AI4OPT Seminar Series

Date: Friday, March 18th, 2026

Time: 1:00 pm – 2:00 pm

Location: Coda (756 W Peachtree St NW, Atlanta, GA 30308) - Conference Room 114

Also live streamed at: https://gatech.zoom.us/j/93415839465 (Meeting ID: 934 1583 9465)

Speaker: Robert Parker

Optimizing over trained neural network surrogates

Abstract: Optimization problems constrained by neural network surrogates arise in design, control, and verification problems involving these surrogates. While these problems can be solved (locally) by interior point methods, the dense, sequential structure of neural networks lead to bottlenecks in naive implementations. This talk introduces MathOptAI.jl, a software package designed to help embed machine learning models into optimization problems, and demonstrates how it can be used to overcome these bottlenecks by exploiting neural networks' structures in automatic differentiation and linear algebra operations. Finally, we demonstrate these techniques on an adversarial input generation problem with a state-of-the-art graph neural network model of AC power flow.

Bio: Robert Parker is a scientist in the applied math and plasma physics group at Los Alamos National Laboratory. He works on applications of optimization to power and energy infrastructure systems and on the integration of optimization and machine learning. He has a Ph.D. in Chemical Engineering from Carnegie Mellon University.

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Note: Lunch will be served at the seminar. So, please stop by 15 minutes beforehand to serve up and be seated on time.

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Videos of the past seminars can be seen on AI4OPT webpage at https://www.ai4opt.org/seminars/past-seminars