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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Ajitha Rajan | My research is in the field of computational immunology and aims to comprehensively characterise T-Cell antigen presentation landscapes and deliver predictive models that will allow for comparative immunology within homosapiens and across species. Our computational models will open the door to answer questions about immunotherapy efficacy. We also use machine learning techniques to predict overall survival and recurrence in cancer datasets, currently for Glioma, Renal and Oesophageal cancer. |
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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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Antonio Vergari | Efficient and reliable machine learning in the wild, tractable probabilistic modeling, combining learning and reasoning |
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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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Andrea Weisse | Computational biology, systems and synthetic biology, health data science, bacterial growth dynamics and antimicrobial resistance |
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Chris Williams | Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models |