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.

Department Members in This Field


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

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 Differential Equations, Optimal Transport in Biology

*Only a partial list of graduate students