Associate Professor of Statistics and Biostatistics
University of Washington
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 a 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, 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 2014, Daniela is an associate professor in Statistics and Biostatistics at University of Washington.
"More Data, More (Statistical) Problems"
By now, virtually every field has become inundated with big data. We have been promised that this data will usher in a new era of previously unimaginable societal and scientific progress. While it is certainly true that more data brings with it incredible opportunities, it is also true that more data can bring new and previously unimaginable statistical challenges. I will talk about some of those statistical challenges, as well as statistical ways to solve them. Examples will be taken from biomedical research.