Philip Greengard
About Me
I am a Postdoctoral Research Scholar at Columbia
University in Andrew Gelman's applied statistics group.
I work on both applied statistics and developing efficient
numerical methods statistical inference.
I got a PhD in Applied Mathematics in 2019 from Yale
where I worked with Vladimir Rokhlin. Here's a
short cv.
Publications and Preprints

Uniform approximation of common Gaussian process kernels using equispaced Fourier grids
(with A. Barnett, M. Rachh)

BISG: When inferring race or ethnicity, does it matter that people often live
near their relatives?
(with A. Gelman)

Learning to grow pretrained models for efficient transformer training
(with P. Wang, R. Panda, L. T. Hennigen, L. Karlinsky, R. Feris, D. D. Cox, Z. Wang, Y. Kim)

Federated learning as variational inference: A scalable expectation propagation approach (with H. Guo, H. Wang, A. Gelman, E. Xing, Y. Kim)

Equispaced Fourier representations for efficient Gaussian process regression from a billion data points (with A. H. Barnett, M. Rachh)

On a linearization of quadratic Wasserstein distance (with J. Hoskins, N. Marshall, A. Singer)

Efficient Fourier representations of families of Gaussian processes

Fast methods for posterior inference of twogroup normalnormal models (with J. Hoskins, C. Margossian, J. Gabry, 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