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Ali H. Sayed
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 766
Citations - 39568
Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.
Papers
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Variance-Reduced Stochastic Learning under Random Reshuffling
TL;DR: In this article, the authors provided the first theoretical guarantee of linear convergence under random reshuffling for SAGA and proposed a new amortized variance-reduced gradient (AVRG) algorithm with constant storage requirements and balanced gradient computations compared to SVRG.
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Digital adaptive phase noise reduction in coherent optical links
TL;DR: In this article, an adaptive filtering scheme was proposed to reduce the effect of the laser phase noise and relaxes the laser linewidth requirement by utilizing the continuing scale down in size and power in VLSI technology.
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Parameter estimation with multiple sources and levels of uncertainties
TL;DR: A new design criterion is formulated that treats multiple sources of uncertainties in the data with possibly varied degrees of intensity and shows that the solution has a regularized form, with one regularization parameter for each source of uncertainty.
Proceedings ArticleDOI
Diffusion LMS-based distributed detection over adaptive networks
TL;DR: This work considers the problem of distributed detection, where a set of nodes are required to decide between two hypotheses based on their measurements, and proposes a distributed detection scheme based on diffusion least-squares techniques.
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Innovations Diffusion: A Spatial Sampling Scheme for Distributed Estimation and Detection
TL;DR: A distributed sampling scheme based on the concept of innovations diffusion to select the sensor nodes in a wireless network with distributed processing capabilities for estimation or detection applications is presented.