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Texas A&M University
Mathematics

Events for 04/23/2014 from all calendars

Noncommutative Geometry Seminar

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Time: 2:00PM - 2:50PM

Location: Blocker 627

Speaker: Rufus Willett, University of Hawai'i

Title: Weak coarse embeddings for random graphs

Abstract: A generic sequence of graphs is an expander, so does not admit a coarse embedding into a Hilbert space (or many other ‘geometrically nice’ Banach spaces). Expanders are also expected to have bad K-theoretic properties. I’ll use work of Mendel and Naor to show that a generic sequence of graphs (in some reasonable sense) admits a weak form of coarse embedding into Hilbert space. I’ll also discuss some K-theoretic consequences of this, and connections to expansion and geometric forms of property (T).


AMUSE

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Time: 6:00PM - 7:00PM

Location: BLOC 164

Speaker: Math 442 Students, Texas A&M University

Title: Mathematical Modeling Project Presentations

Abstract:

Robert Bordovsky – The 3-body problem – Looking for Chaos

We consider the classical N-body problem (when N=3) and look at the general case, as well as a special case where one mass is much lighter than the others (the so-called “Circular Restricted 3 body problem”). We will look for chaotic behavior by computing the Lyapunov exponents and examine the associated Poincare maps.

Pranav Rao – Traffic Modeling

The 2014 Mathematical Contest in Modeling (MCM) Problem A can be summarized as follows: Given the "keep right except to pass rule", in which drivers on multilane freeways must keep to the rightmost lane, except when overtaking a vehicle (at which time they may move one lane to the left to overtake), create a mathematical model to analyze the performance of the rule. By drawing analogies from fluid dynamics, we can investigate the performance of this traffic rule using partial differential equations.

Sankar Sundaresan - Modeling Infectious Diseases

Disease is everywhere and has plagued human society since the beginning. It is inherent within our biological systems, yet we have survived through the worst periods. It would be very beneficial to be able to predict where serious diseases (categorized as epidemics) will appear next and when. The resultant information can be used to help inform public health interventions and to understand the transmission of infections.