We are the National 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, and Microsoft Research.
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
Large-scale graph machine learning: tradeoffs, guarantees and dynamics
Abstract: Graph neural networks (GNNs) are successful at learning representations from most types of network data but suffer from limitations in large graphs, which do not have the Euclidean structure that time and image...
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Research Project
Robust MRI Using New Algorithms for Generative Machine Learning
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IFML Researchers Win Two Outstanding Paper Awards at NeurIPS 2022
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THIS JUST IN: New York Times Story on UT Austin's Online Master's Degree in AI
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IFML Public Lecture: AI for Accurate and Fair Imaging
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NLP Modules for High School
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Standing-Room Only at the 2023 Machine Learning Lab Public Lecture
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Upcoming Events and Workshops
- September22
Large-scale graph machine learning: tradeoffs, guarantees and dynamics
Talk by Luana Ruiz, Assistant Professor, Department of Applied Mathematics and Statistics, Johns Hopkins University
October6Finite-Sum Coupled Compositional Optimization: Theories and Practical Applications
Talk by Tianbao Yang, Associate Professor, CSE, Texas A&M University
Previously Recorded Talks
New & Noteworthy
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IFML @ ICML 2023
IFML members had 35 papers accepted to ICML 2023 -- The Fortieth International Conference on Machine Learning
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Will Robots Triumph over World Cup Winners by 2050?
The RoboCup dream has inspired generations of roboticists