Our research in quantum computing focuses on developing rigorous benchmarking methods and advancing the hardware–software interface to improve the reliability and performance of emerging quantum systems. We investigate error characteristics, qubit fidelity, compiler optimisation, and cross-stack integration to enable more predictable and scalable quantum workloads. We aim to collaborate with public research teams to bridge the gap between quantum hardware constraints and practical application design, supporting the development of next-generation quantum algorithms and hybrid computing models.
We explore the use of ultra-lightweight machine learning models embedded directly within microfluidic and lab-on-chip systems. By integrating TinyML with real-time sensing and control mechanisms, we aim to enable on-device decision-making for diagnostics, fluid manipulation, anomaly detection, and reaction optimisation. Our work focuses on model compression, low-power inference, and robust deployment in constrained environments, paving the way for smarter, autonomous microfluidic platforms for healthcare, environmental monitoring, and point-of-need testing.
We investigate how generative AI can create more adaptive, personalised, and engaging learning experiences across educational, corporate, and technical training environments. Our research focuses on multimodal content generation, behaviour modelling, and dynamic learning pathways that respond to individual learner styles, pace, and performance. By integrating conversational agents, scenario-based simulations, and automated knowledge reinforcement, we aim to develop learning systems that continuously evolve with user needs. These AI-powered environments enhance comprehension, increase motivation, and deliver measurable improvements in learning outcomes across diverse domains.