Hayian Huang, Kathryn Roeder, and Jianwen Cai

Bin Yu

The Committee of Presidents of Statistical Societies selected Bin Yu from the University at California, Berkeley for the 2023 Distinguished Achievement Award and Lectureship, which recognizes meritorious achievement and scholarship in statistical science and the significant impact of statistical methods on scientific investigations. She will deliver the lecture on veridical data science at the 2023 Joint Statistical Meetings in Toronto.

Yu’s 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 (predictability, computability, and stability) framework for veridical data science toward responsible, reliable, and transparent data analysis and decision-making. PCS 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, Yu 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 coverage by numerous media outlets, including The Economist, Forbes, Der Spiegel, Daily Mail, New Scientist, and Technology Review. This work was named one of the best 50 inventions in 2011 by Time magazine.

Yu and collaborators also mapped a cell’s destiny in Drosophila via stability-driven nonnegative matrix factorization and used the PCS framework to stress test or internally validate clinical decision rules used in the emergency room.

Additionally, Yu pioneered Vapnik-Chervonenkis–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 and entropy estimation. Recently, she and her collaborators developed iterative random forests, X-learner for heterogeneous treatment effect estimation in causal inference, hierarchical shrinkage decision trees, and fast and interpretable greedy trees.

Yu is chancellor’s distinguished professor in the departments of statistics and electrical engineering and computer sciences and Center for Computational Biology at the University of California, Berkeley. She earned her BS in mathematics from Peking University and her MS and PhD in statistics from UC Berkeley. She was an assistant professor at the 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 UC Berkeley. She was also visiting faculty at MIT, ETH, Poincare Institute, Peking University, INRIA-Paris, Fields Institute at the University of Toronto, Newton Institute at Cambridge University, and Flatiron Institute in New York City. She also served as chair of the department of statistics at UC Berkeley and had a crucial role in the intellectual and organizational vision for the UC Berkeley Division of Computing, Data Science, and Society as a faculty advisory committee member.

Yu is a member of the National Academy of Sciences and American Academy of Arts and Sciences. She was president of the Institute of Mathematical Statistics from 2013–2014, Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, and Rietz Lecturer of the Institute of Mathematical Statistics. In 2018, COPSS awarded the Elizabeth L. Scott Award to Yu. 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 Proceedings of the National Academy of Sciences editorial board and is senior adviser at Simons Institute for the Theory of Computing at Berkeley. She will also give the Wald memorial lectures at JSM in Toronto.