Parallel Session Themes
AI for the Natural & Physical Sciences
This track explores how artificial intelligence is reshaping scientific discovery across the natural and physical sciences, including astronomy, chemistry, physics, earth and planetary science, materials science, and laboratory automation. As these disciplines increasingly generate vast, high-dimensional, and complex datasets, conventional analytical approaches are often no longer sufficient to fully exploit the scientific value embedded within them. The AI for the Natural & Physical Sciences session will highlight the growing convergence between AI and foundational scientific research, fostering interdisciplinary collaboration among AI researchers, domain scientists, engineers, and industry practitioners.
AI for Life & Health
The AI for Life & Health track focuses on the transformative role of artificial intelligence across healthcare, biomedical research, and population health. It brings together interdisciplinary advances spanning AI for independent living, assistive technologies and accessibility, health and disease monitoring across the lifespan, clinical decision support, mental health and wellbeing, and community healthcare. The track also covers emerging areas such as digital health and remote monitoring, drug discovery, genomics, and healthcare robotics, alongside methods for analysing multimodal health data. The track aims to showcase how AI can enhance both scientific discovery and healthcare delivery, from improving diagnostic accuracy and enabling personalised medicine to supporting proactive, data-driven care pathways across diverse populations.
Algorithms, Agents & Theory
The Algorithms, Agents, & Theory sessions will focus on the mathematical and algorithmic foundations that underpin modern AI systems, spanning topics including approximate inference, generative models, information theory, probability and statistics for AI, reinforcement learning, game theory and multi-agent systems, and optimisation. Game-theoretic approaches are increasingly vital for designing robust AI systems that operate in competitive or collaborative multi-stakeholder environments, from automated trading to spectrum allocation. The theoretical advances that ultimately determine the capability, reliability, and scalability of every AI product and service deployed in the real world. This track offers a unique opportunity to engage with the UK's leading minds in foundational AI research, identify collaboration and recruitment opportunities, and help steer the fundamental science that will define the competitive landscape of AI-enabled industries for years to come.
AI for Engineering & Advanced Manufacturing
The AI for Engineering & Advanced Manufacturing session will explore the transformative role of artificial intelligence in reshaping engineering practices and advanced manufacturing systems, enabling a new generation of intelligent, adaptive, and highly efficient industrial processes. Central themes include the integration of AI with digital twins, advanced simulation, and data-driven design, allowing engineers to explore complex design spaces, optimise performance, and significantly reduce development cycles. AI techniques such as machine learning, computer vision, and reinforcement learning are increasingly applied to real-time monitoring, predictive maintenance, and autonomous control of manufacturing operations, ensuring higher precision, improved quality, and reduced downtime.
Sustainability & AI
This track focuses on the growing role of artificial intelligence in addressing sustainability challenges across environmental, economic, and social systems. As pressures on climate, resources, and ecosystems increase, AI is opening up new ways to analyse complex data, improve efficiency, and support more informed and timely decision-making. At the same time, the environmental impact of AI itself (including energy use, material demands, and lifecycle effects) needs careful consideration as these technologies continue to scale. We invite contributions that explore both the use of AI in supporting sustainability goals and the sustainability of AI technologies in practice. Submissions may cover a wide range of areas, including climate modelling, biodiversity monitoring, energy systems, sustainable supply chains, agriculture, resource efficiency, and circular economy approaches. Work on Green AI, as well as edge and distributed systems suited to resource-constrained settings, is particularly welcome.
Synergistic Human-AI Combination
The Synergistic Human-AI Combination track explores the transformative potential of synergistic human–AI collaboration across research, practice, and organisational contexts. As AI becomes increasingly embedded in everyday decision-making and workflows, understanding how humans and intelligent systems can effectively work together is critical. This theme brings together interdisciplinary perspectives on designing, deploying, and governing AI in ways that enhance human capabilities while ensuring responsible and ethical outcomes.
The track also emphasises human-centred and responsible AI, encouraging critical reflection on ethics, accountability, inclusivity, and societal impact. Topics may include transparency, fairness, and governance frameworks, as well as practical approaches to embedding ethical principles into system design and implementation.
