Sandrine Dudoit
Sandrine Dudoit, PhD
Executive Associate Dean, College of Computing, Data Science, and Society
Professor, Department of Statistics
Professor, Division of Biostatistics, School of Public Health
University of California, Berkeley
367 Evans Hall, #3860
Berkeley, CA 94720-3860
Fax: (510) 642-7892
E-mail: sandrine@stat.berkeley.edu
Advising and enrollment. Please consult the webpage.
Prospective graduate students. Thank you for your interest in joining my group. Please note that you would first have to apply and be admitted into UC Berkeley and that the decision for a student to join a particular research group is typically not made until the second year of study. I am unfortunately not in a position to meet individually with prospective students until after admissions decisions have been made.
Please take the time to read carefully the information on the program websites
(Statistics,
Biostatistics,
Computational Biology) before contacting me. You will find answers to most of your questions regarding application procedures, admission criteria, degree requirements, and financial support on these websites. If you still have questions regarding administrative matters, please contact grad_adm@stat.berkeley.edu (Statistics), biostat@berkeley.edu (Biostatistics), or Kate Chase (Computational Biology).
Press
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Neuroscientists roll out first comprehensive atlas of brain cells, Berkeley News, October 6, 2021. [URL]
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UC Berkeley researchers develop first-ever atlas of brain cells, Daily Cal, October 10, 2021. [URL]
Recent Publications
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Brain Initiative Cell Census Network, Nature, 2021. [URL]
Recent Presentations
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Statistical Methods and Software for Investigating Stem Cell Differentiation Using Single-Cell Transcriptome Sequencing, Department of Statistics and Actuarial Science, Simon Fraser University, March 23, 2018.
Slides: [PDF]
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Keynote Presentation, Intelligent Systems for Molecular Biology (ISMB), Orlando, FL, July 11, 2016.
Using Single-Cell Transcriptome Sequencing to Infer Olfactory Stem Cell Fate Trajectories.
Slides: [PDF]
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Workshop, BioC 2016: Where Software and Biology Connect, Stanford, CA, June 25, 2016.
Analysis of Single-Cell RNA-Seq Data with R and Bioconductor.
R package bioc2016singlecell: [GitHub]
Research and Teaching Activities
My research and teaching activities concern 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 and medicine.
Theory and methods.
My methodological interests regard high-dimensional statistical learning and include exploratory data analysis (EDA), unsupervised learning (e.g., cluster analysis, dimensionality reduction), loss-based estimation with cross-validation (e.g., density estimation, classification, regression, model selection), and causal inference.
Applications.
My methodological work is motivated in large part by statistical learning questions arising in biological and medical research and, in particular, high-throughput sequencing gene expression studies and precision health and medicine. My contributions span a broad range of questions throughout the data science pipeline, of both practical relevance and theoretical interest: experimental design, EDA, normalization, expression quantitation, differential expression analysis, biomarker and treatment effect modifier discovery, class discovery, class prediction, inference of cell lineages, and integration of biological annotation metadata (e.g., Gene Ontology 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 (http://www.bioconductor.org), 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]
Headshots: [Pic1] [Pic2]
Casual shots: [Pic1] [Pic2] [Pic3]
Affiliations