Chancellor’s Distinguished Professor and Class of 1936 Second Chair Departments of Statistics and Electrical Engineering and Computer Sciences at UC Berkeley. Ph.D. - UC Berkeley.
Used books in courses she has taught
Professor McKeown has used books in the following categories in courses she has taught:
🟡 Information Theory
References: , 
Bin Yu obtained her BS degree in mathematics from Peking University, and her MS and PhD degrees in statistics from UC Berkeley. She was an assistant professor at UW-Madison, visiting assistant professor at Yale University, member of technical staff at Lucent Bell-Labs, and Miller Research Professor at Berkeley in 2004 and 2015, respectively. She also was a visiting faculty at MIT, ETH, Poincare Institute, Peking University, INRIA-Paris, Fields Institute at University of Toronto, Newton Institute at Cambridge University, and Flatiron Institute. She is a past chair of department of statistics at UC Berkeley.
Professor Yu is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She is a Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner.
Professor Yu championed for collaborative research with experts in the subject knowledge and led research in statistical machine learning (e.g. boosting, sparse modeling, kernel methods, and spectral clustering) and causal inference (e.g. X-learner) through theoretical analysis and practical fast algorithms.
Her research papers not only investigated a wide range of research topics from practice to algorithms and to theory, but also sought deep insights. The breath and depth of her research experience enabled unique and novel solutions to interdisciplinary data problems in audio and image compression, network tomography, remote sensing, neuroscience, genomics, and precision medicine.
Professor Yu pioneered Vapnik-Chervonenkis (VC) type theory needed for asymptotic analysis of time series and spatio-temporal processes, and made fundamental contributions to information theory and statistics through work on minimum description length (MDL) and entropy estimation. With her students and collaborators, she developed a highly cited spatially adaptive wavelet image denoising method and a low-complexity low-delay perceptually lossless audio coder that was incorporated in Bose wireless speakers, and developed a fast and well-validated Arctic cloud detection algorithm using NASA’s MISR data. With the Jack Gallant Lab and her students, she developed predictive models of fMRI brain activity in vision neuroscience that made “mind-reading” possible (or reconstruction of movies using only fMRI signals).
Professor Yu served on editorial boards including Annals of Statistics, Journal of American Statistical Association, and Journal of Machine Learning Research. Her leadership roles included co-chairing the National Scientific Committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), and serving on the scientific advisory committee of SAMSI and IPAM, and on the board of trustees of ICERM and the Board of Governors of IEEE-IT Society. She served on the scientific advisory committee for the IAS Special Year on optimization, statistics and theoretical machine learning. She is serving on the editorial board of PNAS and the scientific advisory committee of the UK Turing Institute for Data Science and AI.
- IMS Fellow (1999)
- IEEE Fellow (2001)
- ASA Fellow (2005)
- AAAS Fellow (2013)
- Member of NAS (2014)
- Elizabeth L. Scott Award (2018)
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