Philip Greengard
About Me
I am a Postdoctoral Research Scholar at Columbia
University in Andrew Gelman's applied statistics group.
I work on developing efficient numerical methods for
computing in statistical environments.
I obtained a PhD in Applied Mathematics in 2019 from Yale
where I worked with Vladimir Rokhlin. Here's a
short cv.
Research Interests
My research is primarily focused on analysisbased algorithms for
computational statistics and scientific computing.
Publications and Preprints

Efficient Fourier representations of families of Gaussian processes

Fast methods for posterior inference of twogroup normalnormal models (with J. Hoskins, C. Margossian, A. Gelman, A. Vehtari)

Efficient reducedrank methods for Gaussian processes with eigenfunction expansions (with M. O'Neil)

A Fast Regression via SVD and Marginalization (with A. Gelman and A. Vehtari)

The piranha problem: Large effects swimming in a small pond (with C. Tosh, B. Goodrich, A. Gelman, D. Hsu)

Factor Clustering with tSNE (with Y. Liu, S. Steinerberger, A. Tsyvinski)
 On Generalized Prolate
Spheroidal Functions (with K. Serkh)
 Zernike Polynomials:
Evaluation, Integration, and Interpolation (with K. Serkh)

An Algorithm for the Evaluation of the Incomplete Gamma Function
(with V. Rokhlin)
Contact Me
pg2118 at columbia.edu