AI and the Future of Optimization

This talk explores how modern AI can make mathematical optimization more accessible and efficient by helping formulate problems and develop algorithms that deliver trustworthy, provably optimal decisions.

December 4, 2026

11:00 AM ET | 4PM CET

Gurobi Days Digital Academic

AI and the Future of Optimization

December 4, 2026

11:00 AM ET | 4PM CET

Gurobi Days Digital Academic

AI and the Future of Optimization

December 4, 2026

11:00 AM ET | 4PM CET

Webinar topic

Mathematical optimization seeks to equip decisionmakers with the tools needed to make provably optimal data-driven decisions, with important applications from finance and healthcare to manufacturing and logistics. However, widespread use of these powerful tools is limited by the time and expertise required to understand the problem, to design an appropriate algorithmic solution, to implement the algorithm, and to communicate (and debug or improve) the resulting plan.

This talk describes a vision for how modern AI tools can help democratize and accelerate this important work, while delivering trustworthy solutions. We ground our vision in research showing how AI can help formulate optimization problems; design, implement and tune algorithms; and develop new algorithms with provable guarantees.

This webinar is part of the Gurobi Days Digital Academic webinar series focused around the theme of AI Meets Optimization: New Ideas Transforming Research and Education. You can register for our first event of this series here: https://www.gurobi.com/resources/webinar-events/efficient-optimization-formulations-llms-gdda2026

Speaker

Meet Your Expert Speaker

Learn from the best in the industry.

Madeleine Udell
Associate Professor of Management Science and 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. 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).

Speaker

Meet Your Expert Speaker

Learn from the best in the industry.

Madeleine Udell
Associate Professor of Management Science and 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. 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).

Speaker

Meet Your Expert Speaker

Learn from the best in the industry.

Associate Professor of Management Science and Engineering
Madeleine Udell