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!
We are pleased to welcome four new PhD students to VIOS.
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, representation learning, and decision support for data-scarce clinical settings. Current work spans cardiac MRI, surgical digital twins, and multimodal models for clinically grounded analysis.
Causal discovery, disentangled representation learning, and reliable inference for high-stakes domains. This work underpins VIOS's contribution to the EPSRC CHAI Hub.
Computer vision and digital infrastructure for plant phenotyping, crop improvement, and disease resilience. This includes open research infrastructure through projects such as PhenomUK.
AI for virtual power plants, grid resilience, and data-driven materials innovation, alongside broader work on inverse problems across energy, environmental, and biomedical systems.
Active & Recent Funded Projects
A conversational multi-agent AI system for automated plant phenotyping
A Causal Framework for Mitigating Data Shifts in Healthcare
Arabidopsis Thaliana Data for A Conversational Multi-Agent AI System for Automated Plant Phenotyping