Manolis Zampetakis

Manolis Zampetakis is an Assistant Professor in the Department of Computer Science at Yale University. His research interests focus on areas related to computer science, though specific details are not provided. He is affiliated with the Yale University Computer Science department and contributes to academic study within this field. His profile page provides further information about his work and contributions.

Manolis Zampetakis is a researcher at Yale University with a focus on theoretical computer science and machine learning. His work explores the intersection of optimization, statistics, and computational complexity, particularly in high-dimensional settings. He investigates problems such as truncated regression, constrained min-max optimization, and efficient statistics from truncated samples. His research also addresses computational complexity in areas like cryptographic hardness and fixed-point theorems. Zampetakis contributes to understanding the theoretical foundations of learning algorithms, including models like the Mallows block model and compressed sensing with generative priors. His publications highlight the development of efficient algorithms and the analysis of their convergence properties.