College of Science & Engineering
This group has two major research tracks:
- Physics-Guided Machine Learning: Data-driven approaches that have been highly successful in other scientific disciplines hold significant potential for application in environmental sciences. These researchers are working towards building hybrid models that combine physics-based models with data-driven models to improve accuracy for different earth science applications, including lake temperature modeling and river flow modeling.
- Monitoring Land Cover Change at Global Scale: The key focus of this project is to develop new computer science methods and tools that enable effective monitoring of various land cover changes happening on the earth's surface. This group has analyzed various land cover changes such as forest fires, deforestation, insect infestation in forests, urbanization, agricultural changes, and monitoring of water resources. The group has made significant improvements in monitoring forest fires at global scale. Currently, the major focus has been on monitoring change in water bodies across the globe using remote sensing datasets.