Paul Beckman

Program Year:

University:

New York University

Field of Study:

Mathematics

Advisor:

Michael O'Neil

Degree(s):

B.S. Computational and Applied Mathematics, University of Chicago, 2019

Practicum Experience(s)

Lawrence Berkeley National Laboratory (2023)

Practicum Supervisor(s):

Xiaoye

Practicum Title:

Butterfly factorization from matrix-vector products

Annual Program Review Abstracts

Butterfly-Accelerated Gaussian Random Fields on Manifolds (2024)

Boundary Integral Methods for Computing Covariances in Inverse Source Problems (2023)

Fast Algorithms for Elliptic PDEs With Gaussian Boundary Noise (2022)

Publications

Paul G. Beckman, Christopher J. Geoga. “Fast Adaptive Fourier Integration for Spectral Densities of Gaussian Processes.” Statistics and Computing, To appear.

Beckman, Paul G., Christopher J. Geoga, Michael L. Stein, and Mihai Anitescu. "Scalable computations for nonstationary Gaussian processes." Statistics and Computing 33, no. 4 (2023): 84.

Williams-Young, David B., Paul G. Beckman, and Chao Yang. "A Shift Selection Strategy for Parallel Shift-Invert Spectrum Slicing in Symmetric Self-Consistent Eigenvalue Computation." ACM Transactions on Mathematical Software (TOMS) 46, no. 4 (2020): 1-31.

Skluzacek, Tyler J., Rohan Kumar, Ryan Chard, Galen Harrison, Paul Beckman, Kyle Chard, and Ian Foster. "Skluma: An extensible metadata extraction pipeline for disorganized data." In 2018 IEEE 14th International Conference on e-Science (e-Science), pp. 256-266. IEEE, 2018.

Beckman, P. G., J. L. Fattebert, E. Y. Lau, and D. Osei-Kuffuor. "A geometric initial guess for localized electronic orbitals in modular biological systems." No. LLNL-TR-738503. Lawrence Livermore National Lab Summer Poster Session 2017. (LLNL), 2017.

Awards

Courant Institute - Moses A. Greenfield Research Prize

University of Chicago - Computational and Applied Mathematics Departmental Honors (2019)