Our lab's mission is to design and develop effective, efficient, and trustworthy AI algorithms and tools that are inspired by unique challenges from interdisciplinary applications. Our vision is that future AI research requires the convergence of multiple disciplines, as real-world problems are so complex that one size AI does not fit all. We value both technical innovations in ML methodologies and tool deployment to solve a real problem that benefits society.
Current research topics include (but not limited to) spatiotemporal data mining, graph neural networks, self-supervised and weakly-supervised learning, physics-informed machine learning, trustworthy AI (bias, uncertainty, interpretability), large-scale distributed machine learning, as well as interdisciplinary applications in Earth sciences (e.g., hydrology, oceanography, natural disasters), agriculture, transportation and smart cities, health and medicine. Our lab has established collaborations with US Geological Survey, NOAA, NASA, NGA, Los Alamos National Lab, UF Center of Coastal Solutions, and UF College of Medicine.
This material is based upon work supported by the National Science Foundation (NSF) under Grant No. IIS-2147908, IIS-2207072, CNS-1951974, OAC-2152085, and the National Oceanic and Atmospheric Administration (NOAA), UFII Seed Grant, Microsoft AI for Earth Grant and the Extreme Science and Engineering Discovery Environment (XSEDE).
