Sandrine Dudoit is a professor in the Department of Statistics and the School of Public Health’s Division of Biostatistics and a member of the Center for Computational Biology at UC Berkeley. She served as chair of the Department of Statistics from July 2019 to June 2022. Dudoit’s methodological research interests regard high-dimensional statistical learning and include exploratory data analysis, visualization, loss-based estimation with cross-validation (e.g., density estimation, classification, regression, model selection), and multiple hypothesis testing. Much of her methodological work is motivated by statistical questions arising in biological and medical research, such as the design and analysis of high-throughput sequencing studies. Her current projects focus on single-cell transcriptome sequencing (RNA-Seq) for discovering novel cell types and the study of stem cell differentiation. Her contributions include: exploratory data analysis, normalization and expression quantitation, differential expression analysis, class discovery and prediction, inference of cell lineages, and the integration of biological annotation metadata (e.g., Gene Ontology annotation). She is also interested in statistical computing and computationally reproducible research. She is a founding core developer of the Bioconductor Project, an open-source and open-development software project for the rigorous and reproducible analysis of data from current and emerging biological assays.

Dudoit obtained a bachelor’s degree and master’s degree in mathematics from Carleton University in Ottawa, Canada. She first came to UC Berkeley as a graduate student advised by Terry Speed and earned a PhD degree in 1999 from the Department of Statistics. She was a postdoctoral fellow in Pat Brown’s laboratory at Stanford’s biochemistry department from 1999 to 2001.