Bin Yu

University of California, Berkeley

Primary Section: 32, Applied Mathematical Sciences
Secondary Section: 34, Computer and Information Sciences
Membership Type:
Member (elected 2014)


Bin Yu is Chancellor’s Professor in the Departments of Statistics and Electrical Engineering & Computer Science at the University of California, Berkeley. Yu is a statistician and data scientist recognized for her work on statistical inference, machine learning, and interdisciplinary research. She is known particularly for her work in empirical processes for dependent data, information theory, signal processing, high dimensional statistical inference, boosting, and sparse modeling (aka compressed sensing), computational neuroscience, and remote sensing. Yu was born in Harbin, China in 1963. She graduated from Peking University with a degree in mathematics and from the University of California-Berkeley in 1987 and 1990, respectively with MA and PhD degrees. She was an Assistant Professor of Statistics at the University of Wisconsin-Madison from 1990-1993 and returned in 1993 to Berkeley Statistics where she was Chair from 2009 to 2012. She was a Member of Technical Staff at Bell Labs-Lucent from 1998 to 2000 while on leave from Berkeley. She was a Guggenheim Fellow and the President of the Institute of Mathematical Statistics (IMS), and is a member of both the National Academy of Sciences and the American Academy of Arts and Sciences.

Research Interests

Bin Yu’s group is interested in extracting meaningful and useful information from wide-ranging data sources including neuroscience, systems biology, remote sensing, and signal processing. Acquiring subject knowledge relevant to data and in collaboration with scientists, they use skills of critical thinking, computation, and mathematics to achieve their goals. They develop statistical and machine learning theories to understand empirically effective and computationally attractive methods such as EM, Boosting, Lasso, and Spectral Clustering. They develop fast algorithms to deal with large-scale data. In particular, in collaboration with scientists and via data analysis, they are keen to understand primate visual pathway, with breakthroughs on reconstructing movies from fMRI brain signals; they are keen to answer fundamental questions in systems biology such as “how do organs form?” with embryonic images of fruit-fly; they devise effective algorithms to detect clouds in the arctic regions with remote sensing data and provide inputs to climate models, and to retrieve aerosol index to monitor air-quality in heavily polluted areas; and they design a simple and useful spatially adaptive wavelet method to remove noise from images, and a low-complexity low-delay perceptually lossless audio coder to compress mixed sound signals of speech and music.

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