Vipin Kumar
Knowledge-guided machine learning; AI for climate and environmental systems; hydrologic modeling; Earth system data science; scientific machine learning; spatiotemporal data mining
Background & Expertise
Vipin Kumar is a Regents Professor and William Norris Chair in the Department of Computer Science and Engineering at the University of Minnesota. His research focuses on advancing artificial intelligence for scientific discovery, with particular emphasis on climate, hydrology, agriculture and Earth system science.
Kumar pioneered the paradigm of Knowledge-Guided Machine Learning (KGML), which integrates scientific knowledge and physical principles directly into AI models to improve their robustness, interpretability and ability to generalize under changing environmental conditions. His work has helped establish data-driven approaches as a complement to traditional process-based modeling in climate and environmental science. Applications of his research include flood forecasting, streamflow prediction, satellite-based environmental monitoring and sustainability assessment. He has led several large multi-institution initiatives, including an NSF Expeditions in Computing project on understanding climate change through data-driven methods. A recipient of major honors including the ACM SIGKDD Innovation Award and the IEEE Taylor L. Booth Education Award, Kumar works closely with federal agencies and interdisciplinary partners to advance scientifically grounded, operationally deployable AI for environmental resilience.
Kumar currently serves on the United Nations Independent International Scientific Panel on Artificial Intelligence, the first global scientific body established by the UN General Assembly to provide independent, evidence-based assessments of AI.