Collaboratory
We advance interdisciplinary AI — applying machine learning, computer vision, and causal reasoning to open challenges in medicine, agriculture, and the life sciences.
"Life is central to our mission — with advances in AI, computer vision and inverse problems, we address societal challenges by solving key problems in the life and natural sciences."
— Sotirios A. Tsaftaris · Chair in Machine Learning & Computer Vision
Our new website is live! Enjoy the cleaner, modern design and easier navigation. Stay tuned for updates!
Our team is expanding! We have four more PhD students join us!
We have now completed recruitment for two PhD positions as part of the CHAI AI Hub in collaboration with Canon Medical. New openings (if any) will be announced here join us page.
Medical imaging and image analysis with a focus on representation learning — doing more with less through semi-supervised, multi-task, and multi-modal approaches. Active work on cardiac MRI, digital twin surgery, and AI-assisted clinical decision making.
Disentangled representation learning, causal discovery, and equivariant architectures to build AI that reasons rather than correlates — enabling reliable, bias-aware predictions in high-stakes domains. Central to the EPSRC CHAI Hub.
Computer vision for plant phenotypic trait estimation, crop breeding, and disease resistance. Open data frameworks and digital research infrastructure — including PhenomUK — supporting sustainable global agriculture.
Virtual power plants, AI-driven grid resilience, and data-driven materials innovation for the wearable artificial kidney. Addressing complex inverse problems in energy, environmental, and biomedical sciences.
Active & Recent Funded Projects
A Causal Framework for Mitigating Data Shifts in Healthcare
Arabidopsis Thaliana Data for A Conversational Multi-Agent AI System for Automated Plant Phenotyping
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation