Su Jiang

Su Jiang’s research focuses on scientific machine learning, uncertainty quantification, and optimization for subsurface flow processes. She integrates AI methods with domain knowledge to enable efficient prediction, inference and management in large-scale energy and environmental systems. Applications of her work include geological carbon storage, hydrocarbon production, enhanced geothermal systems, and seawater intrusion.

Jiang received her Ph.D. in energy resources engineering at Stanford University in 2022 and her B.S. in environmental engineering from Tsinghua University in 2016. She conducted her postdoctoral research at Stanford University and Lawrence Berkeley National Laboratory.