Research Interests
- Reliability Analysis; Reliability of Artificial Intelligence Systems; Engineering Statistics
- Machine Learning; AI-Powered Statistics and Data Science; Statistical Computing
- Survival Analysis; Longitudinal Data Analysis; Spatial Data Analysis; Biostatistics
Research Overview
- My research focuses on developing statistical methodology and computational tools for problems in reliability, engineering, and data science. My research on reliability analysis includes lifetime and degradation data analysis, accelerated testing, recurrent events, reliability prediction, and the analysis of complex reliability data. My work has addressed modern reliability problems involving large-scale data, dynamic covariates, complex dependence structures, and machine learning.
- A recent direction of my research is the reliability of artificial intelligence (AI) systems. I develop statistical methods and frameworks for defining, modeling, testing, and assessing AI reliability, with applications to autonomous systems, machine learning algorithms, and other AI-enabled systems. This research connects classical reliability principles with emerging challenges in AI, including robustness, failure mechanisms, reliability data collection, and test planning.
- I am also interested in the broader integration of AI, machine learning, and statistics. This line of research investigates the capabilities and limitations of large language models for statistical analysis and explores how LLMs and AI agents can be used to enhance statistical computing and data analysis. This emerging area of AI-powered statistics and data science seeks to combine the capabilities of modern AI with rigorous statistical reasoning, uncertainty quantification, and verification.
- My research also includes methodological work in survival analysis, longitudinal and spatial data analysis, statistical computing, and biostatistics. These methods have been motivated by applications in transportation, manufacturing, materials and renewable energy, high-performance computing, healthcare, and other engineering and scientific domains.
Research Grants
- My research program has been supported by a broad portfolio of federal, state, university, and industry funding, with projects spanning statistical methodology, reliability and risk analysis, artificial intelligence and machine learning, high-performance computing, and interdisciplinary applications in transportation, manufacturing, healthcare, and cyber-physical systems.
- I have served as PI, Co-PI, or Co-Investigator on more than 20 funded projects, including awards from the National Science Foundation (NSF), Federal Railroad Administration (FRA), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Commonwealth Cyber Initiative (CCI), DuPont, and Virginia Tech. Collectively, these projects represent more than $4 million in sponsored funding.
- Recent projects focus particularly on AI-powered statistical analysis, AI reliability and robustness, and Bayesian methods for reliability and risk assessment. Please see my CV for a detailed list of funded projects and awards.