CAREER: New Statistical Methodology and Theory for Mining High-Dimensional Data

The area of high-dimensional modeling is developing rapidly. This research aims to push these developments forward to meet new challenges arising in different fields. In particular, the investigator studies (a). new statistical methodology and theory for mapping high-dimensional datasets onto a space with much-lower dimensions while assuring minimum distortion; (b). efficient and robust variable selection in semiparametic models; (c). a novel regularization approach to nonparametric model selection and estimation.

Modern computing power and scientific innovations allow scientists to easily collect high-dimensional data in various disciplines. Analysis of high-dimensional data poses many challenges and offers great opportunities to statisticians. The availability of high-dimensional data has reshaped statistical modeling. This proposal focuses on new statistical methodology and theory for knowledge discovery and information retrieval from high-dimensional data. The investigator plans to develop User-friendly computer programs for public use. The research will make significant contributions to areas outside statistics as well, including biology, computer science, biomedical engineering, medical informatics, economics, and so on. The integrated educational program includes substantial initiatives that will involve undergraduate and graduate students and expose them to state-of-the-art research in the topics related to the proposal. These include new courses, workshops and mentoring. The research results will be integrated into K-12 education and be applied to industrial research.