Seminars and Events
High Performance Learning with the Unite and Conquer Approach
Abstract: The ever-increasing production of data requires new methodological and technological approaches to meet the challenge of their effective analyses. We highlight the omnipresence of certain linear algebra methods such as the eigenvalue problem or more generally the singular value decomposition in machine learning techniques. A new machine learning approach based on Unite and Conquer methods, used in linear algebra, will be presented. The important characteristics of this intrinsically parallel and scalable technique make it very well suited to multi-level and heterogeneous parallel and/or distributed architectures. We also underline the important role that platforms like YML and Pegasus can play to facilitate the implementation of these methods on these architectures. Experimental results, demonstrating the interest of the approach for efficient data analysis in the case of clustering, cybersecurity and road traffic simulation will be presented.
Nahid Emad has received the Habilitation to Direct Research in computer science from university of Paris Saclay/Versailles in 2001, the PhD and MS in applied mathematics from Pierre et Marie Currie university (Sorbonne University) in 1989 and 1983 and BS from university of Arak (Iran) in 1980. She is Professor at university of Paris Saclay/Versailles and affiliated to Maison de la Simulation and LI-PARAD laboratories where she leads the Intensive Numerical Computation group. She has been scientific director of 20 PhD and HDR and has authored more than 150 articles in international journals, conferences, and books chapters. Her main research interests are in numerical algorithms, linear algebra, parallel and distributed programming methodology, software engineering for parallel and distributed numerical computing and big data analytics.