Date: 23 February 2026
Location: Saïd Business School, University of Oxford
AI is transforming research across every field — from energy systems and climate modelling to historical analysis and healthcare.
Yet researchers in different disciplines often face remarkably similar challenges: how to curate and structure data, how to validate AI-driven findings, how to integrate domain expertise with machine learning, and how to ensure results are robust and reproducible.
Despite these shared challenges, there are surprisingly few opportunities for researchers across fields to compare approaches and learn from one another.
The SuperAIRE 3rd Challenge Workshop brought together scientists from across disciplines who are applying AI in their respective disciplines to share their experiences.
We were delighted to host SuperAIRE’s 3rd Challenge Workshop at Saïd Business School, University of Oxford, welcoming over 65 participants for a day of rich interdisciplinary exchange.
We brought together researchers from across disciplines who are leveraging AI in their respective fields to share experiences and challenge one another’s thinking. The workshop created space for open, cross-disciplinary dialogue about what’s working, what remains challenging, and where the most important open questions lie.
We began with Robert Iliffe, exploring The digital Principia Mathematica as an exemplary form of human-AI collaboration — reflecting on the importance and enduring influence of Newton in shaping modern scientific thinking and its resonance in today’s AI innovation landscape.
We then heard from BINJIAN NIE and Zirui Wang on AI to Advance Chemical Engineering, highlighting current advances in the field, followed by Julian knight, Xin Tian, and Sisi (Sally) Qu on AI and deep immune phenotyping, demonstrating how AI is transforming our understanding of immune dysfunction and diagnostics.
In the afternoon, David Howey discussed Machine learning better batteries, and Jakob Foerster challenged us to reflect on the legacy of the research we focus our efforts on in his talk From Learning to Communicate to the AI Scientist.
We also heard from Lucie Abeler-Dörner on AI opportunities in vaccine trial design, showcasing how AI is helping to accelerate responses to outbreaks.
The day concluded with Volker Deringer on Machine-learned interatomic potentials for materials chemistry, and Nantas Nardelli with A primer on fusion energy, broadening the conversation to the future of sustainable energy systems.
What became clear is that while applications differ, many of the methodological and practical challenges are shared. Creating more opportunities for these conversations across fields will be essential as AI continues to reshape research.
Grateful to everyone who contributed to such thoughtful and energising discussions. This is just the beginning.
