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- H33D-01: Knowledge-guided Machine Learning: Harnessing the Potential of AI for Accelerating Geosciences by Incorporating Scientific Knowledge (invited)
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NOLA CC
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Anuj Karpatne, Virginia Polytechnic Institute and State University (First Author, Presenting Author)
This talk will introduce knowledge-guided machine learning (KGML), a rapidly growing field of research where scientific knowledge is deeply integrated in machine learning frameworks to produce scientifically grounded, explainable, and generalizable predictions even on out-of-distribution data. This talk will present a multi-dimensional view to organize prior research in KGML in terms of the nature and format of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML for diverse scientific use-cases. These KGML concepts will be illustrated using a variety of case studies in ecology, biology, and public health including modeling the quality of water in lakes across the US and discovering novel biological traits of organisms linked with evolution from biodiversity images. The talk will conclude with a discussion of emerging opportunities in KGML especially in the age of generative AI and Foundation models with potential applications in a broad range of scientific disciplines.
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