The fusion of machine learning and optimization has the potential to
achieve breakthroughs in decision making that the two technologies
cannot accomplish independently. This talk reviews a number of
research avenues in this direction, including the concept of
optimization proxies and end-to-end learning. Principled combinations
of machine learning and optimization are illustrated on case studies
in energy systems, mobility, and supply chains. Preliminary results
show how this fusion makes it possible to perform real-time risk
assessment in energy systems, find near-optimal solutions quickly in
supply chains, and implement model-predictive control for large-scale
mobility systems
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