Jacob Fish

Jacob Fish holds the Rosalind and John J. Redfern Jr. Chair in Engineering at Columbia University's Department of Civil Engineering and Engineering Mechanics within the Fu Foundation School of Engineering and Applied Science. His research program focuses on computational mechanics and multiscale modeling with applications across material science and structural engineering.

His research interests center on developing advanced computational frameworks for multiscale analysis of heterogeneous materials. Key areas include computational continua, atomistic-to-continuum coupling, fracture mechanics of composites, and thermomechanical modeling of advanced materials. His work bridges theoretical developments with practical engineering applications through reduced-order modeling and data-physics integration.

His recent publications demonstrate strong trends in multiscale computational engineering, particularly in homogenization techniques, phase-field fracture modeling, and data-driven approaches for material behavior prediction. The research spans from atomistic simulations to structural-scale analysis with emphasis on computational efficiency and physical fidelity.

• Fellow, U.S. Association for Computational Mechanics (USACM)
• Computational Structural Mechanics Award, 2005
• Fellow, International Association for Computational Mechanics (IACM), 2002
• National Science Foundation Presidential Young Investigator Award, 1992
• Walter P. Murphy Fellowship, Northwestern University, 1986

Fish serves as Editor-in-Chief of the International Journal for Multiscale Computational Engineering and has secured numerous research grants focused on multiscale modeling of advanced materials. His collaborative network spans multiple institutions and disciplines, particularly in computational mechanics and material science. His laboratory develops computational frameworks for multiscale analysis with applications in structural engineering, material science, and biomechanics, focusing on efficient algorithms for complex material behavior prediction.