Faculty members

A list of all the faculty members within ANC.

Member		Subject area
	Douglas Armstrong	Molecular neuroinformatics, network models, behavioural models
	Angus Chadwick	Computational/theoretical neuroscience and machine learning
	Nigel Goddard	Probabilistic modeling of energy-related systems
	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	Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology
	Matthias Hennig	Neural dynamics, neural data analysis, neural interfaces and brain development
	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)
	Nina Kudryashova	Neuronal population dynamics, predictive processing, sensorimotor coupling and human-machine-interfaces
	Oisin Mac Aodha	Human-in-the-loop machine learning, machine teaching, deep learning, and computer vision
	Nikolay Malkin	Bayesian machine learning and generative modelling, amortised inference for (neuro-)symbolic models, probabilistic reasoning/planning in language and formal systems, AI for science and mathematics
	Iain Murray	Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis
	Siddharth Narayanaswamy	Explainable and Interpretable ML, Bayesian program learning, Vision and Language, Probabilistic programming, Neuro-symbolic systems, Approximate inference, Human-machine interaction.
	Kia Nazarpour	Neurotechnology, human-in-the-loop machine learning, health data
	Arno Onken	Probabilistic and machine learning methods for modelling and analysing neuroscience data
	Diego Oyarzun	Control theory, systems and synthetic biology, machine learning, metabolic modelling
	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 Seriès	Bayesian approaches to cognition and perception
	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	Computational neuroscience (network models of learning and memory, biomolecular networks, development of neural connectivity and data analysis) and teaching data science.
	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	Efficient and reliable machine learning in the wild, tractable probabilistic modeling, combining learning and reasoning
	Andrea Weisse	Computational biology, systems and synthetic biology, antimicrobial resistance, infectious diseases, dynamic systems and network models, molecular and patient data.
	Chris Williams	Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models
	Heather Yorston	Machine Learning, Medical Imaging and AI, Gaussian Process Models, Image Processing, Mathematical and Computational Modelling, Virtual Human Eye model, Data Science

This article was published on 13 Sep, 2024