Network Data: Statistical Theory and New Models.

摘要

During this period of review, Bin Yu worked on many thrusts of high-dimensional statistical theory and methodologies. Her research covered a wide range of topics in statistics including analysis and methods for spectral clustering for sparse and structured networks [2,7,8,21], sparse modeling (e.g. Lasso) [4,10,11,17,18,19], statistical guarantees for the EM algorithm [3], statistical analysis of algorithm leveraging for solving big data problems [5], causal network modeling [15,20], stability as a general concept/framework for reproducible statistical discovery [9,13], and high-dimensional inference [12]. Yu also collaborated with other research groups and Labs to conduct interdisciplinary research in areas including systems biology, neuroscience, remote sensing, document summarization, and social networks. For example, she has been collaborating with Dr. Frise et al. on constructing gene-gene interaction networks [1], with the Gallant Lab on understanding visual pathway of primates by using sparse coding [14], and with environmental scientists at JPL and Emory University to retrieval from NASA MISR remote sensing images aerosol index AOD for air pollution monitoring and management [6,16].