Sandrine Dudoit

Research Bio

Sandrine Dudoit is a statistician and data scientist whose research focuses on the development and application of statistical learning methods and software for the analysis of high-throughput -omic data in both basic biology and precision health. Her methodological interests regard high-dimensional statistical learning and include exploratory data analysis (EDA), unsupervised learning (e.g., cluster analysis, dimensionality reduction), loss-based inference with cross-validation (e.g., density estimation, classification, regression, model selection), and causal inference. She is also interested in statistical computing and reproducible research, and is a founding core developer of the Bioconductor Project.

Sandrine Dudoit is Professor of Statistics and Biostatistics/School of Public Health at UC Berkeley and a Core Faculty Member of the Center for Computational Biology. At Berkeley, she teaches and mentors students in applied statistics, data science, and computational biology. 

 

Research Expertise and Interest

statistics, machine learning, data science, applied statistics, statistical computing, computational biology, computational genomics, precision medicine, precision health

In the News

Neuroscientists roll out first comprehensive atlas of brain cells

When you clicked to read this story, a band of cells across the top of your brain sent signals down your spine and out to your hand to tell the muscles in your index finger to press down with just the right amount of pressure to activate your mouse or track pad. A slew of new studies now shows that the area of the brain responsible for initiating this action — the primary motor cortex, which controls movement — has as many as 116 different types of cells that work together to make this happen.
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