I am writing to express my interest in attending the second Hong Kong Laureate Forum. As a Research Fellow focused on probabilistic risk analysis for nuclear reactor safety, and machine-learning approaches for uncertainty quantification, I am excited to engage with participating Laureates and the fellow Forum participants to exchange ideas across different disciplines in solving the current global challenges.

My work in risk and uncertainty analysis towards nuclear safety has strengthened my commitment towards tackling global issues of climate, energy, and industrial infrastructure sustainability. The Forum’s multi-disciplinary approach presents a unique opportunity to explore how groundbreaking research can address the aforementioned issues. I am particularly excited about the chance to collaborate with like-minded individuals across diverse backgrounds.

The knowledge and networks I hope to gain from this experience will be invaluable as I continue my work to develop robust risk assessment methods to quantify the reliability of the nuclear reactor systems under limited data. This contributes to the wider government efforts in assessing the feasibility of nuclear energy for Singapore. I am confident that my passion for the Mathematical Sciences and Artificial Intelligence, and my desire to contribute innovative solutions would allow me to add value to the Forum’s discussions.

National University of Singapore / Singapore Nuclear Research and Safety Institute

National University of Singapore / Department of Physics

My current research looks at developing an uncertainty quantification framework enabling the analyst to compute statistically calibrated confidence bounds of the reliability of coherent systems under limited data. More specifically, we propose to use confidence boxes to do so. Such proposal is motivated by the fact that confidence boxes offer a guarantee on statistical performances regardless of the amount of available data through repeated use, which is especially useful when considering that the reliability or failure data for the reliability analysis are often limited in availability.

The aim of the research work is to provide tools allowing the analyst to obtain the true confidence intervals over the system failure and reliability at any desired level. The result of the research is a mathematical formalism which relates the component configurations with the corresponding Boolean logic expressions to perform the forward propagation of the confidence boxes under varying dependencies between the components. The feasibility of the proposed framework is then demonstrated on the TRIGA nuclear research reactor cooling system to perform a probabilistic risk assessment.

The work is done in collaboration with the Thailand Institute of Nuclear Technologies as part of the wider ASEAN Network on Nuclear Power and Safety Research initiative.