Kumar Keynote Speaker at 2024 NSF HDR Conference
CSE DSI Director Vipin Kumar was the keynote speaker at the 3rd annual 2024 NSF HDR Ecosystem Conference (Harnessing the Data Revolution) at the University of Illinois Urbana-Champaign (September 9-12, 2024).
Kumar’s talk, “Knowledge-Guided Machine Learning: A New Framework for Accelerating Scientific Discovery and Addressing Global Environmental Challenges”, presented an overview of a new generation of machine learning algorithms, where scientific knowledge is deeply integrated in the design and training of machine learning models to accelerate scientific discovery. These knowledge-guided machine learning (KGML) techniques are fundamentally more powerful than standard machine learning approaches, and are particularly relevant for scientific and engineering problems that are traditionally addressed via process-guided (also called mechanistic or first principle-based) models, but whose solutions are hampered by incomplete or inaccurate knowledge of physics or underlying processes. While this talk illustrated the potential of the KGML paradigm in the context of environmental problems (e.g., ecology, hydrology, agronomy, climate science), the paradigm has the potential to greatly advance the pace of discovery in any discipline where mechanistic models are used.
NSF’s Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity founded in 2016 to enable new modes of data-driven discovery that address fundamental questions at the frontiers of science and engineering. By engaging NSF's research community in the pursuit of fundamental research in data science and engineering, the ecosystem of HDR Institutes, Data Science Corps (DSC), and Transdisciplinary Research in Principles of Data Science (TRIPODS) strive to provide a cohesive, federated, national-scale approach to research data infrastructure, and the development of a 21st-century data-capable workforce.
View a video of the talk (Total video length: 1:08:47. Talk ends at 51:48 and is followed by a question and answer session.)
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