Benjamin Grimmer, an assistant professor of applied mathematics and statistics, focuses on the design and analysis of algorithms for continuous optimization problems beyond the limited areas where classical theory applies. His goal is to address fundamental issues in modern optimization problems, bridging the gap between classical approaches and the potentially stochastic, nonconvex, nonsmooth, adversarial models employed on many modern data science and machine learning problems.
Grimmer came to Johns Hopkins in the fall of 2021 after finishing his doctorate in operations research at Cornell University, supported by the National Science Foundation. Previously, he worked on adversarial optimization at Google Research, and at the University of California, Berkeley as part of a Simons Institute program bridging continuous and discrete optimization.
He received his bachelor’s and master’s degrees in Computer Science, with a minor in Applied Mathematics, at the Illinois Institute of Technology in 2016.