Biological Sciences, Machine Learning, Statistics
Applications in Life Sciences
Daniela Witten’s research involves the development of statistical machine learning techniques for the analysis of large-scale data sets coming out of genomics and other fields. High-dimensional data sets, which are characterized by the presence of a greater number of variables than observations, pose both statistical and computational challenges. Daniela seeks to develop techniques to analyze such data, particularly in the unsupervised setting in which inference is challenging and validating results obtained is even more so. Particular areas of interest involve graphical modeling, clustering, and matrix decompositions, as well as the analysis of transposable data.