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QnAs with Bin Yu
PNAS ( IF 9.580 ) Pub Date : 2020-02-12 , DOI: 10.1073/pnas.2001302117
Farooq Ahmed

The explosion of available data in the past decades has birthed a myriad of statistical and machine-learning tools. These tools have allowed scientists from fields as disparate as genomics and cosmology to model and interpret data, draw conclusions, and move science forward. Building on computational advances and increased data availability, data science has emerged as a platform that integrates statistics, computer science, and other disciplines. It has now found commonplace usage, often by those untrained in the underlying statistics, methods, and algorithms. University of California, Berkeley professor Bin Yu trained in statistics but was driven to leverage new computational developments, including machine learning, to solve important scientific problems. The Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and electrical engineering and computer sciences at Berkeley, Yu seeks to formalize the principles of data science while making it more accessible to researchers from other fields. In her Inaugural Article (1), Yu lays out a framework called PCS, which stands for the three principles of data science—predictability, computability, and stability—to guide those who solve domain data problems with data science tools. To do so, Yu leveraged her experience solving data problems across fields such as …
更新日期:2020-02-13

 

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