Daniela Witten

Affiliation
Associate Professor of Statistics and Biostatistics, University of Washington

Educational Background
Stanford University: BS, Mathematics and Biology with Honors and Distinction (2005)
Stanford University: MS, Statistics (2006)
Stanford University: PhD, Statistics (2010)

About Daniela
Daniela Witten’s research involves the development of statistical machine learning methods for high-dimensional data, with applications to genomics, neuroscience, and other fields. She is passionate about developing new statistical techniques to bring order to the chaos of large-scale, complex, messy data sets arising from new data-generation technologies. Since contemporary data sets are often collected with the goal of hypothesis generation, rather than hypothesis testing, she is particularly interested in techniques for unsupervised learning.

Daniela is the recipient of a number of honors, including a National Institutes of Health Director’s Early Independence Award, a Sloan Research Fellowship, a National Science Foundation CAREER Award, an National Defense Science and Engineering Graduate Research Fellowship, a Gertrude Cox Scholarship, and a Ray Carroll Young Investigator Award. Her work has been featured in the popular media: in Forbes Magazine (three times), Elle Magazine, on Seattle’s NPR station, and as a PopTech Science Fellow.

Daniela is committed to translating key concepts in statistical machine learning to a broad scientific audience. She is a co-author (with Gareth James, Trevor Hastie, and Robert Tibshirani) of the extremely popular textbook Introduction to Statistical Learning, which is used to teach statistical learning at universities across the United States and worldwide at both the undergraduate and introductory graduate levels. The book is freely available. Daniela was a member of the Institute of Medicine committee that released the influential 2012 report, “Evolution of Translational Omics,” laying out a set of best practices for translating omics-based research to the clinic.

Daniela spends her free time running after her two small children, and also, when time allows, going for runs.