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, and engineering system design.

Her research in optimization centers on detecting and exploiting novel structures in optimization problems, with a particular focus on convex and low rank problems. These structures lead the way to automatic proofs of optimality, better complexity guarantees, and faster, more memory-efficient algorithms. She has developed of a number of open source libraries for modeling and solving optimization problems, including Convex.jl, one of the top tools in the Julia language for technical computing, and is a member of the JuliaOpt organization, which curates high quality optimization software.

Her research in machine learning centers on methods for imputing missing data in large tabular data sets. Her 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.

Madeleine completed her PhD at Stanford University in Computational & Mathematical Engineering in 2015 under the supervision of Stephen Boyd, and a one year postdoctoral fellowship at Caltech in the Center for the Mathematics of Information hosted by Professor Joel Tropp. At Stanford, 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. She received a B.S. degree in Mathematics and Physics, summa cum laude, with honors in mathematics and in physics, from Yale University.