Bin Yu

Research Expertise and Interest

machine learning, trustworthy data science and AI, interdisciplinary research in biomedicine, neuroscience, and climate science.

Research Description

Bin Yu is the Class of 1936 Second Chair in the College of Letters and Science and a Chancellor's distinguished professor in the Department of Statistics, EECS and Center for Computational Biology. She is also a senior advisor at the Simons Institute for Theory of Computing.  Her current research has focused on the practice and theory of machine learning and solving interdisciplinary data problems in neuroscience, genomics, and precision medicine (e.g. in ER and cardiology). The Yu group has developed machine learning algorithms such as next-generation and interpretable tree-based methods (e.g. iterative random forests (iRF), fast and greedy sums of trees (FIGS), HS, and MDI+), stability-driven NMF, and adaptive wavelet distillation (AWD) from deep learning models. 

In the News

When Data Science Meets Medicine

Bin Yu, a 2022 Fellow with the UC Noyce Initiative, is applying cutting edge data science techniques to pressing issues in health and medicine.

Getting the right equipment to the right people

Hospitals are suffering from an acute shortage of emergency medical supplies, including masks, gowns, gloves and ventilators. However, the medical industry is struggling to determine the places that need them the most. Bin Yu, a professor of statistics and of electrical engineering and computer sciences, is working with nonprofit organization Response4Life to connect suppliers with hospitals in need.

Seeking Data Wisdom

Bin Yu’s statistical strategies work hand in hand with intense computation to penetrate storms of data.

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