Team

Aisha Lawal Shuaibu Medical image registration

Amy Upson Probabilistic machine learning for health and patient care

Annie Hartanto Multi-modal modelling for Alzheimer’s disease progression and early cognitive decline

Chenlai Zhou diffusion model,conditional guidance,efficient learning,image reconstruction

Giuseppe Castiglione optimisation theory, AI safety, bio-inspired algorithms

Isabel Lahmann Using implicit neural representations to learn continuous spatiotemporal structure from ecoacoustic data

Ivor Simpson Probabilistic machine learning for inverse problems with spatiotemporal data.

Kieran Gibb generative AI for interpreting ecoacoustic inference

Luca Trautmann Uncertainty quantification in inverse problems, deep generative modeling, hallucination prevention, and extracting latent information from data

Paula Seidler Interpretable uncertainty-aware perceptual similarity metrics, perceptual loss functions

Peter Wijeratne Probabilistic modelling, Computer vision, Machine learning, Neuroimaging, Medical image analysis

Sanduni Pinnawala Interpretable latent variable models that combine machine learning with biophysics to model mechanisms of neurodegenerative diseases

Shahzad Ali Graph learning and AI methods for neurodegenerative disease modelling

Shisi Yu Sampling methods for high dimensional latent variable models

Members of our lab include PIs, students, and collaborators from multidisciplinary backgrounds, working together across imaging, vision, ecology, and core machine learning, with a focus on interpretable modelling.