A list of all the faculty members within ANC. Member Subject area Image Douglas Armstrong Molecular neuroinformatics, network models, behavioural models Image Angus Chadwick Computational/theoretical neuroscience and machine learning Image Matteo Degiacomi computational biophysics, machine learning, generative modelling, molecular dynamics simulations, integrative modelling Image Nigel Goddard Probabilistic modeling of energy-related systems Image 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. Image Michael Gutmann Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology Image Matthias Hennig Neural dynamics, neural data analysis, neural interfaces and brain development Image 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) Image Nina Kudryashova Neuronal population dynamics, predictive processing, sensorimotor coupling and human-machine-interfaces Image Oisin Mac Aodha Human-in-the-loop machine learning, machine teaching, deep learning, and computer vision Image 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 Image Iain Murray Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis Image Siddharth Narayanaswamy Explainable and Interpretable ML, Bayesian program learning, Vision and Language, Probabilistic programming, Neuro-symbolic systems, Approximate inference, Human-machine interaction. Image Kia Nazarpour Neurotechnology, human-in-the-loop machine learning, health data Image Arno Onken Probabilistic and machine learning methods for modelling and analysing neuroscience data Image Diego Oyarzun Control theory, systems and synthetic biology, machine learning, metabolic modelling Image 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. Image Peggy Seriès Bayesian approaches to cognition and perception Image 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 Image David Sterratt Computational neuroscience (network models of learning and memory, biomolecular networks, development of neural connectivity and data analysis) and teaching data science. Image 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. Image Antonio Vergari Efficient and reliable machine learning in the wild, tractable probabilistic modeling, combining learning and reasoning Image Andrea Weisse Computational biology, systems and synthetic biology, antimicrobial resistance, infectious diseases, dynamic systems and network models, molecular and patient data. Image Chris Williams Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models Image 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 2024-11-22
