Chen Greif

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About

Chen Greif is a leading figure in applied and computational mathematics, with a primary focus on numerical linear algebra, iterative methods, and scientific computing. His most influential work includes the development of robust preconditioners for saddle-point systems and novel algorithms for solving large-scale sparse linear systems, which are critical in fields like fluid dynamics and electromagnetics. Among his notable contributions is the "Dip transform for 3D shape reconstruction" (2017), a creative method that reimagines the Archimedes principle for digital geometry—by dipping objects in virtual liquid across multiple orientations, the technique reconstructs three-dimensional shapes from volume displacement data. This work, while still emerging in impact, showcases Greif’s talent for bridging classical physics with modern computational challenges. His broader body of research, spanning over two decades, has garnered thousands of citations, reflecting its deep influence on both theory and practical algorithm design. Greif is also recognized for his pedagogical contributions, including co-authoring widely used textbooks on numerical methods. His research continues to shape how complex systems are solved efficiently, making him a key resource for students and researchers in computational science.