Sandrine Dudoit

Sandrine Dudoit, PhD
Department of Statistics
Division of Biostatistics, School of Public Health
University of California, Berkeley

101 Haviland Hall, #7358
Berkeley, CA 94720-7358
Fax: (510) 643-5163

N.B. Prospective graduate students. Please take the time to read carefully the information on the program website before contacting us. You will find answers to most of your questions regarding application procedures, admission criteria, degree requirements, and financial support on this website. If you still have questions regarding administrative matters, please contact


NIH Brain Initiative award: A Comprehensive Whole-Brain Atlas of Cell Types in the Mouse
NIH press releases: NIH BRAIN Initiative builds on early advances, NIH BRAIN Initiative launches cell census
UC Berkeley press releasse: $65.5 million from NIH to create brain atlas

STAT 278B -- Statistics and Genomics Seminar

PB HLTH C240F/STAT C245F -- Statistical Genomics II

Recent Publications

Recent Software

Recent Presentations

Research and Teaching Activities

My research and teaching activities concern the development and application of statistical methods and software for the analysis of biomedical and genomic data.

Statistical methodology. My methodological research interests regard high-dimensional inference and include exploratory data analysis (EDA), visualization, loss-based estimation with cross-validation (e.g., density estimation, regression, model selection), and multiple hypothesis testing.

Applications to biomedical and genomic research. Much of my methodological work is motivated by statistical inference questions arising in biological research and, in particular, the design and analysis of high-throughput microarray and sequencing gene expression experiments, e.g., single-cell transcriptome sequencing (RNA-Seq) for discovering novel cell types and for the study of stem cell differentiation. My contributions include: exploratory data analysis, normalization and expression quantitation, differential expression analysis, class discovery, prediction, cell lineage inference, integration of biological annotation metadata (e.g., Gene Ontology (GO) annotation).

Statistical computing. I am also interested in statistical computing and, in particular, reproducible research. I am a founding core developer of the Bioconductor Project (, an open-source and open-development software project for the analysis of biomedical and genomic data.

Curriculum Vitae and Biography

Curriculum Vitae: [PDF]

Biography: [PDF]


Employment Opportunities