Department of Statistics
- Professor of the Division of Biostatistics
- Senior Fellow Berkeley Institute for Data Science
- Member of the Center for Computational Biology
Sandrine Dudoit is a Professor of Biostatistics and Statistics. Her work is motivated by statistical inference questions arising in biological and medical research. Recent projects include the development of statistical methods and software for discovering novel cell types and for the study of stem cell differentiation using single-cell transcriptome sequencing (RNA-Seq).
Sandrine Dudoit is a Professor in the Department of Statistics and the Division of Biostatistics at the University of California, Berkeley. Professor Dudoit’s methodological research interests regard high-dimensional inference and include exploratory data analysis (EDA), visualization, loss-based inference with cross-validation (e.g., density estimation, classification, regression, model selection), and multiple hypothesis testing. Much of her methodological work is motivated by statistical inference questions arising in biological and medical 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. Her contributions include: exploratory data analysis, normalization and expression quantitation, differential expression analysis, class discovery, prediction, inference of cell lineages, integration of biological annotation metadata (e.g., Gene Ontology (GO) annotation). She is also interested in statistical computing and, in particular, computationally reproducible research.
Professor Dudoit is a founding core developer of the Bioconductor Project. She is a co-author of the book Multiple Testing Procedures with Applications to Genomics and a co-editor of the book Bioinformatics and Computational Biology Solutions Using R and Bioconductor. She is Associate Editor of three journals, including The Annals of Applied Statistics and IEEE/ACM Transactions on Computational Biology and Bioinformatics. Professor Dudoit was named Fellow of the American Statistical Association in 2010 and Elected Member of the International Statistical Institute in 2014.
Professor Dudoit obtained a Bachelor’s degree (1992) and a Master’s degree (1994) in Mathematics from Carleton University, Ottawa, Canada. She first came to UC Berkeley as a graduate student and earned a PhD degree in 1999 from the Department of Statistics. Her doctoral research, under the supervision of Professor Terence P. Speed, concerned the linkage analysis of complex human traits. From 1999 to 2000, she was a postdoctoral fellow at the Mathematical Sciences Research Institute, Berkeley. Before joining the Faculty at UC Berkeley in July 2001, she underwent two years of postdoctoral training in genomics in the laboratory of Professor Patrick O. Brown, Department of Biochemistry, Stanford University. Her work in the Brown Lab involved the development and application of statistical methods and software for the analysis of microarray gene expression data.
- PhD – Statistics, UC Berkeley, 1999
- MS – Mathematics, Carleton University, Ottawa, 1994
- BS – Mathematics, Carleton University, Ottawa, 1992
- Data science
- Exploratory data analysis
- High-dimensional inference
- Loss-based inference with cross-validation, e.g., classification, regression and model selection
- Cluster analysis/Unsupervised learning
- Statistical computing
- Design and analysis of high-throughput gene expression experiments
- PH C240C-D/STAT C245C-D: Computational Statistics with Applications in Biology and Medicine I and II