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.
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