Forthcoming Seminars

Please note that the list below only shows forthcoming events, which may not include regular events that have not yet been entered for the forthcoming term. Please see the past events page for a list of all seminar series that the department has on offer.

Past events in this series
25 November 2019
Karen Hunger Parshall

American mathematics was experiencing growing pains in the 1920s. It had looked to Europe at least since the 1890s when many Americans had gone abroad to pursue their advanced mathematical studies.  It was anxious to assert itself on the international—that is, at least at this moment in time, European—mathematical scene. How, though, could the Americans change the European perception from one of apprentice/master to one of mathematical equals? How could Europe, especially Germany but to a lesser extent France, Italy, England, and elsewhere, come fully to sense the development of the mathematical United States?  If such changes could be effected at all, they would likely involve American and European mathematicians in active dialogue, working shoulder to shoulder in Europe and in the United States, and publishing side by side in journals on both sides of the Atlantic. This talk will explore one side of this “equation”: European mathematicians and their experiences in the United States in the 1920s.

  • History of Mathematics
26 November 2019
Chris Fewster

Measurement outcomes in quantum theory are randomly distributed, and local measurements of the energy density of a QFT exhibit nontrivial fluctuations even in a vacuum state. This talk will present recent progress in determining the probability distribution for such measurements. In the specific case of 1+1 dimensional CFT, there are two methods (one based on Ward identities, the other on "conformal welding") which can lead to explicit closed-form results in some cases. The analogous problem for the free field in 1+3 dimensions will also be discussed.

  • Quantum Field Theory Seminar
26 November 2019

Persistent homology has been applied to graph classification problems as a way of generating vectorizable features of graphs that can be fed into machine learning algorithms, such as neural networks. A key ingredient of this approach is a filter constructor that assigns vector features to nodes to generate a filtration. In the case where the filter constructor is smoothly tuned by a set of real parameters, we can train a neural network graph classifier on data to learn an optimal set of parameters via the backpropagation of gradients that factor through persistence diagrams [Leygonie et al., arXiv:1910.00960]. We propose a flexible, spectral-based filter constructor that parses standalone graphs, generalizing methods proposed in [Carrière et al., arXiv: 1904.09378]. Our method has an advantage over optimizable filter constructors based on iterative message passing schemes (`graph neural networks’) [Hofer et al., arXiv: 1905.10996] which rely on heuristic user inputs of vertex features to initialise the scheme for datasets where vertex features are absent. We apply our methods to several benchmark datasets and demonstrate results comparable to current state-of-the-art graph classification methods.

26 November 2019
Nikitas Rontsis

The Alternating Directions Method of Multipliers (ADMM) is a widely popular first-order method for solving convex optimization problems. Its simplicity is arguably one of the main reasons for its popularity. For a broad class of problems, ADMM iterates by repeatedly solving perhaps the two most standard Linear Algebra problems: linear systems and symmetric eigenproblems. In this talk, we discuss how employing standard Krylov-subspace methods for ADMM can lead to tenfold speedups while retaining convergence guarantees.

  • Numerical Analysis Group Internal Seminar
26 November 2019
Yan Fyodorov

I will consider the problem of reconstructing a signal from its encrypted and corrupted image
by a Least Square Scheme. For a certain class of random encryption the problem is equivalent to finding the
configuration of minimal energy in a (unusual) version of spherical spin
glass model.  The Parisi replica symmetry breaking (RSB) scheme is then employed for evaluating
the quality of the reconstruction. It  reveals a phase transition controlled
by RSB and reflecting impossibility of the signal retrieval beyond certain level of noise.

  • Random Matrix Theory seminars


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