Robert J. Webber

Robert J. Webber is an Assistant Professor in the Department of Mathematics at the University of California, San Diego (UCSD). He holds a Ph.D. in Mathematics from New York University (2021) and previously served as a postdoctoral scholar at the California Institute of Technology (Caltech) from 2021 to 2024, hosted by Joel A. Tropp.

Research Focus: Webber specializes in randomized numerical methods for data science and scientific computing, including interdisciplinary applications in chemistry, astronomy, and geophysics. His work emphasizes randomized algorithms for matrix approximation, extreme-scale linear algebra, and rare event sampling. Notable projects include the randomly pivoted Cholesky method for kernel matrices and randomly sparsified Richardson iteration for large-scale linear systems.

Teaching: He teaches probability and data science courses at the upper-undergraduate and graduate levels at UCSD. His collaborative projects include studying Mercury’s orbital instability and extreme weather patterns using rare event sampling techniques.

Publications: Over 30 peer-reviewed articles since 2019, with notable contributions to journals like Communications on Pure & Applied Mathematics and SIAM Journal on Scientific Computing. His work has been featured in Forbes, SIAM News, and the California Business Journal.