Name: Madeleine Udell, Assistant Professor from Cornell
Date: Thursday, February 18
Time: 11:00 am - 12:00 pm
Title: Big Data is Low Rank
Abstract: 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 --- indeed, suspiciously well — across a wide range of data science applications, including applications in social science, medicine, and machine learning. 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 effectiveness.
Bio: Madeleine Udell is Assistant Professor of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University. She studies optimization and machine learning for large scale data analysis and control, with applications in marketing, demographic modeling, medical informatics, engineering system design, and automated machine learning. She has received several awards, including an NSF CAREER award (2020), an Office of Naval Research (ONR) Young Investigator Award (2020), and an INFORMS Optimization Society Best Student Paper Award (as advisor) (2019).