University of Edinburgh · CHAI AI Hub

VIOS

Collaboratory

Βίος · Greek for life · pronounced VEE-ohs

We advance interdisciplinary AI — applying machine learning, computer vision, and causal reasoning to open challenges in medicine, agriculture, and the life sciences.

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"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

Latest Updates

News & highlights

Apr 23, 2026

New Website

Our new website is live! Enjoy the cleaner, modern design and easier navigation. Stay tuned for updates!

Sep 1, 2025

New PhD Students Join VIOS

We are pleased to welcome four new PhD students to VIOS.

Dec 9, 2024

New CHAI PhD positions in partnership with Canon Medical

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.

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Research Focus

Where AI meets
the sciences

Healthcare Systems

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.

Cardiac MRI Digital Twin Surgery Multi-modal Foundation Models

Causal AI & Fair Representations

Causal discovery, disentangled representation learning, and reliable inference for high-stakes domains. This work underpins VIOS's contribution to the EPSRC CHAI Hub.

Causal Discovery Disentanglement Bias Mitigation CHAI Hub

Sustainability & Agriculture

Computer vision and digital infrastructure for plant phenotyping, crop improvement, and disease resilience. This includes open research infrastructure through projects such as PhenomUK.

Plant Phenotyping PhenomUK Open Data Trait Estimation

AI for Energy & Inverse Problems

AI for virtual power plants, grid resilience, and data-driven materials innovation, alongside broader work on inverse problems across energy, environmental, and biomedical systems.

Energy AI Virtual Power Plant Inverse Problems Wearable Kidney

Active & Recent Funded Projects

EPSRC Real-time Digital Twin Assisted Surgery
Selected Work

Representative publications

Nature Communications

A conversational multi-agent AI system for automated plant phenotyping

Feng Chen, Ilias Stogiannidis, Andrew Wood, Danilo Bueno, Dominic Williams, Fraser Macfarlane, Bruce Grieve, Darren M. Wells, …

Code → Website →
Transactions on Machine Learning Research

Causal Ordering for Structure Learning from Time Series

Pedro Sanchez, Damian Machlanski, Steven McDonagh, Sotirios A. Tsaftaris

Code → Website →
npj Digital Medicine

Digital twin assisted surgery, concept, opportunities, and challenges

Lisa Asciak, Justicia Kyeremeh, Xichun Luo, Asimina Kazakidi, Patricia Connolly, F. Picard, Kevin O’Neill, Sotirios A. Tsaftaris, …

Website →
Neurips

Benchmarking Counterfactual Image Generation

Nefeli Gkouti, Thomas Melistas, Yannis Panagakis, Giorgos Papanastasiou, Pedro Sanchez, Nikos Spyrou, Sotirios A. Tsaftaris, Athanasios Vlontzos

Code → Website →
MICCAI

The MRI Scanner as a Diagnostic: Image-Less Active Sampling

Yuning Du, Rohan Dharmakumar, Sotirios A. Tsaftaris

Code → Website →
IEEE Journal of Biomedical and Health Informatics

Zero-Shot Medical Phrase Grounding With Off-the-Shelf Diffusion Models

Konstantinos Vilouras, Pedro Sanchez, Alison Q. O’Neil, Sotirios A. Tsaftaris

Code → Website →
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Affiliations
The University of Edinburgh CHAI AI Hub Canon Medical Research PhenomUK