Carlos Fernandez-Granda

Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, affiliated with the Department of Mathematics. His research focuses on developing and analyzing optimization-based methods to tackle problems in applications such as neuroscience, computer vision, and medical imaging. Fernandez-Granda holds a Ph.D. from Stanford University and has contributed to significant publications in mathematical and applied fields. His work is part of the Center for Data Science at NYU.

Carlos Fernandez-Granda is a researcher at the Courant Institute and Center for Data Science, New York University, focusing on machine learning, image processing, data-driven medicine, signal processing, and inverse problems. His work integrates mathematical theory with practical applications, particularly in biomedical imaging and signal recovery. He has developed deep learning models for early Alzheimer’s disease detection, super-resolution techniques for noisy data, and methods for robust image denoising. His research also includes benchmarking machine learning in ocean modeling and developing data-driven approaches for medical diagnostics. Fernandez-Granda's contributions span both theoretical and applied domains, emphasizing the intersection of machine learning and signal processing in healthcare and scientific discovery.