EECS
Joint Colloquium Distinguished Lecture Series |
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Bin Yu UC Berkeley, Dept. of Statistics |
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Embracing Statistical Challenges in the Information Technology Age | ||||

Abstract: |
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Information technology advances are making data collection possible in most if not all fields of science and engineering and beyond. Statistics as a scientific discipline is challenged and enriched by the new opportunities resulted from these high-dimensional data sets. In this talk, I will use serveral research projects to demonstrate how these IT challenges are met by finding new applications of traditional statistical thinking and methods and by incorporating compression and computation considerations into statistical estimation. In particluar, I will cover cloud detection over the polar region, microarray image compression for statistical analysis, and L2 boosting as a computationally efficient method for sparse nonparametric regression model fitting. |
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Biography: | ||||

Bin Yu is Professor of Statistics at University of California Berkeley, where she teaches and conducts research in statistics, information theory/communications, bioinformatics and remote sensing. She received her B.S. degree in Mathematics from Peking University, China in 1984 and M.S. and Ph.D. degrees in Statistics from the University of California at Berkeley in 1987 and 1990 respectively. Her doctoral research was on empirical processes for dependent data and Minimum Description Length (MDL) Principle. She was an Assistant Professor of Statistics at the University of Wisconsin at Madison from 1990 to 1992, a Postdoctoral Fellow at the Mathematical Science Research Institute at Berkeley in Fall 1991, and a Visiting Assistant Professor of Statistics at Yale University in Spring 1993. From 1992 to 1997, and 1997-2001, she was Assistant Professor, and Associate Professor, of Statistics at Berkeley. For 1998-2000, she was Member of Technical Staff at the Math Center, Bell Labs, Lucent Technologies. |