Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning.

She has received a number of awards for her research in statistical machine learning: most notably the Spiegelman Award from the American Public Health Association for a (bio)statistician under age 40, and the Presidents’ Award from the Committee of Presidents of Statistical Societies for a statistician under age 41.

Daniela is a co-author of the textbook "Introduction to Statistical Learning", and beginning in 2023 will serve as Joint Editor of Journal of the Royal Statistical Society, Series B.

Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning.

Daniela is the recipient of an NIH Director's Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, and a Simons Investigator Award in Mathematical Modeling of Living Systems. She received the Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS), awarded annually to a statistician under age 41 in recognition of outstanding contributions to the field of statistics. She also received the Spiegelman Award from the American Public Health Association for a statistician under age 40 who has made outstanding contributions to statistics for public health, and the Leo Breiman Award for contributions to the field of statistical machine learning. She is a Fellow of the American Statistical Association and the Institute for Mathematical Statistics, and an Elected Member of the International Statistical Institute.

Daniela is a co-author (with Gareth James, Trevor Hastie, and Rob Tibshirani) of the very popular textbook "Introduction to Statistical Learning". She has served as an Associate Editor for Biometrika, Journal of Computational and Graphical Statistics, and Journal of the American Statistical Association, and as an Action Editor for Journal of Machine Learning Research. Beginning in 2023, she will serve as Joint Editor of Journal of the Royal Statistical Society, Series B.

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.