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Filling in missing data with generalized low-rank models

Madeleine Udell (Cornell University)
Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Hardcore Data Science
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Madeleine Udell explains how to fill in missing data with generalized low-rank models.

Photo of Madeleine Udell

Madeleine Udell

Cornell University

Madeleine Udell is assistant professor of operations research and information engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University, where she studies optimization and machine learning for large-scale data analysis and control, with applications in marketing, demographic modeling, medical informatics, and engineering system design. Her recent work on generalized low-rank models (GLRMs) extends principal components analysis (PCA) to embed tabular datasets with heterogeneous (numerical, Boolean, categorical, and ordinal) types into a low dimensional space, providing a coherent framework for compressing, denoising, and imputing missing entries. Madeleine has developed of a number of open source libraries for modeling and solving optimization problems, including Convex.jl, one of the top 10 tools in the new Julia language for technical computing, and is a member of the JuliaOpt organization, which curates high-quality optimization software. Previously, she was a postdoctoral fellow at Caltech’s Center for the Mathematics of Information, hosted by Joel Tropp. Madeleine holds a PhD in computational and mathematical engineering (under the supervision of Stephen Boyd) from Stanford University—where she was awarded a NSF graduate fellowship, a Gabilan graduate fellowship, and a Gerald J. Lieberman fellowship and was selected as the doctoral student member of Stanford’s School of Engineering Future Committee to develop a road map for the future of engineering at Stanford over the next 10–20 years—and a BS in mathematics and physics, summa cum laude with honors, from Yale University.