Citation: For fundamental contributions to information theory, statistical and machine learning methodology; interdisciplinary research in fields such as genomics, neuroscience, remote sensing and document summarization; and for outstanding dedication to professional service, leadership, and mentoring of students and young scholars.

Haiyan Huang, Kathryn Roeder, and Jianwen Cai write:

The Committee of Presidents of Statistical Societies (COPSS) has selected Bin Yu from the University at California (UC) at Berkeley for the 2023 Distinguished Achievement Award and Lectureship (DAAL). Formerly known as the R.A. Fisher Award and Lectureship, the DAAL recognizes meritorious achievement and scholarship in statistical science and recognizes the highly significant impact of statistical methods on scientific investigations. She will deliver the DAAL Lecture at the 2023 Joint Statistical Meetings (JSM) in Toronto on veridical data science.

Yu’s current research focuses on practice, algorithm, and theory of statistical machine learning, interpretable machine learning, and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. She and her group have developed the PCS framework for veridical data science towards responsible, reliable, and transparent data analysis and decision-making. PCS stands for predictability, computability and stability. It unifies, streamlines, and expands on ideas and best practices of machine learning and statistics to uncover and address a hidden universe of uncertainties well beyond sample–sample uncertainty in a data science life cycle.

In the past, she jointly 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. She also co-developed a fast and well-validated Arctic cloud detection algorithm. Her collaborative paper in 2011 with the Gallant Lab at Berkeley on movie reconstruction from fMRI brain signals received extensive and intensive coverages by numerous media outlets, including The Economist, Forbes, Der Spiegel, Daily Mail, New Scientist and Massachusetts Institute of Technology (MIT) Technology Review. This work was named one of the best 50 inventions in 2011 by Time magazine. She and collaborators mapped a cell’s destiny in Drosophila via stability-driven NMF, and used the PCS framework to stress-test or internally validate clinical decision rules used in the ER. Previously, she pioneered Vapnik–Chervonenkis (VC) type theory needed for asymptotic analysis of time series and spatio-temporal processes. She made fundamental contributions to information theory and statistics through work on minimum description length (MDL) and entropy estimation. Recently, she and her collaborators developed iterative random forests (iRF), X-learner for heterogeneous treatment effect estimation in causal inference, hierarchical shrinkage (HS) decision trees, and Fast and interpretable greedy trees (FIGS).

She is the Class of 1936 Second Chair in the College of Letters and Science and Chancellor’s Distinguished Professor, Departments of Statistics and Electrical Engineering & Computer Sciences, and Center for Computational Biology, at the University of California at Berkeley. She obtained her BS in Mathematics from Peking University and her MS and PhD in Statistics from UC Berkeley. She was an Assistant Professor at University of Wisconsin–Madison, Visiting Assistant Professor at Yale University, a member of the Technical Staff at Lucent Bell Labs, and a Miller Research Professor at Berkeley. She was a Visiting Faculty at MIT, ETH, Poincare Institute, Peking University, INRIA-Paris, Fields Institute at the University of Toronto, Newton Institute at Cambridge University, and the Flatiron Institute in New York City. She has also served as Chair of the Department of Statistics at UC Berkeley and had a crucial role in envisioning the intellectual and organizational vision for the Division of Computing, Data Science, and Society (CDSS) at UC Berkeley as a faculty advisory committee member.

Yu is a Member of the US National Academy of Sciences and the American Academy of Arts and Sciences. She was President of the IMS in 2013–14, a Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, and IMS Rietz Lecturer. In 2018, Yu was awarded the Elizabeth L. Scott Award by COPSS for principled leadership in the international scientific community; for commitment and actions towards diversity, equity, and inclusion; for consistently mentoring and encouraging women students and new researchers in statistics and data science; and for scientific contributions to statistical and machine learning methodology at the highest scholarly level. She holds an Honorary Doctorate from The University of Lausanne, and served on the inaugural scientific advisory board of the UK Turing Institute of Data Science and AI. She is serving on the editorial board of PNAS and as a senior advisor at Simons Institute for the Theory of Computing at Berkeley.

Bin Yu will also give this year’s IMS Wald Memorial Lectures at JSM in 2023 [see preview here].