AUSTIN, Texas — A National Science Foundation artificial intelligence institute based at The University of Texas at Austin will receive continued funding for research that will improve the accuracy and reliability of AI models and lead to new drug development and improvements in clinical diagnoses.
The work of the NSF AI Institute for Foundations of Machine Learning (IFML) underpins the next generation of artificial intelligence and is crucial for developing more accurate AI systems, from the mathematics of diffusion models to denoise images, to algorithms that improve the speed and accuracy of magnetic resonance imaging (MRI), to biotech innovations set to revolutionize drug discovery and therapeutics.
“UT Austin is a research powerhouse that is focused on preparing students to thrive in an AI-driven future,” said David Vanden Bout, UT’s interim executive vice president and provost, who resumes his post as dean of the College of Natural Sciences on Aug. 1. “This visionary support from the National Science Foundation will empower our world-class faculty and students to continue to push the boundaries of AI innovation, fostering breakthroughs in foundational machine learning that will influence almost every area of science and technology.”
The renewed funding will enable IFML to address key challenges related to best practices for training and fine-tuning large models, robustness and interpretability of deep networks, and domain adaptation across areas including protein engineering and AI for health. It will also enable IFML to support new postdoctoral fellows and graduate students and expand its efforts in workforce development, helping meet future demand for a highly skilled AI workforce by building upon the newly launched Master of Science in Artificial Intelligence degree program at UT.
“Machine learning is the engine that powers AI applications among industries all over the world, but is often proprietary and hard to use,” said IFML Director Adam Klivans, a University of Texas at Austin professor of computer science. “At IFML we are committed to open-source development so that everyone can apply our new models and algorithms. This openness directly translates into a wide-ranging impact across multiple fields.”
There are fewer than 30 NSF-led National Artificial Intelligence Research Institutes across the U.S., and two are based at UT: IFML and the NSF-Simons AI Institute for Cosmic Origins.
IFML comprises researchers from UT; 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; Boston College; and the University of Nevada, Reno.
Since its inception, IFML has been at the forefront of generative AI research, contributing to advancements in OpenCLIP and DataComp, tools that improve how AI systems understand images and text together. This continued investment from NSF, totaling $20 million over five years, will allow IFML to build upon its impressive track record and further solidify its position as a global leader in foundational machine learning.
