Madeleine Udell

a picture of Madeleine
Photo by Diana Mellon

I am a PhD candidate in Computational & Mathematical Engineering at Stanford University, working with Stephen Boyd. I will be a postdoctoral fellow in the Center for the Mathematics of Information at Caltech for the 2015-2016 academic year, hosted by Joel Tropp, and will be joining Cornell as an Assistant Professor in the School of Operations Research and Information Engineering in July 2016. My research focus is on modeling and solving large-scale optimization problems and on finding and exploiting structure in high dimensional data, with applications in marketing, finance, demographic modeling, and medical informatics.

My recent work on generalized low rank models (GLRMs) extends principal components analysis (PCA) to embed tabular data sets with heterogeneous (numerical, Boolean, categorical, and ordinal) types into a low dimensional space, providing a coherent framework for compressing, denoising, and imputing missing entries. My other favorite problems to ponder include how to parallelize optimization algorithms for general cone programs, ways to use graph partitioning to improve the convergence of distributed optimization algorithms, and how to decide which entry to query next in a matrix-completion setting.

I've developed of a number of open source tools for modeling and solving convex optimization problems, including Convex.jl, one of the top ten tools in the Julia language for technical computing, and am a member of the JuliaOpt organization, which curates high quality optimization software.

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