A National Science Foundation (NSF) artificial intelligence institute at The University of Texas at Austin will continue to receive funding for research aimed at increasing the accuracy and reliability of AI models. This work is expected to support advancements in drug development and clinical diagnosis.
The NSF AI Institute for Foundations of Machine Learning (IFML) focuses on developing the next generation of artificial intelligence systems. Its research includes mathematical approaches to diffusion models used for image denoising, algorithms that enhance magnetic resonance imaging (MRI), and biotechnology innovations with potential impacts on 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.”
With renewed funding, IFML plans to tackle challenges related to training large models, improving deep network robustness and interpretability, and adapting AI across domains such as protein engineering and health care. The funding will also help support new postdoctoral fellows and graduate students while expanding workforce development efforts through programs like UT’s Master of Science in Artificial Intelligence degree.
“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 in the United States; two are based at UT: IFML and the NSF-Simons AI Institute for Cosmic Origins.
IFML brings together researchers from several institutions including University of Washington, Wichita State University, Microsoft Research, Stanford University, Santa Fe Institute, UCLA, UC Berkeley, California Institute of Technology, Boston College, and University of Nevada Reno.
Since its creation, IFML has contributed to generative AI by advancing tools such as OpenCLIP and DataComp that improve how systems process images alongside text. The NSF investment totals $20 million over five years for this initiative.
