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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

Borjan Geshkovski Applied Analysis, Inverse Problems

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

Sung Woo Jeong Numerical Linear Algebra, Random Matrix Theory

Zin Lin

Graduate Students*

Rodrigo Arrieta Candia Numerical methods for PDEs, Numerical Analysis, Scientific Computing, Computational Electromagnetism

Mo Chen Optimization, Scientific Computing

Max Daniels High-dimensional statistics, optimization, sampling algorithms, machine learning

Sarah Greer Imaging, inversion, signal processing

George Stepaniants Statistical Learning of PDEs, Continuous Neural Networks

*Only a partial list of graduate students