Pang Du

Pang Du is a Professor in the Department of Statistics at Virginia Tech. He joined the university in 2006 after completing his PhD in Statistics at Purdue University. His research focuses on functional data analysis, nonparametric smoothing, high-dimensional data analytics, and ROC curve methodology. He has collaborated with researchers across engineering, public health, computer science, and biology.

Education:

• PhD in Statistics, Purdue University (2006)
• MS in Statistics, Purdue University (2003)
• MA in Mathematics, Johns Hopkins University (2002)
• MSE in Computer Science, Johns Hopkins University (2002)
• MS in Mathematics, University of Science and Technology of China (1999)
• BS in Mathematics, University of Science and Technology of China (1996)

Research Interests:

• Functional data analysis and nonparametric smoothing
• High-dimensional data and statistical learning
• Lifetime data analysis and survival models
• Diagnostic test data and ROC curve methodology

Recent Trends in Publications: Recent work emphasizes functional data analysis applications (e.g., degradation modeling in engineering systems), nonparametric methods for survival analysis, and statistical algorithms for biomedical data. He has developed methods for smoothing splines, partial likelihood estimation, and functional clustering.

Grants & Advising: Advised over 10 PhD students, including Zhiyuan Du (AbbVie Inc.) and Quyen Do (Corning Inc.). His research is supported by grants from the National Science Foundation and industry collaborations.

Labs/Teams: Leads the Functional Data Analysis research group at Virginia Tech, focusing on methodological advancements in functional regression and statistical learning.