Most of my research incorporates the development of fast
high-order analysis-based algorithms into problems in
computaitonal physics, statistics, integral equations,
singular quadrature, and in general, computational
science. Almost all problems are rooted in relevant
Integral equations, computational physics, and fast algorithms
Almost all partial differential equation occuring in classical mathematical physics can be reformulated as integral equations with an appropriate Green's function. Proper integral formulations are usually very stable, but result in large dense systems which require fast algorithms to solve. Analysis-based algorithms such as fast multipole methods, butterfly algorithms, etc. allow the rapid solution to these systems. I have worked on particular problems in electromagnetics, acoustics, and magnetohydrodynamics.
Recently it has been observed that many of the fast analysis-based algorithms used throughout engineering physics have direct applications in statistics, machine learning, and data analysis. In particular, methods for rapidly inverting similarly structured dense covariance matrices have immediately found applications in Gaussian processes.
Complementary to solving PDEs or integral equations, algorithms which stably and rapidly compute special functions, invert matrices, apply operators, etc. must be developed. These schemes fall broadly under numerical analysis, and constitute the components that go into necessary software toolboxes for applied mathematics.
Also visit the Courant Mathematics and Computing Laboratory and the Greengard research group page for related research.
Below are slides from a few selected talks that I've given over the years.
The largest computational task encountered when modeling using Gaussian processes
is the inversion of a (dense) covariance matrix. Often, these matrices have a systematic
structure that can be exploited. george is a Python wrapper for a C++ implementation
of the HODLR factorization. An optimized Fortran version
is currently in development.
george - HODLR
Two-dimensional and three-dimensional fast multipole codes
developed by Leslie Greengard and Zydrunas Gimbutas for
Laplace, Helmholtz, elastostatic, and Maxwell potentials can
be downloaded on the CMCL webpage.
Tentatively I will teach a course on integral equations at NYU-Poly Spring 2015.
A projects mentoring course for students concentrating on a data science track
within the computational science masters program at the Courant Institute.
Introductory linear algebra.
Fall 2012 & Spring 2012
This is the crowning projects course for students enrolled
in the Data Science masters program at NYU through the
Center for Data Science. Working with industry and/or
faculty mentors, students complete and present a thorough
treatment of a real-world data science project.
This course is a junior/senior level introduction to the mathematical
theory of statistics to be taken after a similarly focused course on the
theory of probability has been taken.
Spring 2014 & Spring 2013