Zoomposium 1: 4 May 2020
Published: 4 May 2020
Dr SIMON CANDELARESI: 'Magnetic Field Topology in Plasmas' Dr FANI DELIGIANNI: 'Building reliable systems on unreliable neurophysiological data' Dr BEN SWALLOW: 'Bayesian inference in complex biotic systems'
Watch Zoomposia 1 (password: 9b=5^kQm)
Speakers:
Dr Simon Candelaresi, School of Mathematics and Statistics
'Magnetic Field Topology in Plasmas'
Hot plasmas, like in our Sun, exhibit strong magnetic fields which are strong enough to affect the plasma motions. The structure of the magnetic field has a strong effect on the evolution of the plasma. If the magnetic field is of the shape of knots or links, the dynamics is restricted in a particular way. This means that certain configurations cannot be reached.
My research focuses on how to quantify the topology (tangling, twisting) of magnetic fields. I also study the effect of the topology on the plasma and its back-reaction with applications in astrophysical plasmas and fusion devices.
Dr Fani Deligianni, School of Computing Science
'Building reliable systems on unreliable neurophysiological data'
My interests include medical image/neuroimage computing, brain computer interfaces, statistical machine learning and health informatics. One of the projects I am interested is about cognitive workload assessment with both wearable sensors as well as rgbd cameras for human motion analysis. My work will be benefited by strong collaboration with sensing/engineering teams with expertise in the development of wearable sensors.
Dr Ben Swallow, School of Mathematics and Statistics
'Bayesian inference in complex biotic systems'
My research interests lie mainly in the field of Bayesian statistical inference, particularly Markov chain Monte Carlo (MCMC) methods, data integration and model selection, applied to problems in ecological, environmental and biological sciences. I am currently interested in developing and applying efficient algorithms for parameter estimation and inference in joint abundance models of ecological communities, particularly using citizen science data.I am also working on methods for inference in stochastic dynamical systems in systems biology.
I would be interested in developing further collaborations with academics in these and complementary areas that may require these types of methods for their own data and models.
First published: 4 May 2020