Lijing Wang, soon to join UConn in August as an assistant professor of geosciences in the College of Liberal Arts and Sciences, has been selected as one of the pioneering scientists in the U.S. to receive support from the National Artificial Intelligence Research Resource (NAIRR) Pilot.
This initiative, a nationwide infrastructure, aims to provide U.S. researchers with the necessary computational data, software, models, and training to conduct paradigm-shifting AI research.
Announced jointly by the U.S. National Science Foundation (NSF) and the Department of Energy (DOE), the NAIRR Pilot has awarded computational time to the first 35 projects, marking a significant step in fostering responsible AI research and ensuring equitable access to AI tools across the country.
The NAIRR Pilot is dedicated to supporting fundamental, translational, and use-inspired AI-related research, with a focus on addressing societal challenges. Initial priority areas include the development of safe, secure, and trustworthy AI, advancements in human health, and initiatives related to the environment and infrastructure.
Wang’s project has been allocated 10,500 node hours at the DOE Argonne National Laboratory AI Testbed. This allocation allows her to investigate water flow in mountainous regions, where limited data on snow melt and water movement pose challenges to accurate predictions of future water flow patterns.
“Mountainous watersheds are critical water sources,” Wang explains. “While intensive monitoring is essential for understanding water availability, it is not always feasible in every catchment. By complementing monitoring efforts with AI tools, we can more efficiently evaluate water variations, particularly in the context of climate change.”
Her research involves simulating water movement across diverse mountain slopes under varying conditions. The outcomes will generate a comprehensive dataset for developing an AI model capable of predicting snow melt, water flow dynamics, and groundwater levels. These predictive capabilities will enable faster water forecasting, thereby enhancing water management strategies and informing climate change studies.
Out of the 35 selected projects, 27 will receive support through NSF-funded advanced computing systems, while the remaining eight projects will utilize DOE-supported systems.
Source: today.uconn.edu
