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Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
Madeleine Udell
Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
Madeleine Udell is Assistant Professor of Management Science and Engineering at Stanford University, with an affiliation with the Institute for Computational and Mathematical Engineering (ICME) and courtesy appointment in Electrical Engineering, and Associate Professor with tenure (on leave) of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University.
She completed her PhD at Stanford in Computational and Mathematical Engineering and a postdoctoral fellowship at the Center for the Mathematics of Information at Caltech.
Her research aims to accelerate and simplify large-scale data analysis and optimization,
with impact on challenges in healthcare, finance, marketing, operations, and engineering systems design, among others.
Her work in optimization seeks to detect and exploit novel structures,
leading to faster and more memory-efficient algorithms,
automatic proofs of optimality, better complexity guarantees,
and user-friendly optimization solvers and modeling languages.
Her work in machine learning centers on challenges of data preprocessing, interpretability, and causality,
which are critical to practical application in domains with messy data.
Her awards include the Kavli Fellowship (2023), Alfred P. Sloan Research Fellowship (2021), an NSF CAREER award (2020), and an ONR Young Investigator Award (2020).
She completed her PhD at Stanford in Computational and Mathematical Engineering and a postdoctoral fellowship at the Center for the Mathematics of Information at Caltech.
Her research aims to accelerate and simplify large-scale data analysis and optimization,
with impact on challenges in healthcare, finance, marketing, operations, and engineering systems design, among others.
Her work in optimization seeks to detect and exploit novel structures,
leading to faster and more memory-efficient algorithms,
automatic proofs of optimality, better complexity guarantees,
and user-friendly optimization solvers and modeling languages.
Her work in machine learning centers on challenges of data preprocessing, interpretability, and causality,
which are critical to practical application in domains with messy data.
Her awards include the Kavli Fellowship (2023), Alfred P. Sloan Research Fellowship (2021), an NSF CAREER award (2020), and an ONR Young Investigator Award (2020).
Education
BS, Yale University, Mathematics and Physics (2009)
PhD, Stanford University, Computational and Mathematical Engineering (2015)
Contact
Mail Code
4026