Ethan N. Epperly

Ethan N. Epperly is a Miller Research Fellow in the Department of Mathematics at the University of California, Berkeley. His research focuses on Applied Mathematics and Probability, with specific interests in Randomized and quantum algorithms, scientific computing, and large-scale machine learning. Appointed in 2025, he is a Faculty Postdoc and can be contacted via email at eepperly@berkeley.edu. His personal website provides additional information about his work and research activities.

Ethan N. Epperly Miller is a research fellow at UC Berkeley, focusing on Randomized Algorithms, Mathematics of Data Science, Matrix Computations, and Quantum Algorithms. His work explores efficient and stable algorithms for large-scale linear algebra problems, including randomized least-squares solvers, kernel matrix approximation, and quantum subspace diagonalization. He has developed methods for stochastic trace estimation, preconditioning for kernel ridge regression, and randomized preconditioning techniques. His research emphasizes practical approximation of kernel matrices and the application of structured random matrices to improve computational efficiency and accuracy in data science and quantum computing.