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, Microsoft Research. Stanford University, Santa Fe Institute, University of California, Los Angeles, University of California, Berkeley, California Institute of Technology, and Arizona State University.
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.
Featured

IFML Seminar
IFML Seminar: 04/04/25 - Robust Autonomy Emerges from Self-Play
Abstract: Self-play has powered breakthroughs in two-player and multiplayer games. In this talk, I show that self-play is a surprisingly effective strategy in another domain: robust and naturalistic driving emerges entirely from self-play in...
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The Future of Protein Engineering: Unlocking Evolutionary Insights & Stability
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Adam Klivans Talks DeepSeek on KXAN News
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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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IFML Diffusion Seminar Series: Tutorial on the Mathematical Foundations of Diffusion Models for Image Generation
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Turbocharging Protein Engineering with AI
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NLP Modules for High School
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Upcoming Events and Workshops
- April4
IFML Seminar: 04/04/25 - Robust Autonomy Emerges from Self-Play
Talk by Philipp Krähenbühl, associate professor in Computer Science, UT Austin
April11IFML Seminar: 04/11/25 - Beyond Benchmarks: Building a Science of AI Measurement
Talk by Sanmi Koyejo, assistant professor in Computer Science, Stanford University and co-founder of Virtue AI
April18IFML Seminar: 04/18/25 - Learning to Solve Imaging Inverse Problems without Ground Truth
Talk by Andrew Wang, PhD student at the Institute for Imaging, Data and Communications in the School of Engineering, University of Edinburgh
April25IFML Seminar: 04/25/25 - On the Role of Gaussian Covariates in Minimum Norm Interpolation
Talk by Gil Kur, postdoctoral fellow at ETH Zürich
Previously Recorded Talks
New & Noteworthy
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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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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
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Now, Later, and Lasting: 10 Priorities for AI Research, Policy, and Practice
Shaping the future of artificial intelligence