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, geo-foundation models, physics-informed machine learning, graph neural networks, self-supervised and weakly-supervised learning, trustworthy AI (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.