Research Interest

My research lies at the intersection of software engineering, performance optimisation, and distributed systems. A major focus is on intelligent performance and reliability management in cloud and edge computing—for example, developing controllers that integrate predictive workload forecasting with reinforcement learning to make proactive scaling decisions based on real-time telemetry. These systems aim to optimise resource usage while meeting performance and availability targets, even under partial observability. They can be adopted as modular open-source tools across industry and the public sector, contributing to goals such as net-zero computing and smart manufacturing. A central theme of this work is addressing the unique challenges of cloud-native applications, particularly microservices, where distributed architectures and DevOps practices create complex trade-offs among performance, reliability, and cost.

I also explore how performance engineering can be more effectively integrated into software engineering education. Drawing on my experience with research tools and teaching distributed systems, I investigate AI-assisted learning resources that help students reason about system performance and reliability. This includes modular content, outcome-driven assessment frameworks, and adaptive lab exercises that simulate performance pitfalls and provide personalised feedback. The broader aim is to bridge the gap between theoretical understanding and practical expertise in modern computing environments.