About Khalil Elkhalil Khalil Elkhalil Postdoctoral Research Fellow, Electrical and Computer Engineering machine learning high dimensional statistics data science Random Matrix Theory Selected Applications statistical signal processing Supervised Learning Algorithms Feedback Reduction in Multiuser and Relay Networks PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology. Articles Related News August 2019 ISL Student Khalil ElKhalil does internship at Duke University 1 min read · Thu, Aug 8 2019 News Random Matrix Theory machine learning statistical finance ISL PhD student Khalil ElKhalil was a visiting Duke university from September 2018 to March 2019. Khalil was working with Vahid Tarokh on topics related to random matrix theory, machine learning and statistical finance. July 2019 ISL Student Khalil-ElKhalil goes to ICASSP 2019 1 min read · Tue, Jul 30 2019 News Khalill ElKhalil, an Electrical Engineering PhD student went to the ICASSP ( IEEE International Conference on Acoustics, Speech and Signal Processing). The confrence was held in Brighton, UK on May 12-17. ElKhalil went to present his work on LDA with random projections. May 2019 Measurement Selection: A Random Matrix Theory Approach 1 min read · Thu, May 30 2019 News Random Matrix Theory Measurement Wireless Communications Khalil Elkhalil , Student Member, IEEE, Abla Kammoun, Member, IEEE, Tareq Y. Al-Naffouri, Member, IEEE, and Mohamed-Slim Alouini , Fellow, IEEE Abstract This paper considers the problem of selecting a set of k measurements from n available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain m dimensional parameter vector. The exhaustive search inspecting each of the (n) possible choices would require very high computational k complexity and as such is not practical for large n and k. Alternative methods with low November 2017 Ph. D. Student Khalil Elkhalil nominated finalist for the Best Student Paper Award in the IEEE MLSP Conference 2 min read · Mon, Nov 20 2017 News Spotlight machine learning high dimensional statistics data science Fast-placing Machine Learning (ML) is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.
ISL Student Khalil ElKhalil does internship at Duke University 1 min read · Thu, Aug 8 2019 News Random Matrix Theory machine learning statistical finance ISL PhD student Khalil ElKhalil was a visiting Duke university from September 2018 to March 2019. Khalil was working with Vahid Tarokh on topics related to random matrix theory, machine learning and statistical finance.
ISL Student Khalil-ElKhalil goes to ICASSP 2019 1 min read · Tue, Jul 30 2019 News Khalill ElKhalil, an Electrical Engineering PhD student went to the ICASSP ( IEEE International Conference on Acoustics, Speech and Signal Processing). The confrence was held in Brighton, UK on May 12-17. ElKhalil went to present his work on LDA with random projections.
Measurement Selection: A Random Matrix Theory Approach 1 min read · Thu, May 30 2019 News Random Matrix Theory Measurement Wireless Communications Khalil Elkhalil , Student Member, IEEE, Abla Kammoun, Member, IEEE, Tareq Y. Al-Naffouri, Member, IEEE, and Mohamed-Slim Alouini , Fellow, IEEE Abstract This paper considers the problem of selecting a set of k measurements from n available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain m dimensional parameter vector. The exhaustive search inspecting each of the (n) possible choices would require very high computational k complexity and as such is not practical for large n and k. Alternative methods with low
Ph. D. Student Khalil Elkhalil nominated finalist for the Best Student Paper Award in the IEEE MLSP Conference 2 min read · Mon, Nov 20 2017 News Spotlight machine learning high dimensional statistics data science Fast-placing Machine Learning (ML) is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.
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