantum algorithms depends on how fast a system can generate entanglement. This raises the following question: given some Hamiltonian and dissipative interactions between two or more subsystems, what is the maximal rate at which an ancilla-assisted entanglement can be generated in time. I will review a recent progress on bounding the entangling rate in a closed bipartite system. Then I will generalize the problem first to open system and then to a higher multipartite system, presenting the most recent results in both cases.

On the geometry of log concave measures

Series
Job Candidate Talk
Time
Tuesday, November 18, 2014 - 12:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Galyna Livshyts – Kent State University
The perimeter of a convex set in R^n with respect to a given measure is the measure's density averaged against the surface measure of the set. It was proved by Ball in 1993 that the perimeter of a convex set in R^n with respect to the standard Gaussian measure is asymptotically bounded from above by n^{1/4}. Nazarov in 2003 showed the sharpness of this bound. We are going to discuss the question of maximizing the perimeter of a convex set in R^n with respect to any log-concave rotation invariant probability measure. The latter asymptotic maximum is expressed in terms of the measure's natural parameters: the expectation and the variance of the absolute value of the random vector distributed with respect to the measure. We are also going to discuss some related questions on the geometry and isoperimetric properties of log-concave measures.

Combining Riesz bases

Series
Job Candidate Talk
Time
Thursday, November 13, 2014 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Shahaf Nitzan – Kent State University
Orthonormal bases (ONB) are used throughout mathematics and its applications. However, in many settings such bases are not easy to come by. For example, it is known that even the union of as few as two intervals may not admit an ONB of exponentials. In cases where there is no ONB, the next best option is a Riesz basis (i.e. the image of an ONB under a bounded invertible operator). In this talk I will discuss the following question: Does every finite union of rectangles in R^d, with edges parallel to the axes, admit a Riesz basis of exponentials? In particular, does every finite union of intervals in R admit such a basis? (This is joint work with Gady Kozma).

A topological approach to investigating the structure of neural activity

Series
Job Candidate Talk
Time
Thursday, February 13, 2014 - 15:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Vladimir Itskov – U. of Nebraska
Experimental neuroscience is achieving rapid progress in the ability to collect neural activity and connectivity data. This holds promise to directly test many theoretical ideas, and thus advance our understanding of "how the brain works." How to interpret this data, and what exactly it can tell us about the structure of neural circuits, is still not well-understood. A major obstacle is that these data often measure quantities that are related to more "fundamental" variables by an unknown nonlinear transformation. We find that combinatorial topology can be used to obtain meaningful answers to questions about the structure of neural activity. In this talk I will first introduce a new method, using tools from computational topology, for detecting structure in correlation matrices that is obscured by an unknown nonlinear transformation. I will illustrate its use by testing the "coding space" hypothesis on neural data. In the second part of my talk I will attempt to answer a simple question: given a complete set of binary response patterns of a network, can we rule out that the network functions as a collection of disconnected discriminators (perceptrons)? Mathematically this translates into questions about the combinatorics of hyperplane arrangements and convex sets.

A geometric approach to understanding neural codes in recurrent networks

Series
Job Candidate Talk
Time
Tuesday, February 4, 2014 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Carina Curto – University of Nebraska-Lincoln
Synapses in many cortical areas of the brain are dominated by local, recurrent connections. It has long been suggested, therefore, that cortical networks may serve to restore a noisy or incomplete signal by evolving it towards a stored pattern of activity. These "preferred" activity patterns are constrained by the excitatory connections, and comprise the neural code of the recurrent network. In this talk I will briefly review the permitted and forbidden sets model for cortical networks, first introduced by Hahnloser et. al. (Nature, 2000), in which preferred activity patterns are modeled as "permitted sets" - that is, as subsets of neurons that co-fire at stable fixed points of the network dynamics. I will then present some recent results that provide a geometric handle on the relationship between permitted sets and network connectivity. This allows us to precisely characterize the structure of neural codes that arise from a simple learning rule. In particular, we find "natural codes" that can be learned from few examples, and that closely mimic receptive field codes that have been observed in the brain. Finally, we use our geometric description of permitted sets to prove that these networks can perform error correction and pattern completion for a wide range of connectivities.

Dynamics of ferromagnets: averaging methods, bifurcation diagrams, and thermal noise effects

Series
Job Candidate Talk
Time
Friday, January 31, 2014 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Katherine Newhall – Courant Institute
Driving nanomagnets by spin-polarized currents offers exciting prospects in magnetoelectronics, but the response of the magnet to such currents remains poorly understood. For a single domain ferromagnet, I will show that an averaged equation describing the diffusion of energy on a graph captures the low-damping dynamics of these systems. In particular, I compute the mean times of thermally assisted magnetization reversals in the finite temperature system, giving explicit expressions for the effective energy barriers conjectured to exist. I will then outline the problem of extending the analysis to spatially non-uniform magnets, leading to a transition state theory for infinite dimensional Hamiltonian systems.

Extremal Eigenvalue Problems in Optics, Geometry, and Data Analysis

Series
Job Candidate Talk
Time
Tuesday, January 28, 2014 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Braxton Osting – UCLA, Math
Since Lord Rayleigh conjectured that the disk should minimize the first eigenvalue of the Laplace-Dirichlet operator among all shapes of equal area more than a century ago, extremal eigenvalue problems have been an active research topic. In this talk, I'll demonstrate how extremal eigenvalue problems arise in a variety of contexts, including optics, geometry, and data analysis, and present some recent analytical and computational results in these areas. One of the results I'll discuss is a new graph partitioning method where the optimality criterion is given by the sum of the Dirichlet energies of the partition components. With intuition gained from an analogous continuous problem, we introduce a rearrangement algorithm, which we show to converge in a finite number of iterations to a local minimum of a relaxed objective function. The method compares well to state-of-the-art approaches when applied to clustering problems on graphs constructed from synthetic data, MNIST handwritten digits, and manifold discretizations.

Towards the control of multiscale stochastic systems

Series
Job Candidate Talk
Time
Thursday, January 23, 2014 - 11:05 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Molei Tao – Courant Institute, NYU
Motivated by rich applications in science and engineering, I am interested in controlling systems that are characterized by multiple scales, geometric structures, and randomness. This talk will focus on my first two steps towards this goal. The first step is to be able to simulate these systems. We developed integrators that do not resolve fast scales in these systems but still capture their effective contributions. These integrators require no identification of underlying slow variables or processes, and therefore work for a broad spectrum of systems (including stiff ODEs, SDEs and PDEs). They also numerically preserve intrinsic geometric structures (e.g., symplecticity, invariant distribution, and other conservation laws), and this leads to improved long time accuracy. The second step is to understand what noises can do and utilize them. We quantify noise-induced transitions by optimizing probabilities given by Freidlin-Wentzell large deviation theory. In gradient systems, transitions between metastable states were known to cross saddle points. We investigate nongradient systems, and show transitions may instead cross unstable periodic orbits. Numerical tools for identifying periodic orbits and for computing transition paths are proposed. I will also describe how these results help design control strategies.

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