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 at UC Berkeley. She obtained her BS Degree in Mathematics from Peking University, and MS and PhD Degrees in Statistics from UC Berkeley. She was 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. She 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 the Flatiron Institute in NYC. She was Chair of the Department of Statistics at UC Berkeley

She has published more than 170 publications in premier venues and these papers not only investigate a wide range of research topics from practice to algorithms and to theory, but also seek deep insights. The breadth 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.

Currently, she is championing collaborative research with experts in the subject knowledge and leading research in statistical machine learning (e.g. boosting, sparse modeling, kernel methods, and spectral clustering and decision trees and deep learning) and causal inference to design algorithms such as iterative random forests (iRF), agglomerative contextual decomposition (ACD) and adaptive wavelet distillation (AWD) for interpreting deep neural networks and X-learner for heterogeneous treatment effect estimation in causal inference.

She is a Member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is 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. She has been selected to deliver the Wald Memorial Lectures of IMS at JSM in 2023.

Previously, she 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).

She 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 recently served on the scientific advisory committee for the IAS Special Year on optimization, statistics and theoretical machine learning, and the inaugural scientific advisory committees of the UK Turing Institute for Data Science and AI. She is serving on the editorial board of PNAS, the Scientific Advisory Boards of Canadian Statistical Sciences Institute (CANSSI), of the AI Policy Hub at UC Berkeley, and the Scientific Advisory Committee of the Department of Quantitative and Computational Biology at USC.