Bio

Madeleine Udell is Associate Professor of Management Science and Engineering and Gabilan Faculty Fellow at Stanford University, with an affiliation with the Institute for Computational and Mathematical Engineering (ICME) and a courtesy appointment in Electrical Engineering. She was previously a tenured Associate Professor of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University.

Her research develops the mathematical and computational foundations for data-driven decision systems that are scalable, accessible, and verifiable. Across optimization, machine learning, and scientific computing, she identifies and exploits hidden structure in data, algorithms, formal models, and human workflows to create faster algorithms, more usable interfaces, and more reliable checks.

Her work ranges from low-dimensional modeling and randomized numerical linear algebra to adaptive optimization methods that learn how to accelerate themselves. She also develops human-centered AI systems that help people formulate and solve optimization problems, generate scientific code, and support high-stakes decisions while surfacing assumptions, enforcing constraints, and producing evidence users can audit. An emerging thread of her work asks how AI-assisted science can be verified at scale, combining automated technical checks with human judgment about significance and value. Applications span healthcare, operations, engineering design, energy, and scientific discovery.

Her awards include the Kavli Fellowship (2023), Alfred P. Sloan Research Fellowship (2021), a National Science Foundation CAREER award (2020), an Office of Naval Research (ONR) Young Investigator Award (2020), a Cornell Engineering Research Excellence Award (2020), an INFORMS Optimization Society Best Student Paper Award (as advisor) (2019), and INFORMS Doing Good with Good OR (2018). Her work has been supported by the NSF, ONR, AFOSR, DARPA, IBM, and the Canadian Institutes of Health.

Madeleine has advised more than 60 students and postdocs, including eight graduated PhD students who later joined Google, Amazon, Two Sigma, Uber, the University of Washington, UCSD, and Tsinghua University. She has developed several new courses in optimization and machine learning, earning Cornell's Douglas Whitney ’61 Engineering Teaching Excellence Award in 2018.

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.