The University of Washington has always been a powerhouse of talent, but it’s got some new bragging rights—National Science Foundation-funded artificial intelligence research institutes
As part of a push to grow the next generation of science innovators, the US National Science Foundation recently announced it will spend $220 million across seven research areas and 11 new artificial intelligence institutes, and the University of Washington is a major beneficiary of the grants.
The first round came this summer, when five institutes were given the green light, including the UW-led AI Institute for Dynamic Systems which will focus on AI/ML theory, algorithms, and applications for real-time learning, especially in hectic situations where predicting an outcome is tough, such as turbulence or injury recovery. The goal of this institute, says the institute’s director and professor of applied mathematics, J. Nathan Kurtz, is to integrate physics-based models with AI/ML learning approaches to develop explainable solutions for complex issues in science and engineering.
In this same round, Paul G. Allen School of Computer Science & Engineering’s professor Sewoong Oh was tapped to head up the development of new AI tools and techniques to advance next-generation wireless edge networks as part of the Ohio State-led AI Institute for Future Edge Networks and Distributed Intelligence AI Institute for Future Edge Networks and Distributed Intelligence. That institute brings together 30 researchers from 18 universities plus industry partners and government labs with a goal of ensuring that networks are efficient, robust, and secure. Professor Oh plans to work on tools that will enable wireless edge networks to be self-healing and self-optimizing in a dynamic space. In the next few years, expect to see his innovations in various disciplines like telehealth, transportation, robotics, aerospace.
Professor Oh was also tapped—along with the Allen School’s Machine Learning Group and the UW Department of Statistics—to work on the new NSF AI Institute for Foundations of Machine Learning (IFML), led by University of Texas at Austin. That institute will use its $20 million to address fundamental problems in machine learning research and thus overcome today’s limitations in that field. This complements a $12.5 million Phase 2 funding of the NSF’s Transdisciplinary Research In Principles Of Data Science (TRIPODS), whose goal is to cross-pollinate various science departments at top universities to develop the theoretical foundations of data science. As part of that initiative, the UW’s own TRIPODS Institute on Algorithmic Foundations of Data Science (ADSI) is on the hunt for new algorithms and design principles that could lead to a common language for practitioners to use to tackle data science’s toughest challenges.
“UW is one of the nation’s preeminent research universities. It’s been in the Top 5 among Federal research funding every year since 1969—not a misprint!—and AI is a particular strength of ours,” says Ed Lazowska, professor and Bill & Melinda Gates Chair Emeritus at UW’s Allen School. “UW has top people in machine learning, robotics, computer vision, and natural language processing and close relationships in these fields with the Allen Institute for AI, Amazon, Apple, Facebook, Google, Microsoft, and NVIDIA. It makes sense that UW is a participant in multiple major National Science Foundation centers in AI and in machine learning.”