EMS PhD Thesis Prize winner 2021

Dr Leonardo Tolomeo (The University of Edinburgh) The EMS PhD Thesis Prize for 2021 was awarded to Dr Leonardo Tolomeo of the Mathematisches Institut der Universität Bonn (PhD, University of Edinburgh) for his outstanding thesis ‘Stochastic dispersive PDEs with additive space-time white noise’.

Whittaker Prize winner 2021

Dr Ben Davison (The University of Edinburgh) The Sir Edmund Whittaker Memorial Prize for 2021 has been awarded to Dr Ben Davison of The University of Edinburgh in recognition of his outstanding research achievements in the fields of enumerative counting invariants in algebraic geometry and non-commutative algebra.

EMS Impact Prize winners 2021

Prof. Marian Scott OBE (University of Glasgow) and Prof. Andrew Cairns (Heriot-Watt University) Marian Scott is Professor of environmental Statistics at the University of Glasgow. She is an applied statistician with broad research interests. Her current projects span archaeology and radiocarbon dating, measuring animal welfare and quality of life and more widely, the environment, whether that be air pollution and […]

Steven Tobias (University of Edinburgh)

From Order to Chaos and Chaos to Order in Fluid Flows The eleven year solar activity cycle is a remarkable example of regular behaviour emerging from an extremely turbulent system. The jets on Jupiter sit unmoving on a sea of turbulent eddies. Astrophysical phenomena often display organisation on spatial and temporal scales much larger than the turbulent processes that drive […]

Martin Bridson (University of Oxford)

Finite Shadows of Infinite Groups, Finiteness Properties, and Geometry There are many situations in geometry and group theory where it is natural, convenient or necessary to explore infinite groups via their actions on finite objects. But how much understanding can one really gain about an infinite group by examining its finite images? Sometimes little, sometimes a lot. In this colloquium […]

Gavin Gibson (Heriot-Watt University)

Data Augmentation and Imagination The technique of data augmentation, whereby observed data are effectively augmented by additional quantities not actually observed in an experiment, has proved to be extremely powerful in modern statistics generally, and in Bayesian parametric inference in particular.  This talk will describe its application to Bayesian inference for epidemic models where many challenges arise from the typically incomplete observations of epidemic processes.

Henry Segerman (Oklahoma State University)

Artistic Mathematics: Truth and Beauty This is about Henry’s work in mathematical visualisation: making accurate, effective, and beautiful pictures, models, and experiences of mathematical concepts. He discusses what it is that makes a visualisation compelling, and show many examples in the medium of 3D printing, as well as some work in virtual reality and spherical video. He also discusses his experiences […]

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