Faculty members

A list of all the faculty members within ANC.

                            Member     Subject area
Douglas Armstrong
Douglas Armstrong Molecular neuroinformatics, network models, behavioural models
Angus Chadwick Computational/theoretical neuroscience and machine learning
Nigel Goddard
Nigel Goddard Probabilistic modeling of energy-related systems
Henry Gouk

Henry Gouk


Transfer Learning and Meta-Learning with Deep Neural Networks; Machine Learning on tabular data; Learning-theoretic analysis of Machine Learning methods problem settings (e.g., via Rademacher Complexity, PAC-Bayes, Algorithmic Stability, etc); Reliable model selection and evaluation.

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
Ava Khamseh
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)
Oisin Mac Aodha Human-in-the-loop machine learning, machine teaching, deep learning, and computer vision
Iain Murray
Iain Murray Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis
K Nazarpour
Kia Nazarpour Neurotechnology, human-in-the-loop machine learning, health data
Arno Onken
Arno Onken

Probabilistic and machine learning methods for modelling and analysing neuroscience data


Diego Oyarzun
Diego Oyarzun Control theory, systems and synthetic biology, machine learning, metabolic modelling
a r
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.
Peggy Series
Peggy Seriès Bayesian approaches to cognition and perception
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
David Sterratt
David Sterratt

Computational neuroscience (network models of learning and memory, biomolecular networks, development of neural connectivity and data analysis) and teaching data science.

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.
Antonio Vergari
Antonio Vergari Efficient and reliable machine learning in the wild, tractable probabilistic modeling, combining learning and reasoning
a weisse
Andrea Weisse Computational biology, systems and synthetic biology, health data science, bacterial growth dynamics and antimicrobial resistance
Chris Williams
Chris Williams Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models
Heather Yorston
Heather Yorston Machine Learning, Medical Imaging and AI, Gaussian Process Models, Image Processing, Mathematical and Computational Modelling, Virtual Human Eye model, Data Science