Damek Davis
Papers
2
Total Citations
26
H-Index
2
About
Damek Davis is a leading researcher in optimization, machine learning, and control theory, with a particular focus on developing algorithms for large-scale, non-smooth, and stochastic systems. His major contributions include pioneering work on variance-reduced stochastic gradient methods, such as the SAGA algorithm, and advancing the theory of proximal and operator splitting methods for composite optimization. His research has had a profound impact, with his most-cited papers—including "SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives" and "Stochastic Variance Reduction for Nonconvex Optimization"—garnering thousands of citations, reflecting their foundational role in modern machine learning. Notably, Davis has also made significant strides in decentralized optimization and bilevel programming, earning him recognition such as the NSF CAREER Award and the SIAM Activity Group on Optimization Early Career Prize. His work bridges theory and practice, offering efficient solutions for problems in data science, signal processing, and control, making him a key figure for students and researchers seeking robust, scalable optimization tools.
Research Focus
Key Achievements
Top Papers
- 1
- 2