Agile project management is highly effective in managing complex and evolving projects. It allows for continuous adaptation as new challenges arise or project requirements change. In research environments, particularly projects such as my PhD work on DTs, SHM, and IoT integration, Agile enables the team, me and my supervisory team, to regularly reassess and realign goals, ensuring that adjustments can be made based on new discoveries or unforeseen obstacles. This iterative process allows for steady progress, with tasks broken down into manageable sprints, helping to maintain momentum and enabling the early identification of potential issues or areas for improvement. Regular collaboration and feedback are key elements of Agile, ensuring continuous input from supervisors, which is especially crucial when integrating cutting-edge technologies such as AI and IoT. Additionally, Agile’s focus on risk mitigation helps identify and address potential risks early, reducing the impact of uncertainties.
To further enhance the effectiveness of Agile, I also incorporate the Eisenhower Matrix into my daily work. This tool helps prioritise tasks based on urgency and importance, ensuring that critical and high-impact activities are addressed promptly, while less urgent tasks are managed appropriately. By categorising daily tasks into four quadrants—urgent and important, important but not urgent, urgent but not important, and neither urgent nor important—I can streamline my focus, avoid distractions, and delegate tasks that do not require immediate attention. The combination of Agile project management and the Eisenhower Matrix fosters innovation and adaptability while ensuring efficient time management and decision-making.
For example, during my PhD project, when the COVID-19 pandemic hit, both Agile and the Eisenhower Matrix proved invaluable. The sudden shift to remote work disrupted normal research activities and access to physical infrastructure, which required immediate adjustments. Agile allowed me to break down the larger project into smaller, more manageable tasks that could be completed remotely, such as data analysis and model refinement. Regular feedback loops with supervisors via virtual meetings kept the project on track, ensuring we stayed aligned with evolving goals despite the limitations posed by the pandemic.
The Eisenhower Matrix helped me prioritise urgent tasks, such as adapting experiments to work within the constraints of a remote environment, while postponing non-essential fieldwork until conditions allowed. This combination of Agile’s adaptability and the Eisenhower Matrix’s prioritisation allowed me to remain productive during a period of unprecedented uncertainty, ensuring that key milestones were achieved and the project continued to progress despite the challenges. By maintaining a clear focus on urgent and important tasks while staying flexible and open to iterative improvements, I was able to successfully navigate the difficulties brought on by COVID-19, ensuring the continued success of my research.
The first major PhD project involved developing a hybrid model combining advanced modeling techniques, RB space, and a data-driven approach using deep learning. The goal was to create an accurate and efficient DT framework for real-time SHM. This project focused on integrating:
The second PhD project involved designing and implementing a cost-effective Printed Circuit Board (PCB) for IoT-based SHM.