People

Faculty

	Douglas Armstrong Molecular neuroinformatics, network models, behavioural models
	Christopher Bishop Graphical models, variational methods, pattern recognition
	Dragan Gasevic Learning analytics, educational technology, self-regulated learning, social learning, and higher education policy
	Nigel Goddard Probabilistic modeling of energy-related systems
	Michael Gutmann Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology
	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
	Arno Onken Probabilistic models, in particular copula-based models; Dimensionality reduction techniques; Information theory; Applications to biological systems
	Guido Sanguinetti Probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems

	Rico Sennrich Machine translation, natural language processing, deep learning
	Peggy Seriès Bayesian approaches to cognition and perception
	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
	Chris Williams Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models