Women in Data Science (WiDS)

Filling in Missing Data with Low Rank Models | Madeleine Udell | WiDS 2019

Madeleine Udell, Assistant Professor of Operations Research & Information Engineering, & Richard and Sybil Smith Sesquicentennial Fellow, Cornell University​

​​Data scientists are often faced with the challenge of understanding a high dimensional data set organized as a table. These tables may have columns of different (sometimes, non-numeric) types, and often have many missing entries. In this talk, we discuss how to use low rank models to analyze these big messy data sets.

Low rank models perform well across a wide range of data science applications, including recommender systems, movie references, topic models, medical records, and genomics. In this talk, we introduce the mathematics of low rank models,
demonstrate a few surprising applications of low rank models in data science, and present a simple mathematical explanation for their efficacy.

Learn more: widsconference.org