About Abla Kammoun Abla Kammoun Senior Research Scientist, Electrical and Computer Engineering Wireless Communications machine learning Abla Kammoun focuses on the development of random matrix theory tools in view of their applications to wireless communication, signal processing and machine learning. Events Presented Events Oct 19 - Oct 25, 2025 A Generalized Gaussian Min–Max Framework Inferring Post-Processing Effects in High-Dimensional Estimation Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering Oct 21, 14:30 - 15:30 B1 L3 R3119 statistical analysis This talk introduces the Convex Gaussian Min–Max Theorem (CGMT) as a principled framework to analyze the performance of such solutions, and presents a new Gordon-type inequality to study functionals of the solutions that arise when direct deployment is infeasible. Sep 26 - Oct 2, 2021 Discriminant analysis: Fundamental limits and Optimal designs Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering Sep 30, 12:00 - 13:00 KAUST Despite the recent advances in big data processing, enabled by the emergence of large-scale machine learning techniques, several statistical questions regarding the behavior in the regime of high dimensions of well-established and fundamental methods have remained unresolved. Apr 21 - Apr 27, 2019 Applications of random matrix theory for wireless communication and big data processing Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering Apr 25, 12:00 - 13:00 B9 L2 H1 Since the pioneer works of Telatar, random matrix theory has found a variety of applications in engineering disciplines that, to name a few, include wireless communication and signal processing. Its scope is now going far beyond the field of mathematics, being recognized as an indispensable tool for advanced research in engineering disciplines as can be evidenced by the dramatic increase in related publications. Recently, random matrix theory has found its way into the field of big data processing, allowing accurate characterization of the performance of many algorithms met in the field of machine learning.
A Generalized Gaussian Min–Max Framework Inferring Post-Processing Effects in High-Dimensional Estimation Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering Oct 21, 14:30 - 15:30 B1 L3 R3119 statistical analysis This talk introduces the Convex Gaussian Min–Max Theorem (CGMT) as a principled framework to analyze the performance of such solutions, and presents a new Gordon-type inequality to study functionals of the solutions that arise when direct deployment is infeasible.
Discriminant analysis: Fundamental limits and Optimal designs Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering Sep 30, 12:00 - 13:00 KAUST Despite the recent advances in big data processing, enabled by the emergence of large-scale machine learning techniques, several statistical questions regarding the behavior in the regime of high dimensions of well-established and fundamental methods have remained unresolved.
Applications of random matrix theory for wireless communication and big data processing Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering Apr 25, 12:00 - 13:00 B9 L2 H1 Since the pioneer works of Telatar, random matrix theory has found a variety of applications in engineering disciplines that, to name a few, include wireless communication and signal processing. Its scope is now going far beyond the field of mathematics, being recognized as an indispensable tool for advanced research in engineering disciplines as can be evidenced by the dramatic increase in related publications. Recently, random matrix theory has found its way into the field of big data processing, allowing accurate characterization of the performance of many algorithms met in the field of machine learning.
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