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 feasible (or near-feasibel) and near-optimal
solutions to classes of optimization problems in milliseconds. The
talk reviews some of the concepts underlying optimization proxies,
including end-to-end learning, compact optimization learning, and
self-supervised learning. The benefits of optimization proxies are
demonstrated on applications in power systems, supply chains, and
mobility.

Short 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 the Georgia Institute of Technology. Several of
his optimization systems have been in commercial use for more than 20
years. His current research focuses on AI for Engineering, fusing
machine learning and optimization for applications in energy systems,
supply chains and manufacturing, and mobility. Van Hentenryck is a
fellow of AAAI and INFORMS, and the recipients of numerous research
and teaching awards. He was also a Ulam fellow at the Los Alamos
National Laboratories.