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13 de jun. de 2019 · June 13, 2019. Daniela Witten. The American Public Health Association (APHA) has named Daniela Witten, a professor in statistics and biostatistics at the University of Washington and the Dorothy Gilford Endowed Chair in Mathematical Statistics, the winner of the 2019 Mortimer Spiegelman Award. This award honors a statistician who has made ...
Written by Witten — Daniela Witten. Email. dwitten@uw.edu. Department of Statistics. B-323, Padelford Hall, Box 354322, Seattle WA 98195-4322. Department of Biostatistics. Room 332, Hans Rosling Center, Box 351617, Seattle WA 98195-1617.
31 de mar. de 2020 · 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 ...
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".
Witten has been married to Chiara Nappi, a professor of physics at Princeton University, since 1979. They have two daughters and a son. Their daughter Ilana B. Witten is a neuroscientist at Princeton University, and daughter Daniela Witten is a biostatistician at the University of Washington.
Daniela Witten M.OST Daniela is a caring professional osteopath with an inherent desire to help people. She has experience of working with a diverse range of patients from amateur sportsmen/women to older adults and those living with life-limiting conditions. Daniela can relate to people from all walks of life and backgrounds. She has a mature
Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.
