Faculty members

A list of all the faculty members within ANC.

  Member Subject area
Douglas Armstrong
Douglas Armstrong Molecular neuroinformatics, network models, behavioural models.
Chris Bishop
Christopher Bishop Graphical models, variational methods, pattern recognition
Dragan Gasevic
Dragan Gasevic Learning analytics, educational technology, self-regulated learning, social learning, and higher education policy
Nigel Goddard
Nigel Goddard Probabilistic modeling of energy-related systems
Michael Gutmann
Michael Gutmann Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology
Matthias Hennig
Matthias Hennig Models of neural networks, homeostasis and development; visual and auditory neuroscience; analysis of large-scale electrophysiological recordings
Iain Murray
Iain Murray Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis
Arno Onken
Arno Onken   Probabilistic models, in particular copula-based models; Dimensionality reduction techniques; Information theory; Applications to biological systems
Diego Oyarzun
Diego Oyarzun Control theory, systems and synthetic biology, machine learning, metabolic modelling
Guido Sanguinetti

Guido Sanguinetti

Probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems
Rico Sennrich
Rico Sennrich Machine translation, natural language processing, deep learning
Peggy Series
Peggy Seriès Bayesian approaches to cognition and perception
Richard Shillcock
Richard Shillcock Word recognition and reading; hemispheric interaction; philosophy of cognitive modelling and theory construction; synaesthesia; artificial grammar learning
Ian Simpson
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
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
Charles Sutton Probabilistic modeling of large-scale computer systems, approximate inference, statistical processing of natural and programming languages
David Willshaw
David Willshaw The development of nerve connections particularly the formation of topographic maps. David is Emeritus and so no longer supervises students
Chris Williams
Chris Williams Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models