Benjamin Grimmer is an assistant professor of applied mathematics and statistics. He is also a member of the Mathematical Institute for Data Science (MINDS) and the Data Science & AI Institute. His work focuses on the design and analysis of algorithms for continuous optimization, ranging from classic topics like gradient descent and conic settings to stochastic, nonconvex, nonsmooth, adversarial problems as arise in modern machine learning. Recently, one branch of Grimmer’s work has used computer-assisted proof machinery to develop state-of-the-art algorithms and convergence theory, beyond what can classically be accomplished by hand. His results from these cutting-edge tools were featured in Quanta magazine’s 2023 article “Risky Giant Steps Can Solve Optimization Problems Faster”.
Grimmer was named an Alfred P Sloan Fellow in Mathematics in 2024. Additionally, Grimmer’s work is supported by the US Air Force Office of Scientific Research. During his doctorate in operations research at Cornell University, he was supported by a National Science Foundation fellowship. He also spent semesters at Google Research working on adversarial optimization and at the Simons Institute as part of a program bridging continuous and discrete optimization.
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Hopkins Engineers recognized for their potential to become leaders in their fields.