In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU–GPU based and CPU based.

The Field Effect Transistor (FET) is the main device for the integrated circuits era. This presentation starts with an overview of the main progress steps of FET evolution and finish with a discussion of possible FET devices for future technologies. New type of device conduction mechanism like Tunnel-FET devices (TFETs) have been studied to replace the conventional drift-diffusion conduction mechanisms due to the benefits obtained by tunneling conduction. The analog behavior of the TFET device will be presented. Nanowire devices and the first basics circuits with Tunnel-FET will be discussed and compared with FinFET ones.

Although there is a rich history of cross-layer design for embedded computing systems to achieve desired QoS, we are facing ever more challenges from the intertwined goals of energy- efficiency, thermal design constraints, as well as resilience to errors emanating from the application, environment and hardware platforms. We posit that next-generation computing platforms must necessarily deploy intelligent cross-layer design achieved through self-awareness principles inspired by biology and nature.  Such an approach will move us from current strategies (using limited cross-layer coordination) to a holistic cross-layer strategy that enables intelligent cross-layer management policies which can adaptively tune itself based on the current state of the system. The talk will present design exemplars that embrace this intelligent cross-layer approach, and highlight the role of self-awareness in achieving dynamic adaptivity.

This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.

As the title suggests, this interaction focuses on three different set-ups of free-space optical (FSO) communication. We will start with the historical background of FSO system and review traditional data transmission techniques for FSO system. In the first set-up, we will discuss a novel modulation scheme for traditional multi-input multi-output configuration, called optical space shift keying (OSSK). Two recent modified versions of OSSK scheme will also be elaborated.

The talk will discuss how recent advances in wireless computing and communication nodes can be harnessed to serve the multitude of deployment scenarios required to empower communities of the future with smart and connected systems. In this talk, we address fundamental questions that should be asked when contemplating future smart and connected systems, namely, How, Where and What? (1) How can we design computing and communication nodes that best utilize resources in a way that is cognizant of both the abilities of the platform, as well as the requirements of the network? (2) Where are the nodes deployed? By understanding the context of deployment, one can architect unique solutions that are currently unimaginable. With the transformation to diverse applications such as body area networking, critical infrastructure monitoring, precision agriculture, autonomous driving, etc., the need for innovative solutions becomes even more amplified. (3) What benefit can be inferred from the data gathered by nodes in the capacity of computing, communication, and sensing?

This seminar will review the development of electromagnetic surfaces, as well as state-of-the-art concepts and designs. Detailed presentations will be provided on their unique electromagnetic features. Furthermore, a wealth of practical examples will be presented to illustrate promising applications of the surface electromagnetics in microwaves and optics.

Abstract

The analysis of solar irradiance ha

Recently, surface plasmon resonance (SPR) effect has been widely applied in wide-band-gap semiconductor materials (e.g. GaN, ZnO or TiN etc.) for emission enhancement, absorption regulation, sensitive bio- or chemical detections and so on. In this talk, different metal (Ag or Al) nanostructures were introduced into and successfully fabricated on several kinds of wide-band-gap semiconductor materials, including ZnO, AlGaN, TiN, and h-BN by template-based nanosphere lithography method and deposition techniques.

Abstract

There are several Bayesian models where the posterior density

In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (IEEE S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of perturbation distances between classification labels to detect when a backdoor trigger has created shortcuts to misclassification to a particular label.  Second, I will also summarize our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attack that is more difficult to detect and survives retraining in commonly used transfer learning systems. Latent backdoors are robust and stealthy, even against the latest detection tools (including neural cleanse).

Abstract

5G networks are currently facing the first installation steps

The European Telecom Standards Institute (ETSI) introduced the concept of Network Function Virtualization (NFV) with the aim of efficient network architecture and network system operation. In traditional networks, network functions are implemented in dedicated physical machines which are designed for single functionalities. Network services have been provided by connecting these physical machines, so the network architecture has been highly rigid and hard to change. NFV environment provides a more flexible and scalable network configuration and implementation through the softwarization of physical network functions. Network functions are transformed to Virtual Machines (VMs) so that Virtualized Network Functions (VNFs) can be implemented in commodity servers built for common uses, including public clouds.

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

2019 Statistics and Data Science Workshop confirmed speakers include Prof. Alexander Aue, University of California Davis, USA, Prof. Francois Bachoc, University Toulouse 3, France, Prof. Rosa M. Crujeiras Casais, University of Santiago de Compostela, Spain, Prof. Emanuele Giorgi, Lancaster University, UK, Prof. Jeremy Heng, ESSEC Asia-Pacific, Singapore, Prof. Birgir Hrafnkelsson, University of Iceland, Iceland, Prof. Ajay Jasra, KAUST, Saudi Arabia, Prof. Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project, Japan, Prof. Robert Krafty, University of Pittsburgh, USA, Prof. Guido Kuersteiner, University of Maryland, USA, Prof. Paula Moraga, University of Bath, UK, Prof. Tadeusz Patzek, KAUST, Saudi Arabia, Prof. Brian Reich, North Carolina State University, USA, Prof. Dag Tjostheim, University Bergen, Norway, Prof. Xiangliang Zhang, KAUST, Saudi Arabia, Sylvia Rose Esterby, University of British Colombia, Canada, Prof. Abdel El-Shaarawi, Retired Professor at the National Water Research Institute, Canada. View Workshop schedule and abstracts here.

This talk will introduce current challenges and methods in Biophotonics which relate to the research work carried out at the Vibrational Imaging Lab at KAUST.

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

Over the past 30 years, my research has focused on problems involving aerospace information systems, give or take a couple of exceptions - I also seem to like things with wheels-. In the talk, I will introduce and discuss a few research topics that, I believe, best describe the kind of research I like to perform. The topics will include things as diverse as drones, airports, and the hunt for Lyapunov functions. I will also introduce an educational initiative focusing on systematically restoring "the sense of touch" in all Engineering disciplines, and which I would like to experiment with as part of the courses I will be in charge of at KAUST.

Physically compliant electronics are scientifically intriguing, mechanically complex, ‎technologically ‎challenging but with huge socio-economical potential. Until now the target applications ‎for ‎flexible electronics have been limited to displays, solar cells, printed batteries, wearables, and ‎implantable. However, with the emergence and growth of Internet of Things (IoT) devices ‎worldwide ‎from nearly 27 billion in 2017 to 125 billion in 2030, in this PhD research, expanding the ‎horizon of ‎applications for flexible electronics toward existing “things” will be explored. Low-cost sustainable materials as active electronic materials and a ‎Do-It-Yourself (DIY) integration strategy is used to build “Add-on” standalone sensory system which can be ‎attached to any ‎existing things like a decal. Such electronics can also be embedded in newly minted ‎devices specially ‎using additive methods.

We consider the problem of recovering a low-rank signal matrix in the presence of a general, unknown additive noise; more specifically, noise where the eigenvalues of the sample covariance matrix have a general bulk distribution. We assume given an upper bound for the rank of the assumed orthogonally invariant signal, and develop a selector for hard thresholding of singular values, which adapts to the unknown correlation structure of the noise.

Power constraint has become a critical challenge for computing, restricting the rate at which data can be processed. The physics of ordered and correlated systems allow for fundamental improvement of the energy efficiency in this regard, going beyond what is possible with conventional materials in today’s computing hardware. One such example is the the ferroelectric materials, where thermodynamics dictate that charge can be switched with much lower energy compared to conventional dielectrics. This leads to a situation where a ferroelectric material can be stabilized at a state of negative capacitance. In this talk, I shall discuss our experimental work demonstrating the stabilization of negative capacitance, its integration into advanced transistors, and its potential impact on next generation computing hardware.

A variety of intriguing patterns in eigenvalues were observed and speculated about in ML conference papers. We describe the work of Vardan Papyan showing that the traditional subdisciplines, properly deployed, can offer insights about these objects that ML researchers had.