Title: Trustworthy Optimization Learning

Abstract: The fusion of deep learning and optimization has the potential to deliver outcomes for engineering applications that the two technologies cannot achieve independently. This talk illustrates this potential with the concept of optimization proxy, a differentiable program that can produce, in milliseconds, feasible and near-optimal solutions to classes of optimization problems. The talk reviews some of the foundations underlying optimization proxies, including end-to-end learning, compact optimization learning, dual learning, and self-supervised learning. The benefits of optimization proxies are demonstrated on applications in power systems and supply chains.

Bio: Pascal Van Hentenryck is the director of the NSF AI Institute for Advances in Optimization (AI4OPT) and the A. Russell Chandler III Chair and Professor at Georgia Tech. He was a professor at Computer Science at Brown University for over 20 years and led the optimization research group at the National ICT Research, Australia. Van Hentenryck designed and implemented several innovative optimization systems that have been in commercial use for over 20 years, including the pioneering CHIP and OPL systems. His current research focuses on AI Engineering, for applications in energy systems, supply chains, manufacturing, and mobility.