In honor of Women's History Month, AMSTAT News has recognized Professor Daniela Witten as one of several American Statistical Association (ASA) women who work in statistics and data science. She was chosen based on her accomplishments, and because she inspires and influences other women in the field.

Daniela Witten is an Associate Professor of Statistics and Biostatistics at the University of Washington. She completed a B.S. in Math and Biology with Honors and Distinction at Stanford University in 2005, and a Ph.D. in Statistics at Stanford University in 2010.

Daniela’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. She is particularly interested in techniques for unsupervised learning, with a focus on graphical modeling.

Daniela is the recipient of a number of honors, including an NDSEG Research Fellowship, an NIH Director’s Early Independence Award, a Sloan Research Fellowship, and an NSF CAREER Award. Her work has been featured in the popular media: among other forums, in Forbes Magazine (three times), Elle Magazine, on KUOW radio (Seattle’s local NPR affiliate), and as a PopTech Science Fellow. Most recently, she has made an appearance on NOVA, in the episode “Prediction by the Numbers”.

Daniela 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.