UK AI Research Symposium (UKAIRS) Newcastle, UK – September 8–9, 2025
The Informed AI Hub was proud to participate in the inaugural UK AI Research Symposium (UKAIRS), held at Northumbria University. This landmark event brought together leading minds from across the UK’s AI research landscape, including UKRI-funded researchers, EPSRC AI Research Hubs and Centres for Doctoral Training, to foster collaboration and shape the future of responsible and impactful AI research.
Informed AI Hub colleagues in attendance were Harriet Lee, Professor Sidharth Jaggi, and Professor Neil Walton, along with our amazing early career researchers Fernando Estevez Casado and Guru Ganesan. The event allowed for much interaction with fellow researchers and other UKRI funded organisations; providing a valuable platform for cross-hub dialogue and strategic alignment with national research priorities.
Research Highlights
Fernando Estevez Casado (Imperial College London) presented his work at a specialist panel on AI & Robotics:
• An Integrated 3D Eye-Gaze Tracking Framework for Assessing Trust in Human-Robot Interaction
This innovative framework leverages head-mounted displays and Bayesian modelling to explore how eye-gaze patterns correlate with human trust in robotic systems. The study revealed that gaze behaviours such as fixation duration and saccade amplitude vary significantly under different robot reliability conditions, offering new insights into trust modelling in HRI.
Sanidhay Bhambay, Thirupathaiah Vasantam, and Neil Walton (Durham University), introduced their work at a poster session:
• Optimal Scheduling in a Quantum Switch: Capacity and Throughput Optimality
This work addresses the challenge of scheduling in quantum networks, proposing optimal strategies for data throughput and capacity in quantum switches—an area critical to the future of distributed quantum computing.
Ghurumuruhan Ganesan (University of Bristol); along with colleagues Siddharth Chatterjee (Tel Aviv University); Abhiram Lokanathan (ISI Delhi) also presented their work at a poster session
• Index Redundancy Mitigation in Datasets using Graphs and Shapley Values
This research proposes a novel method for reducing dataset redundancy using graph-theoretic models and Shapley value allocation. By identifying stable vertex sets and leveraging economic principles, the team demonstrated how to extract diverse, low-redundancy data subsets, improving model performance and interpretability.
Building Connections
Beyond the technical sessions, the symposium was a valuable opportunity for the Informed AI Hub team to connect with other AI hubs, project managers, and EPSRC representatives. These interactions are vital for aligning research goals, sharing best practices, and exploring future collaborations.
“UKAIRS was a fantastic opportunity to connect with the broader UK AI research community. The conversations we had highlighted the breadth of work happening in the UK AI community and just how interdisciplinary and impactful AI research can be. For the Informed AI Hub, these insights directly feed into our mission to develop AI systems that are not only technically robust but also socially responsible and trustworthy.”
— Professor Sidharth Jaggi, Director, Informed AI Hub
