Abstract: Processor sharing models occur in a wide variety of situations for example in models of internet bottlenecks. They are good models for bandwidth sharing as well as being solutions to NUM for logarithmic utilities.
Abstract: Consider an irreducible continuous time Markov chain with a finite or a countably infinite number of states and admitting a unique stationary probability distribution.
Abstract: We are confronted with very high dimensional data sets. As a result, methods of dealing with high dimensional data have become prominent. One geometrically motivated approach for analyzing data is called manifold learning.
Abstract: A Kakeya set is a subset of [image: F^n], where [image: F] is a finite field of [image: q] elements, that contains a line in every direction. What can we say about the size of this set? How large the size of the set must be?
Abstract: In this talk, we focus on a programmability challenge for parallel computing that can be captured by the following question: how easy or difficult is it to identify and repair bugs when developing a parallel program?
Abstract: Let f: F_2^n -> {+1, -1} be a Boolean function with the first Fourier norm A and Fourier sparsity s. We will prove that there is an affine subspace of the vector space F_2^n, of dimension O(A), on which the f is constant.
Abstract: Mathew Kahle and Elizabeth Meckes recently established interesting results concerning the topology of the clique complex $X(n,p)$ on an Erdos Renyi graph $G(n,p).$ Specifically, they showed that, if $p = n^{\alpha}$
Abstract: Alice and Bob want to compute jointly/collaboratively a function. They are both super-computers. However, part of the input is with Alice and the other part is with Bob.
