We are the NSF AI Institute for Foundations of Machine Learning (IFML)

Designated by the National Science Foundation (NSF) in 2020, IFML develops the key foundational tools for the next decade of AI innovation. Our institute comprises researchers from The University of Texas at Austin, University of Washington, Wichita State University, Stanford University, Santa Fe Institute, University of Nevada-Reno, Boston College, CalTech, University of California, Berkeley, and University of California, Los Angeles.

Our researchers create new algorithms that can help machines learn on the fly, change their expectations as they encounter people and objects in real life, and even bounce back from deliberate attempts by adversaries to manipulate datasets.

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Previously Recorded Talks

  • Tutorial on Diffusion Models for Image Generation -- Sanjay Shakkottai

    Sanjay Shakkottai

  • IFML Seminar: 10/4/25 - Foundation Model for Sequential Decision-Making

    Furong Huang, Associate Professor, University of Maryland

  • IFML Seminar: 9/27/24 - Computationally Efficient Reinforcement Learning with Linear Bellman Completeness

    Noah Golowich , PhD Student, MIT

  • IFML Seminar: 9/13/24 - On the Computational Complexity of Private High-dimensional Model Selection

    Saptarshi Roy, Postdoc Research Fellow, The University of Texas at Austin

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