People

Faculty

	Douglas Armstrong Molecular neuroinformatics, network models, behavioural models
	James Bednar Simulations of the development of the visual cortex
	Christopher Bishop Graphical models, variational methods, pattern recognition
	Nigel Goddard Probabilistic modeling of energy-related systems
	Matthias Hennig Models of neural networks, homeostasis and development; visual and auditory neuroscience; analysis of large-scale electrophysiological recordings
	Iain Murray Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis
	Guido Sanguinetti Probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems
	Peggy Seriès Bayesian approaches to cognition and perception
	Dragan Gasevic

	Richard Shillcock Word recognition and reading; hemispheric interaction; philosophy of cognitive modelling and theory construction; synaesthesia; artificial grammar learning
	Ian Simpson Regulatory genomics, bioinformatics and computational biology. Neural development and function especially in cortical structures and in relation to cognition, learning and memory using genomic, meta-genomic, transcriptomic and proteomic data.
	Amos Storkey Structured machine learning and big data: Bayesian methods, Machine Learning Markets, deep learning, learning temporal systems, neural computation. Applications in image analysis, brain imaging, and medicine.
	Charles Sutton Probabilistic modeling of large-scale computer systems, approximate inference, statistical processing of natural and programming languages
	Tom Thorne Systems biology, graphical models of gene regulatory networks, Bayesian statistics, Bayesian nonparametrics
	Mark van Rossum Computational neuroscience: synaptic plasticity, noise in neural systems, the early visual system, the retina
	Chris Williams Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models
	David Willshaw Computational models of development of the nervous system
	Michael Gutmann Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology