Learning from Data: Optimization and Low-Rank Factorization
Friday, June 14, 12-1 p.m.
EN-4000
Lunch and learn session.
Abstract
Inspecting data to uncover valuable information is critical, especially with the increasing data from advancing technologies. Low-rank factorization techniques, such as nonnegative matrix factorization (NMF), exploit matrix structures to handle missing values, detect anomalies and reduce dimensionality for knowledge extraction. This talk will cover NMF, its optimization methods and practical applications in revealing latent information from nonnegative data.
Bio
Flavia Esposito is an assistant professor at the University of Bari Aldo Moro, with a PhD in informatics and mathematics. Her research focuses on dimensionality reduction, matrix decompositions, and optimization in machine learning, particularly in bioinformatics and environmental science. She has organized scientific events and has been a research fellow at IRCSS-Tumori Giovanni Paolo II and at Ingegneria Elettrica e dell’Informazione, Politecnico di Bari.
Presented by Engineering Research Office