Upcoming Meetings
Venue Date Speaker and Affiliation University of Stirling 16 May 2025 Miguel Pinar (University of Granada)
Prof Jon Chapman (University Oxford)
Exponential asymptotics and applied mathematics Divergent series are the invention of the devil, and it is shameful to base on themany demonstration whatsoever.” – N. H. Abel.The lecture will introduce the concept of an asymptotic series, showing how useful divergentseries can be, despite Abel’s reservations. We will then discuss Stokes’ phenomenon, wherebythe coefficients in the series appear to change discontinuously. […]
Prof Alain Goriely (University of Oxford)
Seeing is deceiving: The mathematics of visual illusions Illusions have been a constant source of amusement but they are also a unique gateway into understanding the way we perceive the world and how the brain processes information. The simplest visual illusions often involve a primary element—be it a line or a circle—that undergoes deformation or displacement due to the influence […]
Prof Anna-Karin Tornberg (KTH Stockholm)
Layer potentials – quadrature error estimates and approximation with error control When numerically solving PDEs reformulated as integral equations, so-called layer potentials must be evaluated. The quadrature error associated with a regular quadrature rule for evaluation of such integrals increases rapidly when the evaluation point approaches the surface and the integrand becomes sharply peaked. Error estimates are needed to determine […]
Professor Yang-hui He (London Institute for Mathematical Sciences and University of Oxford)
The AI Mathematician We argue how AI can assist mathematics in three ways: theorem-proving,conjecture formulation, and language processing.Inspired by initial experiments in geometry and string theory in 2017, we summarize how thisemerging field has grown over the past years, and show how various machine-learningalgorithms can help with pattern detection across disciplines ranging from algebraicgeometry to representation theory, to combinatorics, and […]
Prof. Sir David Spiegelhalter FRS OBE (University of Cambridge)
Chance, luck, and ignorance; how to put our uncertainty into numbers We all have to live with uncertainty about what is going to happen, what has happened, and why things turned out how they did. We attribute good and bad events as ‘due to chance’, label people as ‘lucky’, and (sometimes) admit our ignorance. I will show how to use the […]
Prof. Mark Jerrum (Queen Mary University London)
Perfect sampling, old and new The possibility of obtaining perfect samples efficiently from a complex probability distribution entered the consciousness of the community in the mid-nineties with the invention of `coupling from the past’ by Propp and Wilson. The study of perfect samplers of course has considerable theoretical appeal. But, in addition, their ‘self clocking’ aspect may have practical advantages. […]
Prof. Benjamin Doyon (King’s College London)
The emergence of hydrodynamics in many-body systems One of the most important problems of modern science is that of emergence. How do laws of motion emerge at large scales of space and time, from much different laws at small scales? Hydrodynamics offers a basic but very relevant example. Molecules in air simply go along their journey following Newton’s equations. But […]
Prof. John Baez (University of California, Riverside)
Category Theory in Epidemiology “Stock and flow diagrams” are widely used for modeling in epidemiology. Modelers often regard these diagrams as an informal step toward a mathematically rigorous formulation of a model in terms of ordinary differential equations. However, these diagrams have a precise syntax, which can be explicated using category theory. Although commercial tools already exist for drawing these […]
Prof. Raúl Tempone (RWTH Aachen University and KAUST)
Navigating the Unknown: Harnessing Uncertainty in Renewable Energy and Heart Health Uncertainty Quantification (UQ) emerges as a guiding force in the turbulent sea of data-driven domains, from energy to health. This talk presents a methodology that harnesses UQ for robust renewable energy forecasting, employing a stochastic differential equation model that sails beyond the challenges of wind and solar predictability. Shifting […]