I’m a PhD candidate in Applied and Computational Mathematics at Caltech, whose research is focused on designing computational techniques for solving large-scale computational problems in machine learning, quantum information, and scientific computing. My advisor is Joel A. Tropp.
I got my undergraduate degrees in Mathematics and Computing from the College of Creative Studies at UCSB, where I worked with Professor Shivkumar Chandrasekaran and the scientific computing group. I have had internships at Sandia National Lab, working with Ryan Sills, Don Ward, and Jonathan Hu; Lawrence Livermore National Lab, working with Andrew Barker; and Lawrence Berkeley National Lab, working with Lin Lin.
I have been recognized for my work with the UCSB Chancellor’s Award for Excellence in Undergraduate Research, the UCSB Mathematics Department’s Raymond L Wilder Award, finalist status for the Hertz foundation fellowship, and the Caltech Thomas A. Tisch Prize for Graduate Teaching in CMS. I grateful to have been supported by the Department of Energy Computational Science Graduate Fellowship.
Recent publications:
- E. N. Epperly, A. Greenbaum, & Y. Nakatsukasa (2025). Stable algorithms for general linear systems by preconditioning the normal equations. arXiv preprint arXiv:2502.17767.
- H. Wilber, E. N. Epperly, & A. H. Barnett (2025). Superfast direct inversion of the nonuniform discrete Fourier transform via hierarchically semi-separable least squares. SIAM Journal on Scientific Computing. (preprint)
- E. N. Epperly, G. Goldshlager, & R. J. Webber (2025). Randomized Kaczmarz with tail averaging. arXiv preprint arXiv:2411.19877.
Some of my favorite projects:
- Y. Chen, E. N. Epperly, J. A. Tropp, & R. J. Webber (2025). Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations. Communications on Pure and Applied Mathematics. (preprint)
- E. N. Epperly, M. Meier, & Y. Nakatsukasa (2025). Fast randomized least-squares solvers can be just as accurate and stable as classical direct solvers. Communications on Pure and Applied Mathematics, accepted. (preprint)
- E. N. Epperly, J. A. Tropp, & R. J. Webber (2024). XTrace: Making the most of every sample in stochastic trace estimation. SIAM Journal on Matrix Analysis and Applications. (preprint)
- E. N. Epperly, L. Lin, & Y. Nakatsukasa (2022). A theory of quantum subspace diagonalization. SIAM Journal of Matrix Analysis and Applications. (preprint)