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Daniela Witten's research involves the development of statistical machine learning methods for high-dimensional data, with applications to genomics and other fields. Daniela is a co-author (with Gareth James, Trevor Hastie, and Rob Tibshirani) of the very popular textbook "Introduction to Statistical Learning". Daniela is the recipient of a number of honors, including an NIH Director's Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, and a Simons Investigator Award. Her work has been featured in the popular media: among other forums, in Forbes Magazine (three times), Elle Magazine, on KUOW radio, and as a PopTech Science Fellow. Daniela completed a BS in Math and Biology with Honors and Distinction at Stanford University in 2005, and a PhD in Statistics at Stanford University in 2010. Since 2018, Daniela is the Dorothy Gilford Endowed Chair in Mathematical Statistics and a Professor of Statistics and Biostatistics at University of Washington.
Daniela Witten's research involves the development of statistical machine learning methods for high-dimensional data, with applications to genomics and other fields. Daniela is a co-author (with Gareth James, Trevor Hastie, and Rob Tibshirani) of the very popular textbook "Introduction to Statistical Learning". Daniela is the recipient of a number of honors, including an NIH Director's Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, and a Simons Investigator Award. Her work has been featured in the popular media: among other forums, in Forbes Magazine (three times), Elle Magazine, on KUOW radio, and as a PopTech Science Fellow. Daniela completed a BS in Math and Biology with Honors and Distinction at Stanford University in 2005, and a PhD in Statistics at Stanford University in 2010. Since 2018, Daniela is the Dorothy Gilford Endowed Chair in Mathematical Statistics and a Professor of Statistics and Biostatistics at University of Washington.