This Project Grant award of $300,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports a research initiative at Cornell University focused on Lipschitz optimization methods and their applications in modern data science. The project aims to transform the design and analysis of optimization algorithms to address challenges posed by big data, with potential impacts across domains such as robust control engineering and computational biology. Key activities include extending classical mathematical techniques to non-Euclidean optimization settings, analyzing condition measures of nonsmoothness and nonconvexity, and developing new intuitive algorithms for practitioners in machine learning, high-dimensional statistics, and imaging science. The research will involve graduate student collaborations, dissemination through publications and international lectures, and incorporation into graduate coursework. This award reflects NSF's mission to support fundamental research that strengthens the nation's scientific enterprise.
Generated 3/18/25, 3:13 AM