School of Mathematics


General Audience Maths Edinburgh Seminars

Speakers from the School of Mathematics will explain their research to a general mathematical audience, including mature undergraduates, postgraduates, postdocs and staff. Each session will contain two 25 minutes talks.

This seminar series will be a great opportunity to:
- Get an idea about the research which is going on in the School
- Reinforce relationships between staff and students, and give our undergraduates an idea of "what it is like to be a mathematician"
Please, contact Francesca Iezzi, if you are interested in giving a talk.

Schedule for Spring 2018


Thursday 18th January 2018, 13:10-13:45, room 5323 (research seminar room)
Julian Hall
Optimal diets for all!

This talk introduces linear programming (LP) via the human diet problem. After giving a brief history of the simplex method for solving LP problems, the diet problem in the animal feed formulation industry is described. This yields an insight into the challenge and value of solving large-scale LP problems.


Thursday 1st February 2018, 13:10-13:45, room 5323 (research seminar room)
Zofia Tristanova
Diffusion maps

Diffusion maps is a popular framework based upon diffusion processes for finding meaningful geometric descriptions of data sets. In this talk, I will explain how this method can be used to define collective reaction coordinates in molecular dynamics.


Thursday 22nd March 2018, 13:10-13:45, room 5326  (note the unusual room)
Diletta Martinelli
Minimal Model Program: Contract your bad curves!

Algebraic varieties are solutions of a system of polynomial equations in the affine or in the projective space. They are very fundamental objects and have been studied since ancient times.

In this talk, I will give a gentle introduction to the Minimal Model Program, one of the main tool in the classification of higher dimensional algebraic varieties.



Schedule for Autumn 2017

Tuesday 7th November 2017, 13:00-14:00, room 5323 (research seminar room)
Kostas Zygalakis
Bayesian Uncertainty Quantification in the Classification of High Dimensional Data

In this talk, we present a Bayesian framework for semi-supervised binary classification on graphs. We develop several Bayesian models through the construction of a Gaussian prior from the graph Laplacian. Connections to the Ginzburg-Landau model are also made through the notion of a push-forward of the Gaussian prior under the double-well thresholding. We introduce efficient MCMC methods designed for large data sets to effectively sample from the posterior distribution for large scale problems. Through a variety of numerical experiments, we demonstrate the ability to perform uncertainty quantification by sampling from the posterior distribution. In particular, we observe empirically that the posterior mean and variance aligns well with certain external notions of uncertainty.

Francisco Sobral
Models in trust-region algorithms

In this talk, we will show a very general version of a trust-region algorithm for unconstrained optimization problems. The idea is to extract only the important conditions that a model should have in order to successfully prove convergence of the algorithm. The described algorithm can then be used in a derivative-free implementation.



Tuesday 21st November 2017, 13:10-13:45, room 5323 (research seminar room)


Chris Dent
Famous quotes on energy systems modelling

Mathematical and computer modelling is widely used to support decisions in energy systems planning and in development of government energy policy. This talk will explore key issues in the use of modelling for decision support (with the help of a few experts from history), and give a flavour of what we at Edinburgh are doing about these.


Tuesday 5th December 2017, 13:10-13:45, room 5323 (research seminar room)


Jonathan Gair
Measuring the size of the Universe

Abstract: The expansion history of the Universe encodes information about the matter and energy content on large scales and hence is a key observable for cosmological models. Starting with Edwin Hubble in 1929, astronomers have been improving their measurement of the expansion history for  nearly a century. We describe some of the mathematics and statistics underlying these measurements and the most recent result - the first ever measurement of the expansion rate of the Universe using gravitational waves. The latter was made possible by combined gravitational wave and electromagnetic observations of the binary neutron star merger GW170817 discovered by LIGO in August 2017.