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Bin Yu is a distinguished statistician and data scientist who develops novel and interpretable machine learning methods for neuroscience, genomics and precision medicine. He is the head of the Yu Group at Berkeley and a Chan-Zuckerberg Biohub Investigator.
Bin Yu (Chinese: 郁彬) is a Chinese-American statistician. She is currently Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California, Berkeley.
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.
2000. The minimum description length principle in coding and modeling. A Barron, J Rissanen, B Yu. IEEE transactions on information theory 44 (6), 2743-2760. , 1998. 1394. 1998. Definitions, methods, and applications in interpretable machine learning. WJ Murdoch, C Singh, K Kumbier, R Abbasi-Asl, B Yu.
Bin Yu is a leading researcher in statistical machine learning and causal inference, with applications in genomics, neuroscience, and text summarization. He is a member of the U.S. National Academy of Sciences and a fellow of the American Academy of Arts and Sciences.
Bin Yu is a professor of statistics and electrical engineering at UC Berkeley, where she develops novel statistical machine learning approaches and frameworks for data science. She also leads interdisciplinary projects in neuroscience, genomics and precision medicine, and advocates for veridical data science based on predictability, computability and stability.
Bin Yu is a renowned statistician and data scientist who works on interdisciplinary problems in audio, image, network, genomics, and causal inference. She is a member of the U.S. National Academy of Sciences and the American Academy of Arts and Sciences, and has received many awards and honors for her research.
