Welcome.AI
    Skip to content
    Machine Learning

    TILOS Secures $15 Million NSF Grant to Advance Practical AI Solutions

    With a $15 million NSF grant, UC San Diego's TILOS is set to revolutionize artificial intelligence by advancing intelligent systems capable of operating in complex environments. This new phase focuses on adaptive AI, multi-agent systems, and reliable hardware.

    today.ucsd.edu•October 16, 2026•2 min read

    Key Facts

    • TILOS secures $15M NSF funding, enhancing UC San Diego's AI leadership through 2031.
    • Shift from optimization to intelligent systems indicates a strategic pivot in AI research focus.
    • New partnerships with Brown and MIT strengthen competitive positioning in AI innovation.
    • Research aims to impact sectors like healthcare and manufacturing, signaling economic growth potential.
    • Emphasis on real-world applications suggests a shift towards practical AI solutions and workforce development.

    Summary

    The National Science Foundation (NSF) has awarded $15 million to the University of California, San Diego (UC San Diego) to support the second phase of the AI Institute for Learning-enabled Optimization at Scale (TILOS). This funding, effective from November 1, 2026, through October 31, 2031, is significant as it marks a strategic pivot from foundational research in learning-enabled optimization to a broader focus on intelligent systems capable of learning, reasoning, and operating in complex physical environments. This shift is vital for advancing real-world applications of artificial intelligence (AI) across various sectors, including robotics, communication networks, and chip design.

    TILOS, established in 2021 as one of the first NSF AI Institutes, has been at the forefront of AI research in optimization. The renewed funding will enable TILOS to expand its research agenda into three main areas: developing adaptive AI systems, creating multi-agent systems that can coordinate actions in the physical world, and designing reliable AI hardware. This comprehensive approach aims to enhance the efficiency and robustness of AI systems, which is crucial for addressing complex societal and economic challenges.

    The leadership team at TILOS includes prominent figures such as Yusu Wang, who is the principal investigator, and co-principal investigators from various disciplines within UC San Diego and partner institutions. This diverse collaboration reflects a growing trend in AI research, where interdisciplinary efforts are essential for tackling the multifaceted nature of intelligent systems. The inclusion of new partners like Brown University and continued collaboration with institutions such as MIT and the University of Texas at Austin strengthens the research network, positioning TILOS as a national nexus for AI and optimization.

    The implications of TILOS's renewed focus are far-reaching. By integrating AI with physical systems, researchers aim to develop technologies that can make real-time decisions and adapt to changing conditions, which is critical in high-stakes environments such as healthcare and transportation. The potential for these advancements to enhance operational efficiency and decision-making capabilities across industries signals a significant shift in how businesses might leverage AI technologies in the coming years.

    Moreover, TILOS plans to invest in education and workforce development, ensuring that the next generation of researchers is equipped to contribute to this evolving field. The institute's commitment to translating research into practical applications through partnerships with industry further emphasizes the importance of bridging the gap between theoretical advancements and real-world implementation. This approach not only accelerates innovation but also enhances the competitiveness of U.S. industries in the global market.

    As TILOS embarks on this new phase, it is positioned to define the future of scalable and trustworthy intelligent systems. The emphasis on physically embedded intelligence suggests a growing market for AI solutions that can operate seamlessly within existing infrastructures. For businesses, this represents an opportunity to harness advanced AI capabilities to optimize operations, improve customer experiences, and drive innovation. The trajectory of TILOS indicates a broader trend where the convergence of AI and physical systems will play a pivotal role in shaping the future landscape of technology and industry.

    Entities Mentioned

    Technologies

    AI
    optimization
    robotics
    communication networks
    chip design

    People

    Yusu Wang
    Tara Javidi
    Henrik Christensen
    Arya Mazumdar
    David Z. Pan
    Pradeep K. Khosla
    Peipei Zhou
    Alexander Rakhlin
    Yury Polyanskiy
    Sensen Li

    Organizations

    National Science Foundation
    University of California San Diego
    Massachusetts Institute of Technology
    Pennsylvania State University
    Brown University
    Yale University
    University of Texas at Austin

    Key Concepts

    AI Institute for Learning-enabled Optimization at Scale (TILOS)
    intelligent systems
    learning-enabled optimization
    research collaboration
    education and workforce development
    real-world applications
    Phase II award
    national research network

    Definitions

    TILOS
    The AI Institute for Learning-enabled Optimization at Scale, focused on advancing AI and optimization for intelligent systems.
    NSF
    The National Science Foundation, a U.S. government agency that supports fundamental research and education in all the non-medical fields of science and engineering.
    Phase II award
    A funding phase that supports the continuation and expansion of research initiatives, in this case for TILOS, from 2026 to 2031.
    embodied intelligence
    Intelligent systems that can learn, reason, and operate effectively in physical environments.
    AI Futures Roundtables
    Events organized to convene researchers, educators, and industry leaders to discuss the future of AI.

    Use Cases

    • →advancing robotics
    • →enhancing communication networks
    • →improving chip design
    • →developing real-time decision-making technologies
    • →adapting to changing conditions in high-stakes settings
    • →creating interdisciplinary training opportunities for students

    Frequently Asked Questions

    What is the purpose of the TILOS institute?

    TILOS aims to advance AI and optimization for intelligent systems, focusing on research that enables these systems to learn, reason, and operate in the physical world.

    Who leads the TILOS institute?

    The TILOS institute is led by Yusu Wang, a professor at UC San Diego, along with several co-principal investigators from the university and other institutions.

    What are the main research areas of TILOS?

    TILOS focuses on three main areas: developing adaptive AI systems, creating embodied multi-agent AI, and designing AI hardware substrates for reliable operation across computing infrastructures.

    How does TILOS contribute to education?

    TILOS invests in education and workforce development by creating interdisciplinary training opportunities and collaborating with industry partners to translate research into real-world applications.

    What is the significance of the NSF funding?

    The NSF funding of $15 million over five years supports the continued research and development of TILOS, enabling it to expand its impact on AI and optimization for intelligent systems.

    Where AI Leaders Stay Informed

    The latest AI intelligence, case studies, and research — delivered to your inbox every week.

    Free to read. Unsubscribe anytime.