UWashington Distinguished Seminar in Optimization and Data: Damek Davis

Nonsmoothness and nonconvexity are significant challenges in large-scale optimization problems, such as training neural networks and solving inverse problems in computational imaging. Although first-order methods are commonly used to address these problems, they are often considered slow. In this presentation, we introduce a (locally) accelerated first-order method that violates this perception by solving "generic" nonsmooth equations at a superlinear rate. The central insight is that nonsmooth functions often exhibit "partial" smoothness, which can be leveraged through a novel generalization of Newton's method.

Speaker biography: Damek Davis is an Associate Professor of Operations Research at Cornell University. He received his Ph.D. in mathematics from the University of California, Los Angeles in 2015. His research focuses on the interplay of optimization, signal processing, statistics, and machine learning. He has received several awards for his work, including a Sloan Research Fellowship in Mathematics (2020), the INFORMS Optimization Society Young Researchers Prize (2019), an NSF CAREER Award (2021), and the SIAM Activity Group on Optimization Best Paper Prize (2023).