AI Institute for Advances in Optimization

This NSF Artificial Intelligence (AI) Research Institute for Advances in Optimization aims at delivering a paradigm shift in automated decision-making at massive scales by fusing AI and Mathematical Optimization (MO) to achieve breakthroughs that neither field can achieve independently. The Institute is driven by societal challenges in energy, logistics and supply chains, resilience and sustainability, and circuit design and control.

In particular, the Institute will help deliver the next generation of control and optimization algorithms for operating electric grids with distributed generation and demand response, and for planning and scheduling large-scale, green, and resilient supply chains. The Institute will also pioneer novel AI&MO methods for designing new mixed-signal integrated circuits, dramatically reducing development time, and for operating sustainable urban environments.

To address the widening gap in job opportunities, the Institute will deliver an innovative longitudinal education and workforce development program with an initial focus on historically black high schools and colleges in Georgia, as well as Hispanic-serving high-schools and colleges in California. The program is organized along “pathways for AI in engineering” and seeks long-term partnerships with high schools and community colleges to transform AI education and research for underserved students.

These pathways will bring a step change to existing programs and teach AI&MO in the context of engineering disciplines and societal challenges. The Institute will develop internship programs with national laboratories and industrial partners, and build a strong, welcoming, and inclusive community where social mobility opportunities and the societal impact of AI technologies will be highlighted.

The Institute assembles a multi-disciplinary team in artificial intelligence and optimization and domain experts in the end-use cases, bringing together the Georgia Institute of Technology, the University of California at Berkeley, the University of Southern California, Clark Atlanta University, Spelman College, and the University of Texas at Arlington.

To transform decision-making at massive scales, the Institute moves from optimization solutions to intelligent agents that predict and quantify uncertainty, reason and optimize, learn continuously, and coordinate and collaborate. It unifies the data-driven and model-driven approaches at the core of AI and Operations Research (OR). Its methodology thrusts include a new generation of hybrid optimization solvers that learn to optimize, end-to-end learning and optimization to tightly integrate forecasting and decision making, and novel machine-learning methods based on combinatorial optimization.

To learn and optimize at massive scales, the Institute will contribute innovations in compact representations, data compression, and probabilistic modeling. To enable safe and scalable decision-making in uncertain and multi-agent environments that often arise in engineering disciplines, the Institute will design new methods in reinforcement learning, decentralized optimization, and data-driven optimization at massive scales.

Importantly, to ensure that these scientific advances serve the interests of society, a transversal thrust will integrate ethics and values in complex systems design from inception through design and operation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.