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 particularly interested in unsupervised learning, with a focus on graphical modeling.

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 in Mathematical Modeling of Living Systems, a David Byar Award, a Gertrude Cox Scholarship, and an NDSEG Research Fellowship. Her work has been featured in the popular media: among other forums, in Forbes Magazine (three times) and Elle Magazine, on KUOW radio (Seattle's local NPR affiliate station), in a NOVA documentary, and as a PopTech Science Fellow.

Daniela is a co-author (with Gareth James, Trevor Hastie, and Rob Tibshirani) of the very popular textbook "Introduction to Statistical Learning". She was a member of the Institute of Medicine committee that released the report "Evolution of Translational Omics".

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 a professor of Statistics and Biostatistics at University of Washington.