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Current research topics of my group cover sparse modeling (e.g. Lasso), structured sparsity (e.g. hierarchical and group and graph path), analysis and methods for spectral clustering for undirected and directed graphs; and our data problems come from diverse interdisciplinary areas including genomics, neuroscience, remote sensing, document ...

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Departments of Statistics and EECS, University of California at Berkeley, Berkeley, CA 94720, USA. E-mail: [email protected] Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, reproducibility manifests itself in ...

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University of California, Berkeley Chan-Zuckerberg Biohub Investigator mail: 367 Evans Hall #3860 • Berkeley, CA 94720 • phone: 510-642-2021 • fax: 510-642-7892

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Research Expertise and Interest. machine learning, trustworthy data science and AI, interdisciplinary research in biomedicine, neuroscience, and climate science.. Research Description. Bin Yu is the Class of 1936 Second Chair in the College of Letters and Science and a Chancellor's distinguished professor in the Department of Statistics, EECS and …

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d Department of Statistics, University of California, Berkeley, CA 94720-3860, United States ABSTRACT Detection of clouds in satellite-generated radiance images, including those from MODIS, is an important first step in many applications of these data. In this paper we apply spectral unmixing to this problem with the aim of estimating

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She obtained her B.S. degree in Mathematics from Peking University in 1984, her M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley in 1987 and 1990, respectively. She held faculty positions at the University of Wisconsin-Madison and Yale University and was a Member of Technical Staff at Bell Labs, Lucent.

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Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Departments of statistics and EECS, and Center for Computational Biology, and serves as a senior advisor at the Simons Institute for the Theory of Computing, all at UC Berkeley. She obtained her BS Degree in

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Current research topics of my group cover sparse modeling (e.g. Lasso), structured sparsity (e.g. hierarchical and group and graph path), analysis and methods for spectral …

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Submitted to the Annals of Statistics DATA SPECTROSCOPY: EIGENSPACES OF CONVOLUTION OPERATORS AND CLUSTERING By Tao Shi∗, Mikhail Belkin† and Bin Yu‡ The Ohio State University∗† and University of California, Berkeley‡ This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples.

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closely related to non-parametric spectral methods, such as spectral clustering (e.g., [8]) and Kernel Prin-cipal Components Analysis [11]. Those methods, as well as certain methods in manifold learning (e.g., [1]), construct a kernel matrix or a graph Laplacian ma-trix associated to a data set. The eigenvectors and

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Statistics at UC Berkeley. We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology ...

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