AI Institute for Learning-Enabled Optimization at Scale (TILOS)

Improved optimizations of energy-efficiency, safety, robustness, and other criteria in engineered systems offer the promise of incalculable societal benefits. However, challenges of scale and complexity keep many real-world optimization needs beyond our reach. The mission of The National Artificial Intelligence (AI) Institute for Learning-enabled Optimization at Scale (TILOS) is to make impossible optimizations possible, at scale and in practice.

The institute (a partnership of University of California, San Diego, Massachusetts Institute of Technology, National University, University of Pennsylvania, University of Texas at Austin and Yale University) will pioneer learning-enabled optimizations that transform chip design, robotics, communication networks, and other use domains that are vital to our nation’s health, prosperity and welfare. In TILOS, research, education, outreach and translation are holistically driven by what makes the nexus of AI/ML and optimization uniquely challenging at the leading edge of practice.

Industry partners will interact closely with TILOS on both foundational research and its use-domain application. TILOS will build an openly accessible program of continuing education with long-term, lifelong learning and skills renewal as its central tenet. This institute will also broaden participation, building on the visible successes at its partner institutions that have reached underserved demographics from K-12 onward.

Through these efforts, TILOS will discover, educate, and translate into real-world practice a new nexus of AI, optimization, and use.

TILOS is organized around multiple virtuous cycles that unify AI and optimization, use domains, and the translation of AI-optimization breakthroughs into practice. A first virtuous cycle of AI and optimization, where each enables and amplifies the other, is at the heart of TILOS. Foundational research will pursue five main pillars: (i) bridging discrete and continuous optimization; (ii) distributed, parallel, and federated optimization; (iii) optimization on manifolds; (iv) dynamic decisions under uncertainty; and (v) nonconvex optimization in deep learning.

A second virtuous cycle of challenges, inspirations and data-enabled validations connects the foundational research in AI-optimization with use-domain expertise. The initial use-domain foci bring diverse optimization challenges but inspire shared solutions with commonalities such as physical embeddedness, hierarchical-system context, underlying graphical models, safety and robustness as first-class concerns, and the bridging of human-guided and autonomous systems.

A third virtuous cycle is one of translation and ever-tighter connections to the leading edge of practice. TILOS will leverage industry partnerships to accelerate impact via open standards, data sets and “data virtual reality”, and open source that democratize access to research enablement. Roadmaps of optimization formulations and progress metrics will draw researchers together and toward shared research goals.

A fourth virtuous cycle with industry and the institutional partners spans both workforce development and the broadening of participation. Workforce development will identify and teach the skills and mindsets needed at the nexus of learning, optimization and practice, so as to provide skills renewal for the existing workforce as well as onramps for underserved demographics such as veterans or those seeing a career change.

Broadening of participation will be pursued via the institute’s partnerships with community organizations and middle and high school educators, via tiers of engagement that span exposure, experience and environment.

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.