Faculty members
A list of all the faculty members within ANC.
| Member | Subject area | |
|---|---|---|
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Douglas Armstrong | Molecular neuroinformatics, network models, behavioural models |
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Christopher Bishop | Graphical models, variational methods, pattern recognition |
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Angus Chadwick | Computational/theoretical neuroscience and machine learning |
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Dragan Gasevic | Learning analytics, educational technology, self-regulated learning, social learning, and higher education policy |
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Nigel Goddard | Probabilistic modeling of energy-related systems |
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Michael Gutmann | Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology |
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Matthias Hennig | Models of neural networks, homeostasis and development; visual and auditory neuroscience; analysis of large-scale electrophysiological recordings |
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Ava Khamseh | Non-parametric probabilistic modelling, targeted learning, machine learning, causality and its applications to population biomedicine, cancer modelling, experimental molecular biology (genomics and transcriptomics) |
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Oisin Mac Aodha | Human-in-the-loop machine learning, machine teaching, deep learning, and computer vision |
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Iain Murray | Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis |
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Kia Nazarpour | Neurotechnology, human-in-the-loop machine learning, health data |
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Arno Onken | Probabilistic models, in particular copula-based models; Dimensionality reduction techniques; Information theory; Applications to biological systems |
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Diego Oyarzun | Control theory, systems and synthetic biology, machine learning, metabolic modelling |
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Probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems | |
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Rico Sennrich | Machine translation, natural language processing, deep learning |
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Peggy Seriès | Bayesian approaches to cognition and perception |
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Richard Shillcock | Word recognition and reading; hemispheric interaction; philosophy of cognitive modelling and theory construction; synaesthesia; artificial grammar learning |
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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. |
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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. |
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Charles Sutton | Probabilistic modeling of large-scale computer systems, approximate inference, statistical processing of natural and programming languages |
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David Willshaw | The development of nerve connections particularly the formation of topographic maps. David is Emeritus and so no longer supervises students |
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Chris Williams | Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models |