CAREER: A Foundation for Unsupervised Learning of High-Dimensional Data

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This research aims to develop theory, algorithms, and interactive visualization tools for mining a variety of high-dimensional data stemming from real world application areas, ranging from medical images to satellite data. The research aims to develop a unified framework for unsupervised feature selection, identify and characterize clustering criteria for different clustering objectives, create new similarity metrics that reflect the spatial and temporal nature of data in specific domains (e.g., earth science images), define measures of feature relevance/irrelevance, and incorporate feature selection into hierarchical clustering methods. The new algorithms developed will benefit a variety of application domains; in particular, it will directly aid physicians studying the severity of emphysema and cystic fibrosis lung diseases, and help scientists discover interesting patterns of the earth surface. This project integrates research and education by providing hands-on research experiences to both undergraduate and graduate students in the classroom and in the lab. Moreover, the PI plans to work with the Society of Women Engineers and the Connections program to inspire female high school students to pursue careers in engineering and computer science and to insure that under-represented groups are involved in this research.