The aim of this conference is to bring together researchers and practitioners in the interdisciplinary field of biodevices, which spans across electronics, medicine, engineering, material sciences, and related areas.  The conference is a continuation of a series that started this year with the KAUST Research Conference on New Trends in Biosensors and Bioelectronics.

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.

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Abstract

Compliant electronics are an emerging class of electronics which offer physical flexibility in their structure.

The Wroław Research Center EIT+ was established in 2007 and renamed in 2018 into Polish Center for technology Development. This year PORT became an institute of the Łukasiewicz Network having similar goals like the Fraunhofer Society in Germany as an applied center for R&D. The presentation will concentrate on my research projects on deep-UV emitters. Going from UV-B (320-280nm) to UV-C (280-200nm) the external quantum efficiency decreases drastically and no breakthrough was obtained here based on high-Al content AlGaN. New, unconventional material combinations are needed to overcome these problems. One idea here is to add a small amount of arsenic to (Al,Ga)N to shift the valence band upwards and to increase the hole concentrations which is crucial for increasing the efficiencies of UV-C emitters. Results will be presented confirming this approach. Another idea is connected with strain engineering by using w-BN. This will allow to shift the unwanted TE to TM transition towards larges bandgaps (higher Al-contents).

We invite all members of the community to visit the first ever E-poster competition at KAUST for the SSI. The Saudi Summer Internship (SSI) is currently in it’s 7th year. This year CEMSE welcomes over 40 undergraduate students from Saudi Arabia to KAUST. With 25 interns being female, and 12 universities represented, 2019 is the largest and most diverse cohort of the SSI that the division has welcomed since it’s inception. All students have an excellent academic record in their home institution in areas related to Computer Science, Electrical Engineering and Mathematics. The students undertake a 12 week research program, supplemented with graduate skills and academic courses and are mentored by faculty and researchers in the CEMSE Division.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Ultraviolet (UV) group III-Nitride-based light emitters have been used in various applications such as water purification, medicine, lighting and chemical detection. Despite attractive properties such as bandgap tunability in the whole UV range (UV-C to UV-A), high chemical stability and relative low cost, the low quantum efficiency hamper the full utilization. This thesis aims to show alternative solutions to such problems by employing nanowires (NWs) structures, and target the eventual application of reliable and high power NWs-based light-emitting devices, enabling large-scale production using the established silicon foundry processes. Here, we present the improvement of injection current and optical power of AlGaN NWs LEDs by involving a metal bilayer thin film with a dual purpose: eliminate the potential barrier for carrier transport, and inhibit the formation of silicide.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

International Students in CEMSE who attended Photonics Summer Camp 2019, will present their research findings and experience of KAUST, in 10 minute presentations. Breakfast will be served. All KAUST affiliates are welcome to attend. The Photonics Summer Camp is an international internship program, currently in it’s 4th year. The program is just 4 weeks long and is designed to welcome overseas and local students to KAUST, in order to facilitate research innovation and collaboration amongst the next generation of photonics researchers. The program is sponsored by the CEMSE Division, with support from Graduate Affairs, and the International Programs Office.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step.

Abstract

Transcriptomics is the large-scale study of RNA molecules produc

Current computing infrastructures are evolving from traditional systems to environments that involve sensors, edge devices, instruments, and high-end  computing power in the cloud and HPC systems. A key aspect is how to describe the applications to be executed in such platforms. Very often these applications are not standalone, but involve a set of sub-applications or steps composing a workflow. The scientists then rely on effective environments to describe their workflows and engines to manage them in complex infrastructures. Refreshments - Brown Bag Lunch will be served.

Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

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.

It is an accepted fact that science, technology, and innovation are central to addressing global challenges facing humanity, such as climate change, energy security, food security, diseases, clean water, population control, conflicts and poverty. Therefore, nations are adopting policies and strategies for advancing science, technology and innovation as tools for sustainable economic growth, poverty alleviation and environmental protection. In this talk, I will discuss the role of TWAS working with developing countries in building their science capacity, with special attention to the 47 Least Developed Countries and Africa. TWAS initiatives such as South-South fellowships, research grants, exchange programs and science diplomacy will be discussed.

This series of lectures guide students to the preparation and analysis of a well-organized abstract. We will discuss the proper language (tense, voice, and person) for abstract writing, and learn how to meet the purposes of different abstracts. Finally, students will have a chance to compose and evaluate their writing. Topics: Overview of abstract writing; Conference abstract journal abstract; Organization of an abstract; Language conventions of abstract writing; Disciplinary abstract analysis; Frequent mistakes of abstract writing.

Abstract

In the last decade, with the emergence of the internet of things (IoT) as well as machine-to-machine (M2M) paradigms,

The objective of benchmark learning is to use a training sample to learn about fundamental limits on performance of a classifier or other statistical inference procedure. This meta-learning problem is a crucial component of data science and interpretable AI. Examples include sequential design of experiments, reinforcement learning and sensor management in the fields of statistics, machine learning and systems engineering, respectively. The challenge is learn about best achievable accuracy directly from the data sample without having to approximate and mplement an optimal classifier algorithm. In this talk we will introduce a general information theoretic framework that yields benchmark learners having both linear computational complexity and linear sample complexity. We will illustrate how this framework in the context of benchmarking image classification, autonomous navigation, and deep neural network performance.

Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.

Massive multiple-input multiple-output (MIMO) is a key enabling technology to achieve the required spectral and energy efficiency of the next generation of wireless networks. By endowing the base station (BS) with hundreds of antennas and relying on spatial multiplexing, massive MIMO allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate massive MIMO in different contexts.

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Abstract

We are stumbling across a video tsunami flooding our communication channels.