optimization

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Spectrum Management for Future Generations of Cellular Networks

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B1 L2 R2202

The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

Virtual sensing: methods and applications

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B9 L2 H1 R2322

Abstract Due to the inherent complexity, physical or economical constraints, a number of important physical and biological systems are inaccessible. Only few, often indirect, experimental data are available to extract information or to manipulate and control the system. Introduced as a promising technology to respond to the growing need for reliable, accurate, fast, robust and low-cost sensing strategies, virtual sensors are gaining importance in several fields. These sensors combine the available data with effective estimation methods and algorithms to access non-measurable variables of

Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

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KAUST

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Stochastic Optimization in Target Positioning and Location-based Applications

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KAUST

This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

Adel is invited to give a talk in EECVC on "Optimization meets Deep Learning"

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​Adel is invited to give a talk in EECVC on "Optimization meets Deep Learning"​. It is a 45 minutes talk scheduled on the 6th of July 2019.

An Airline observatory created by Passengers for Passengers

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The COVID-19 pandemic has generated a significant worldwide disruption to flight operations, affecting the lives of millions of passengers.

Qizhou Wang

Qizhou Wang is an M.S./Ph.D. candidate in the KAUST Primalight research group under the supervision of Professor Andrea Fratalocchi. Before joining KAUST, Qizhou obtained a bachelor's degree in communication engineering from the University of Electronic Science and Technology, China. Research Interests Qizhou's research interests include machine learning, optimization theories and computer vision.

Qilin Sun

Qilin Sun is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Qilin Sun obtained his bachelor degree in Optical Information Science and Technology from Huazhong University of Science and Technology in Wuhan, China in 2015. After that, he joined KAUST and received his master degree in Electrical Engineering in 2017.Qilin is a CEO&Founder at Point Spread Technology, China. Research Interest Qilin is interested in Computational

Krishnendu S Tharakan

Visiting Student (former), Electrical and Computer Engineering
Visiting student at King Abdullah University of Science and Technology (KAUST).

Itsikiantsoa Randrianantenaina

Itsikiantsoa Randrianantenaina received the Engineering Diploma degree from the Institut National des Postes et Telecommunications, Rabat, Morocco, in 2013, and the M.S. degree in electrical engineering from the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, from June 2015- June 2019, where she earned the Ph.D. degree and Joined NEOM, Saudi Arabia, as a Research Engineer after graduation. Her research interests include full-duplex and NOMA systems, interference management, FogRAN architecture, stochastic geometry, and optimization. Publications dblp Scopus

Ganesh Sundaramoorthi

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications. Education and Early Career His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that

Eric Feron

With 31 years of teaching and research experience, Professor Feron’s work utilizes elementary concepts of control systems, optimization and computer science to address critical issues in modern aerospace engineering and robotics.

Ebrahim Safadi

Internship at King Abdullah University of Science and Technology (KAUST).

Ahmed Douik

Visiting B.S. Student, Computer, Electrical and Mathematical Sciences and Engineering , Quantitative Researcher at Chicago Trading Company PhD candidate at California Institute of Technology

Adel Bibi

Adel Bibi is a postdoctoral research assistant in computer vision and machine learning at the Torr Vision Group working with Professor Philip Torr at the University of Oxford. Prior to that, he received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) where he was part of the Image and Video Understanding Lab (IVUL) advised by Professor Bernard Ghanem. Education and Early Career Adel Bibi has a Bachelor’s Degree in Electrical Engineering from Kuwait University in 2014. He received his Master Degree in Electrical Engineering from King Abdullah University of

Abderrazak Chahid

Abderrazak Chahid is a PhD candidate in Electrical and Computer Engineering department within CEMSE division. He obtained his Bachelor of Science degree in Electrical Engineering and Power Electronics from Sultan Moulay Slimane University in Morocco. He then got a Master degree in Electrical Engineering from the National School of Applied Sciences in Morocco before obtaining a second Master of Science degree in Embedded Systems from Lorraine University in France. Abderrazak is currently working on developing biomedical signal/image processing: MRS water suppression, MRI image denoising. He is