Bin Yu

Research Areas

Signal Processing (SP), Machine learning, Remote Sensing, Neuroscience

Research Centers

Berkeley Artificial Intelligence Research Lab (BAIR)
Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB)
Simons Institute for the Theory of Computing (SITC)

Biography

Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley. Her current research interests focus on statistics and machine learning theory, methodologies, and algorithms for solving high-dimensional data problems. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and remote sensing.

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. She was Chair of Department of Statistics at UC Berkeley from 2009 to 2012, and is a founding co-director of the Microsoft Lab on Statistics and Information Technology at Peking University, China, and Chair of the Scientific Advisory Committee of the Statistical Science Center at Peking University.

She is Member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an Invited Speaker at ICIAM in 2011, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014, and will be the Rietz Lecturer of IMS in 2016. She is a Fellow of IMS, ASA, AAAS and IEEE.

She served on the Board of Mathematics Sciences and Applications (BMSA) of NAS and as co-chair of SAMSI advisory committee. She is serving on the Board of Trustees at ICERM and Scientific Advisory Board of IPAM. She has served or is serving on numerous editorial boards, including Journal of Machine Learning Research (JMLR), Annals of Statistics, and Journal of American Statistical Association (JASA).