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Computational Science & Numerical Analysis

Computational science is a key area related to physical mathematics. The problems of interest in physical mathematics often require computations for their resolution. Conversely, the development of efficient computational algorithms often requires an understanding of the basic properties of the solutions to the equations to be solved numerically. For example, the development of methods for the solution of hyperbolic equations (e.g. shock capturing methods in, say, gas-dynamics) has been characterized by a very close interaction between theoretical, computational, experimental scientists, and engineers.


Laurent Demanet Applied analysis, Scientific Computing

Alan Edelman Parallel Computing, Numerical Linear Algebra, Random Matrices

Steven Johnson Waves, PDEs, Scientific Computing

Gilbert Strang Numerical Analysis, Partial Differential Equations

Instructors & Postdocs

Peter Baddoo Fluid Dynamics, Complex Analysis, Machine Learning

Keaton Burns PDEs, Spectral Methods, Fluid Dynamics

Pui Tung Choi Applied and Computational Geometry, Metamaterials, Quantitative Biology, Medical Imaging

Andrew Horning Numerical Analysis, Scientific Computing, Large-Scale And Infinite-Dimensional Spectral Problems

Adam Kay Hydrodynamic Quantum Analogues

Zin Lin

Raphaƫl Pestourie Inverse design in nanophotonics, efficient approximate solvers in optics, deep surrogate for PDES.

Adrien Scheuer Multiscale modeling, model order reduction, scientific machine learning

Graduate Students*

Mo Chen Optimization, Scientific Computing

Sarah Greer Imaging, inversion, signal processing

Sung Woo Jeong Numerical Linear Algebra, Random Matrix Theory

George Stepaniants Statistical Learning of PDEs, Continuous Neural Networks

*Only a partial list of graduate students