Sandrine Dudoit is professor of biostatistics and statistics and chair of the graduate group in biostatistics at the University of California, Berkeley. Professor Dudoit's methodological research interests regard high-dimensional inference and include exploratory data analysis, visualization, loss-based estimation with cross-validation (e.g., density estimation, regression, model selection), and multiple hypothesis testing. Much of her 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, for example, mRNA-Seq for transcriptome analysis and genome annotation and ChIP-Seq for DNA-protein interaction profiling (e.g., transcription factor binding). Her contributions include exploratory data analysis, normalization and expression quantitation, differential expression analysis, class discovery, prediction, integration of biological annotation metadata (e.g., gene ontology annotation). She is also interested in statistical computing and, in particular, reproducible research. She is a founding core developer of the Bioconductor Project, an open source and open development software project for the analysis of biomedical and genomic data.
Professor Dudoit is a coauthor of the book Multiple Testing Procedures with Applications to Genomics and a coeditor 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.
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