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Find similar grantsNSF AI Institute for Foundations of Machine Learning (NSF IFML) is sponsored by NSF Directorate for Computer and Information Science and Engineering (CISE). Funds an AI institute dedicated to advancing the theoretical and algorithmic foundations of machine learning.
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Institute for Foundations of Machine Learning 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.
IFML's signature Seminar Series: Spring 2026 Recap Leaders in AI, Robotics and Ethical Innovation Come Together at UT Austin UT Launches New School of Computing, Uniting Computer and Data Science, Statistics, Information Disciplines UT Austin Becomes an AI Research Powerhouse with NVIDIA Blackwell GPUs UT Ranks No. 1 in U.S. for Research Funded by National Science Foundation UT Doubles Size of One of World’s Most Powerful AI Computing Hubs Previously Recorded Talks Tutorial on Diffusion Models for Image Generation -- 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 Leaders in AI, Robotics and Ethical Innovation Come Together at UT Austin UT Launches New School of Computing, Uniting Computer and Data Science, Statistics, Information Disciplines UT Austin Becomes an AI Research Powerhouse with NVIDIA Blackwell GPUs UT Ranks No. 1 in U.S. for Research Funded by National Science Foundation UT Doubles Size of One of World’s Most Powerful AI Computing Hubs Artificial Intelligence Trained to Draw Inspiration From Images, Not Copy Them Ambient Diffusion: Reducing Dataset Memorization by Training with Heavily Corrupted Data The Future of Protein Engineering: Unlocking Evolutionary Insights & Stability Danny Diaz on Root Access Podcast!
New Texas Center Will Create Generative AI Computing Cluster Among Largest of Its Kind
According to the current listing, eligibility includes: Academic institutions, research organizations, and industry partners. Confirm the full requirements in the official notice before applying.
NSF AI Institute for Foundations of Machine Learning (NSF IFML) is funded by NSF Directorate for Computer and Information Science and Engineering (CISE). Verify program details on the funder's official page before applying.
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Past winners and funding trends for this program
RET Sites close October 14, 2026 — about nine awards from $5.8 million, capped at $600,000 over three years, with a PI eligibility rule that disqualifies most of the people who write outreach proposals. RET Supplements reach the same money for $15,000 a teacher and are not a competition.
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Read articleFuture CoRe (NSF 25-543) advertises $280 million and 400 to 600 awards across 11 programs, with a September 10, 2026 target date. The directorate behind it is running 43 percent below its own four-year average, $1 billion of NSF's budget is locked in a central account, and the two-proposal cap is enforced with no exceptions. Here is how to read the gap between what the solicitation promises and what the directorate is actually paying for.
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