Benjamin Grimmer

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I am an assistant professor in the Johns Hopkins Department of Applied Math and Statistics, joining after finishing my PhD in Operations Research at Cornell University, advised by Jim Renegar and Damek Davis. I spent Spring 2020 with Google Research working on adversarial optimization and Fall 2017 at UC Berkeley as part of a Simons Institute program bridging continuous and discrete optimization.

My current research focuses on the design and analysis of algorithms for continuous optimization problems beyond the areas where classical theory applies. For example, the selected works below all address fundamental issues in modern optimization problems, bridging the gap between classical approaches and the potentially stochastic, nonconvex, nonsmooth, nonLipschitz, adversarial objectives employed on many modern data science problems. During my PhD, I was awarded an NSF Graduate Research Fellowship supporting this research.

Office: N419 Wyman Hall
Email: grimmer at
CV: here
Twitter: @prof_grimmer (mostly sharing pretty 3D prints)

Selected Recent Papers

Accelerated Gradient Descent via Long Steps Youtube, arXiv
Benjamin Grimmer, Kevin Shu, Alex L. Wang.

Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization arXiv
Benjamin Grimmer, Danlin Li.

Radial Duality Part I: Foundations and Part II: Applications and Algorithms Mathematical Programming, 2023
Benjamin Grimmer. arXiv: Part I, Part II

The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization Mathematical Programming, 2022
Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni. arXiv

Student Advisees and Collaborators

Ning Liu (PhD Candidate) Johns Hopkins, AMS
Thabo Samakhoana (PhD Candidate) Johns Hopkins, AMS
Alan Luner (PhD Student) Johns Hopkins, AMS
Eleanor Belkin (PhD Student) Johns Hopkins, AMS
Shengyi Yan (Masters) Johns Hopkins, AMS
Zhichao Jia (Masters) Johns Hopkins, AMS
Danlin Li (Masters) Johns Hopkins, AMS
Raj Gosain (Undergraduate) Johns Hopkins, AMS