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 Pre-prints

• 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 two-group normal-normal models (with J. Hoskins, C. Margossian, J. Gabry, A. Gelman, A. Vehtari)
• Efficient reduced-rank 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 t-SNE (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