My research spans the areas of trustworthiness assurance, reliability engineering, and risk management of AI-powered intelligent systems through data analytics, uncertainty quantification, and optimization methods, where the uncertainties arising from the limited knowledge, data scarcity and natural variability are considered in the formulated models. The ultimate goal of my research is to safeguard the adoptions and deployment of AI-enabled intelligent systems in high-impact decision settings, enhance their trustworthiness and reliability against various sources of risks, such as data variability, model limitations, data noise, and increase the depth and breadth of adopting AI in mission-critical applications for societal benefit. 

The investigation along these fronts will help us better understand how to prevent the escalation of the initiating events at their early stages, mitigate the catastrophic consequence of such events if they already happened, and restore system performance quickly in the aftermath of unanticipated disastrous events, thereby enhancing the safety and efficiency of many engineering systems. Explore these pages to learn more about my research, ongoing projects, publications, job opportunities, and more.