Howard Elman

Howard Elman is a Professor in the Department of Computer Science at the University of Maryland, with affiliations to the Institute for Advanced Computer Studies (UMIACS) and as an Affiliate Professor in the Department of Mathematics. His research spans numerical analysis, computational fluid dynamics, and uncertainty quantification, focusing on iterative solvers for partial differential equations.

• Education: PhD in Computer Science, Yale University (1982); BA in Mathematics, Columbia University (1975); Stuyvesant High School (1971)

Elman's research integrates Scientific Computing with Numerical Linear Algebra, Computational Fluid Dynamics, and Uncertainty Quantification. His work addresses Stochastic Galerkin Methods, Reduced-Order Modeling, and Low-Rank Approximations for PDEs with random data. Recent publications emphasize Surrogate Models and Deep Learning in Bayesian inverse problems.

His scientific awards include SIAM Fellowship (2009) and roles as Associate Editor for journals like Mathematics of Computation and SIAM Journal on Scientific Computing. He served as SIAM Editor-in-Chief (1998-2004) and Vice President for Publications.

Contact: helman@umd.edu | Office: 4210 Iribe Center | Courses: AMSC/CMSC 460 Computational Methods