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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UT Expands Research on AI Accuracy and Reliability to Support Breakthroughs in Science, Technology and the Workforce
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UT to Lead the Next Gen of AI
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NSF IFML Podcast: From Research Discovery to Real-World Impact
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NSF IFML Researchers Win Outstanding Paper Award at ICML 2025
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New IFML Framework for Diffusion Models to be included in several production pipelines by teams in Google
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Turbocharging Protein Engineering with AI
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Upcoming Events and Workshops
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Previously Recorded Talks
New & Noteworthy
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NSF IFML Researchers Win Outstanding Paper Award at ICML 2025
"Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions"
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IFML research collaborations power next generation of multimodal AI with OpenCLIP and DataComp
OpenCLIP, the main text/image encoder in Stable Diffusion, is in the top 1% of all Python packages and has more than 50,000 git clones per day. DataComp, is the first rigorous benchmark for advancing multimodal dataset creation.
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The Future of Protein Engineering: Unlocking Evolutionary Insights & Stability
Danny Diaz on Root Access Podcast!
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Can AI Make Critical Communications Chips Easier to Design?
NSF IFML Director Adam Klivans part of multi-university team and industry team formed to accelerate AI-driven design of radio frequency integrated circuits.
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Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension earns Best Paper Award at COLT 2024!
Authored by IFML Director Adam Klivans, students Gautam Chandrasekaran, Konstantinos Stavropoulos, IFML postdoc Vasilis Kontonis, and former UT CS PhD Raghu Meka